diff -Nru r-cran-epir-0.9-32/DESCRIPTION r-cran-epir-0.9-38/DESCRIPTION --- r-cran-epir-0.9-32/DESCRIPTION 2011-05-10 04:21:26.000000000 +0000 +++ r-cran-epir-0.9-38/DESCRIPTION 2012-02-02 07:41:00.000000000 +0000 @@ -1,21 +1,22 @@ Package: epiR -Version: 0.9-32 -Date: 2011-05-10 -Title: Functions for analysing epidemiological data +Version: 0.9-38 +Date: 2012-02-02 +Title: An R package for the analysis of epidemiological data Author: Mark Stevenson with contributions - from Telmo Nunes, Javier Sanchez, and Ron Thornton. + from Telmo Nunes, Javier Sanchez, Ron Thornton, Jeno Reiczigel, + Jim Robison-Cox and Paola Sebastiani Maintainer: Mark Stevenson -Description: A package for analysing epidemiological data. Contains - functions for directly and indirectly adjusting measures of - disease frequency, quantifying measures of association on the - basis of single or multiple strata of count data presented in a - contingency table, and computing confidence intervals around - incidence risk and incidence rate estimates. Miscellaneous - functions for use in meta-analysis, diagnostic test - interpretation, and sample size calculations. -Depends: R (>= 2.0.0) +Description: An R package for the analysis of epidemiological data. + Contains functions for directly and indirectly adjusting + measures of disease frequency, quantifying measures of + association on the basis of single or multiple strata of count + data presented in a contingency table, and computing confidence + intervals around incidence risk and incidence rate estimates. + Miscellaneous functions for use in meta-analysis, diagnostic + test interpretation, and sample size calculations. +Depends: R (>= 2.14.0), survival License: GPL (>= 2) URL: http://epicentre.massey.ac.nz -Packaged: 2011-05-10 03:39:51 UTC; mstevens +Packaged: 2012-02-01 20:46:17 UTC; mstevens Repository: CRAN -Date/Publication: 2011-05-10 04:21:26 +Date/Publication: 2012-02-02 07:41:00 diff -Nru r-cran-epir-0.9-32/MD5 r-cran-epir-0.9-38/MD5 --- r-cran-epir-0.9-32/MD5 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r-cran-epir-0.9-38/NAMESPACE 2012-02-01 20:46:18.000000000 +0000 @@ -0,0 +1,10 @@ +# Default NAMESPACE created by R +# Remove the previous line if you edit this file + +# Export all names +exportPattern(".") + +# Import all packages listed as Imports or Depends +import( + survival +) diff -Nru r-cran-epir-0.9-32/R/epi.2by2.R r-cran-epir-0.9-38/R/epi.2by2.R --- r-cran-epir-0.9-32/R/epi.2by2.R 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/R/epi.2by2.R 1970-01-01 00:00:00.000000000 +0000 @@ -1,1197 +0,0 @@ -"epi.2by2" <- function(dat, method = "cohort.count", conf.level = 0.95, units = 100, verbose = FALSE){ - # Elwoood JM (1992). Causal Relationships in Medicine - A Practical System for Critical Appraisal. Oxford Medical Publications, London, p 266 - 293. - # Rothman KJ (2002). Epidemiology An Introduction. Oxford University Press, London, p 130 - 143. - # Hanley JA (2001). A heuristic approach to the formulas for population attributable fraction. J. Epidemiol. Community Health 55:508 - 514. - # Jewell NP (2004). Statistics for Epidemiology. Chapman & Hall/CRC, New York, p 84 - 85. - - # Incidence risk in exposed IRiske - # Incidence risk in unexposed IRisko - # Incidence risk in population IRpop - - # Incidence rate in exposed IRatee - # Incidence rate in unexposed IRateo - # Incidence rate in population IRatepop - - # Odds in exposed Oe - # Odds in unexposed Oo - # Odds in population Opop - - # Incidence risk ratio RR.p - # Incidence rate ratio IRR.p - # Odds ratio OR.p - # Corrected incidence risk ratio cRR.p - - # Attributable risk ARisk.p - # Attributable rate ARate.p - - # Attributable fraction risk data AFRisk.p - # Attributable fraction rate data AFRate.p - # Estimated attributable fraction AFest.p - - # Population attributable risk PARisk.p - # Population attributable rate PARate.p - - # Population attributable fraction risk data PAFRisk.p - # Population attributable fraction rate data PAFRate.p - - # Crude incidence risk ratio (strata): cRR.p - # Crude incidence rate ratio (strata): cIRR.p - # Crude incidence odds ratio (strata): cOR.p - # Crude attributable risk (strata): cARisk.p - # Crude attributable rate (strata): cARate.p - - # Summary incidence risk ratio: sRR.p - # Summary incidence rate ratio: sIRR.p - # Summary incidence odds ratio: sOR.p - # Summary attributable risk sARisk.p - # Summary attributable rate sARate.p - - # Reporting - method == cohort.count: - # Inc risk ratio; odds ratio - # Attributable risk; attributable risk in population - # Attributable fraction in exposed; attributable fraction in population - - # Reporting - method == cohort.time: - # Inc rate ratio - # Attributable rate; attributable rate in population - # Attributable fraction in exposed; attributable fraction in population - - # Reporting - method == case.control: - # Odds ratio - # Attributable prevalence; attributable prevalence in population - # Attributable fraction (est) in exposed; attributable fraction (est) in population - - # Reporting - method == cross.sectional: - # Prevalence ratio; odds ratio - # Attributable prevalence; attributable prevalence in population - # Attributable fraction in exposed; attributable fraction in population - - # Make a copy of the original data. These values used when sums of cells across all strata are greater than zero but - # some strata contain zero cell frequencies. - - if(length(dim(dat)) == 2){ - a <- dat[1]; A <- a - b <- dat[3]; B <- b - c <- dat[2]; C <- c - d <- dat[4]; D <- d - } - - if(length(dim(dat)) > 2){ - a <- dat[1,1,]; A <- a - b <- dat[1,2,]; B <- b - c <- dat[2,1,]; C <- c - d <- dat[2,2,]; D <- d - } - - # Test each strata for zero values. Add 0.5 to all cells if any cell has a zero value: - for(i in 1:length(a)){ - if(a[i] < 1 | b[i] < 1 | c[i] < 1 | d[i] < 1){ - a[i] <- a[i] + 0.5; b[i] <- b[i] + 0.5; c[i] <- c[i] + 0.5; d[i] <- d[i] + 0.5 - } - } - -.funincrisk <- function(dat, conf.level){ - # Exact binomial confidence limits from D. Collett (1999) Modelling binary data. Chapman & Hall/CRC, Boca Raton Florida, p. 24. - N. <- 1 - ((1 - conf.level) / 2) - a <- dat[,1] - n <- dat[,2] - b <- n - a - p <- a / n - - # Wilson's method (see Rothman, Epidemiology An Introduction, page 132): - # N. <- 1 - ((1 - conf.level) / 2) - # z <- qnorm(N., mean = 0, sd = 1) - # a <- dat[,1] - # n <- dat[,2] - # p <- dat[,1] / dat[,2] - - # a. <- n/(n + z^2) - # b. <- a/n - # c. <- z^2/(2 * n) - # d. <- (a * (n - a)) / n^3 - # e. <- z^2 / (4 * n^2) - # low <- a. * (b. + c. - (z * sqrt(d. + e.))) - # up <- a. * (b. + c. + (z * sqrt(d. + e.))) - - a. <- ifelse(a == 0, a + 1, a); b. <- ifelse(b == 0, b + 1, b) - low <- a. /(a. + (b. + 1) * (1 / qf(1 - N., 2 * a., 2 * b. + 2))) - up <- (a. + 1) / (a. + 1 + b. / (1 / qf(1 - N., 2 * b., 2 * a. + 2))) - low <- ifelse(a == 0, 0, low) - up <- ifelse(a == n, 1, up) - rval <- as.data.frame(cbind(p, low, up)) - names(rval) <- c("est", "lower", "upper") - rval - } - -.funincrate <- function(dat, conf.level){ - N. <- 1 - ((1 - conf.level) / 2) - a <- dat[,1] - n <- dat[,2] - p <- a / n - low <- 0.5 * qchisq(p = N., df = 2 * a + 2, lower.tail = FALSE) / n - up <- 0.5 * qchisq(p = 1 - N., df = 2 * a + 2, lower.tail = FALSE) / n - # a.prime <- dat[,1] + 0.5 - # p <- dat[,1]/dat[,2] - # PT <- dat[,2] - # low <- (a.prime * (1 - (1/(9 * a.prime)) - (z/3 * sqrt(1/a.prime)))^3)/PT - # up <- (a.prime * (1 - (1/(9 * a.prime)) + (z/3 * sqrt(1/a.prime)))^3)/PT - - # Wilson's method (see Rothman, Epidemiology An Introduction, page 132): - # N. <- 1 - ((1 - conf.level) / 2) - # z <- qnorm(N., mean = 0, sd = 1) - # a <- dat[,1] - # n <- dat[,2] - # p <- dat[,1] / dat[,2] - # a. <- n/(n + z^2) - # b. <- a/n - # c. <- z^2/(2 * n) - # d. <- (a * (n - a)) / n^3 - # e. <- z^2 / (4 * n^2) - # low <- a. * (b. + c. - (z * sqrt(d. + e.))) - # up <- a. * (b. + c. + (z * sqrt(d. + e.))) - - rval <- as.data.frame(cbind(p, low, up)) - names(rval) <- c("est", "lower", "upper") - rval - } - - # ================= - # DECLARE VARIABLES - # ================= - - # | D+ | D- | Total - # ---------------------------- - # Exp + | a | b | N1 - # Exp - | c | d | N0 - # -------|------|------|------ - # Total | M1 | M0 | Total - - - N. <- 1 - ((1 - conf.level) / 2) - z <- qnorm(N., mean = 0, sd = 1) - lower <- "lower" - upper <- "upper" - - # For large numbers you need to use floating point rather than integer representation. This will avoid "integer overflow" messages: - a <- as.numeric(a); A <- as.numeric(A) - b <- as.numeric(b); B <- as.numeric(B) - c <- as.numeric(c); C <- as.numeric(C) - d <- as.numeric(d); D <- as.numeric(D) - - # Total within strata cases: - M1 <- a + c - # Total within strata non-cases: - M0 <- b + d - # Total within strata exposed: - N1 <- a + b - # Total within strata unexposed: - N0 <- c + d - # Total within strata subjects: - total <- a + b + c + d - # Number of strata: - n.strata <- length(a) - - # Added 190809: - # If the sums across strata for all cells are greater than 0, use the sums of the crude data (cf the sums of the adjusted values): - if(sum(A) > 0 & sum(B) > 0 & sum(C) > 0 & sum(D) > 0){ - sa <- sum(A); sb <- sum(B); sc <- sum(C); sd <- sum(D) - } - - # If the sums across strata for all cells contain a 0, use the sums of the adjusted data: - if(sum(A) == 0 | sum(B) == 0 | sum(C) == 0 | sum(D) == 0){ - sa <- sum(a); sb <- sum(b); sc <- sum(c); sd <- sum(d) - } - - # sa <- sum(a); sb <- sum(b); sc <- sum(c); sd <- sum(d) - - # Grand total cases: - sM1 <- sa + sc - # Grand total non-cases: - sM0 <- sb + sd - # Grand total exposed: - sN1 <- sa + sb - # Grand total unexposed: - sN0 <- sc + sd - # Grand total: - stotal <- sa + sb + sc + sd - - # Within-strata incidence risk in exposed: - tmp <- .funincrisk(as.matrix(cbind(a, N1)), conf.level = conf.level) - IRiske.p <- as.numeric(tmp[,1]) * units - IRiske.l <- as.numeric(tmp[,2]) * units - IRiske.u <- as.numeric(tmp[,3]) * units - - # Within-strata incidence risk in unexposed: - tmp <- .funincrisk(as.matrix(cbind(c, N0)), conf.level = conf.level) - IRisko.p <- as.numeric(tmp[,1]) * units - IRisko.l <- as.numeric(tmp[,2]) * units - IRisko.u <- as.numeric(tmp[,3]) * units - - # Within-strata incidence risk in population: - tmp <- .funincrisk(as.matrix(cbind(M1, total)), conf.level = conf.level) - IRiskpop.p <- as.numeric(tmp[,1]) * units - IRiskpop.l <- as.numeric(tmp[,2]) * units - IRiskpop.u <- as.numeric(tmp[,3]) * units - - # Within-strata incidence rate in exposed: - tmp <- .funincrate(as.matrix(cbind(a, b)), conf.level = conf.level) - IRatee.p <- as.numeric(tmp[,1]) * units - IRatee.l <- as.numeric(tmp[,2]) * units - IRatee.u <- as.numeric(tmp[,3]) * units - - # Within-strata incidence rate in unexposed: - tmp <- .funincrate(as.matrix(cbind(c, d)), conf.level = conf.level) - IRateo.p <- as.numeric(tmp[,1]) * units - IRateo.l <- as.numeric(tmp[,2]) * units - IRateo.u <- as.numeric(tmp[,3]) * units - - # Within-strata incidence rate in population: - tmp <- .funincrate(as.matrix(cbind(M1, M0)), conf.level = conf.level) - IRatepop.p <- as.numeric(tmp[,1]) * units - IRatepop.l <- as.numeric(tmp[,2]) * units - IRatepop.u <- as.numeric(tmp[,3]) * units - - # Within-strata odds in exposed (based on Ederer F and Mantel N (1974) Confidence limits on the ratio of two Poisson variables. - # American Journal of Epidemiology 100: 165 - 167. - # Cited in Altman, Machin, Bryant, and Gardner (2000) Statistics with Confidence, British Medical Journal, page 69). - # Added 160609. - Al <- (qbinom(1 - N., size = a + b, prob = (a / (a + b)))) / (a + b) - Au <- (qbinom(N., size = a + b, prob = (a / (a + b)))) / (a + b) - Oe.p <- (a / b) - Oe.l <- (Al / (1 - Al)) - Oe.u <- (Au / (1 - Au)) - - # Within-strata odds in unexposed: - Al <- (qbinom(1 - N., size = c + d, prob = (c / (c + d)))) / (c + d) - Au <- (qbinom(N., size = c + d, prob = (c / (c + d)))) / (c + d) - Oo.p <- (c / d) - Oo.l <- (Al / (1 - Al)) - Oo.u <- (Au / (1 - Au)) - - # Within-strata odds in population: - Al <- (qbinom(1 - N., size = M1 + M0, prob = (M1 / (M1 + M0)))) / (M1 + M0) - Au <- (qbinom(N., size = M1 + M0, prob = (M1 / (M1 + M0)))) / (M1 + M0) - Opop.p <- (M1 / M0) - Opop.l <- (Al / (1 - Al)) - Opop.u <- (Au / (1 - Au)) - - # Crude incidence risk in exposed: - tmp <- .funincrisk(as.matrix(cbind(sa, sN1)), conf.level = conf.level) - cIRiske.p <- as.numeric(tmp[,1]) * units - cIRiske.l <- as.numeric(tmp[,2]) * units - cIRiske.u <- as.numeric(tmp[,3]) * units - - # Crude incidence risk in unexposed: - tmp <- .funincrisk(as.matrix(cbind(sc, sN0)), conf.level = conf.level) - cIRisko.p <- as.numeric(tmp[,1]) * units - cIRisko.l <- as.numeric(tmp[,2]) * units - cIRisko.u <- as.numeric(tmp[,3]) * units - - # Crude incidence risk in population: - tmp <- .funincrisk(as.matrix(cbind(sM1, stotal)), conf.level = conf.level) - cIRiskpop.p <- as.numeric(tmp[,1]) * units - cIRiskpop.l <- as.numeric(tmp[,2]) * units - cIRiskpop.u <- as.numeric(tmp[,3]) * units - - # Crude incidence rate in exposed: - tmp <- .funincrate(as.matrix(cbind(sa, sb)), conf.level = conf.level) - cIRatee.p <- as.numeric(tmp[,1]) * units - cIRatee.l <- as.numeric(tmp[,2]) * units - cIRatee.u <- as.numeric(tmp[,3]) * units - - # Crude incidence rate in unexposed: - tmp <- .funincrate(as.matrix(cbind(sc, sd)), conf.level = conf.level) - cIRateo.p <- as.numeric(tmp[,1]) * units - cIRateo.l <- as.numeric(tmp[,2]) * units - cIRateo.u <- as.numeric(tmp[,3]) * units - - # Crude incidence risk in population: - tmp <- .funincrate(as.matrix(cbind(sM1, sM0)), conf.level = conf.level) - cIRatepop.p <- as.numeric(tmp[,1]) * units - cIRatepop.l <- as.numeric(tmp[,2]) * units - cIRatepop.u <- as.numeric(tmp[,3]) * units - - # Crude odds in exposed (based on Ederer F and Mantel N (1974) Confidence limits on the ratio of two Poisson variables. - # American Journal of Epidemiology 100: 165 - 167. - # Cited in Altman, Machin, Bryant, and Gardner (2000) Statistics with Confidence, British Medical Journal, page 69). - # Added 160609 - Al <- (qbinom(1 - N., size = sa + sb, prob = (sa / (sa + sb)))) / (sa + sb) - u <- (qbinom(N., size = sa + sb, prob = (sa / (sa + sb)))) / (sa + sb) - cOe.p <- sa / sb - cOe.l <- Al / (1 - Al) - cOe.u <- Au / (1 - Au) - - # Crude odds in unexposed: - Al <- (qbinom(1 - N., size = sc + sd, prob = (sc / (sc + sd)))) / (sc + sd) - u <- (qbinom(N., size = sc + sd, prob = (sc / (sc + sd)))) / (sc + sd) - cOo.p <- sc / sd - cOo.l <- Al / (1 - Al) - cOo.u <- Au / (1 - Au) - - # Crude odds in population: - Al <- (qbinom(1 - N., size = sM1 + sM0, prob = (sM1 / (sM1 + sM0)))) / (sM1 + sM0) - u <- (qbinom(N., size = sM1 + sM0, prob = (sM1 / (sM1 + sM0)))) / (sM1 + sM0) - cOpop.p <- sM1 / sM0 - cOpop.l <- Al / (1 - Al) - cOpop.u <- Au / (1 - Au) - - - # ========================================= - # INDIVIDUAL STRATA MEASURES OF ASSOCIATION - # ========================================= - - # Individual strata incidence risk ratio (Rothman p 135 equation 7-3): - RR.p <- (a / N1) / (c / N0) - lnRR <- log(RR.p) - lnRR.var <- (1 / a) - (1 / N1) + (1 / c) - (1 / N0) - lnRR.se <- sqrt((1 / a) - (1 / N1) + (1 / c) - (1 / N0)) - RR.se <- exp(lnRR.se) - RR.l <- exp(lnRR - (z * lnRR.se)) - RR.u <- exp(lnRR + (z * lnRR.se)) - # Incidence risk ratio weights (equal to precision, the inverse of the variance of the RR. See Woodward page 168): - RR.w <- 1 / (exp(lnRR.var)) - - # Individual strata incidence rate ratio (exact confidence intervals http://www.folkesundhed.au.dk/uddannelse/software): - IRR.p <- (a / b) / (c / d) - lnIRR <- log(IRR.p) - lnIRR.var <- (1 / a) + (1 / c) - lnIRR.se <- sqrt((1 / a) + (1 / c)) - IRR.se <- exp(lnIRR.se) - pl <- a / (a + (c + 1) * (1 / qf(1 - N., 2 * a, 2 * c + 2))) - ph <- (a + 1) / (a + 1 + c / (1 / qf(1 - N., 2 * c, 2 * a + 2))) - IRR.l <- pl * d / ((1 - pl) * b) - IRR.u <- ph * d / ((1 - ph) * b) - # lnIRR.l <- lnIRR - (z * lnIRR.se) - # lnIRR.u <- lnIRR + (z * lnIRR.se) - # IRR.l <- exp(lnIRR.l) - # IRR.u <- exp(lnIRR.u) - # Incidence rate ratio weights (equal to precision, the inverse of the variance of the IRR. See Woodward page 168): - IRR.w <- 1 / (exp(lnIRR.var)) - - # Individual strata odds ratios (Rothman p 139 equation 7-6): - OR.p <- (a * d) / (b * c) - lnOR <- log(OR.p) - lnOR.var <- 1/a + 1/b + 1/c + 1/d - lnOR.se <- sqrt(1/a + 1/b + 1/c + 1/d) - lnOR.l <- lnOR - (z * lnOR.se) - lnOR.u <- lnOR + (z * lnOR.se) - OR.se <- exp(lnOR.se) - OR.l <- exp(lnOR.l) - OR.u <- exp(lnOR.u) - # Odds ratio weights (equal to precision, the inverse of the variance of the OR. See Woodward page 168): - OR.w <- 1 / (exp(lnOR.var)) - - # Individual strata corrected incidence risk ratio (Zhang and Khai 1998): - cRR.p <- OR.p / ((1 - N0) + (N0 * OR.p)) - cRR.l <- OR.l / ((1 - N0) + (N0 * OR.l)) - cRR.u <- OR.u / ((1 - N0) + (N0 * OR.u)) - - # Individual strata attributable risk (Rothman p 135 equation 7-2): - ARisk.p <- ((a / N1) - (c / N0)) * units - # ARisk.var <- (((a * b) / (N1^2 * (N1 - 1))) + ((c * d) / (N0^2 * (N0 - 1)))) - ARisk.se <- (sqrt(((a * (N1 - a))/N1^3) + ((c * (N0 - c))/N0^3))) * units - ARisk.l <- (ARisk.p - (z * ARisk.se)) - ARisk.u <- (ARisk.p + (z * ARisk.se)) - # Attribtable risk weights (equal to precision, the inverse of the variance of the RR. See Woodward page 168): - ARisk.w <- 1 / (ARisk.se / units)^2 - - # Individual strata attributable rate (Rothman p 137 equation 7-4): - ARate.p <- ((a / b) - (c / d)) * units - ARate.var <- (a / b^2) + (c / d^2) - ARate.se <- (sqrt((a / b^2) + (c / d^2))) * units - ARate.l <- ARate.p - (z * ARate.se) - ARate.u <- ARate.p + (z * ARate.se) - # Attribtable rate weights (equal to precision, the inverse of the variance of the RR. See Woodward page 168): - ARate.w <- 1 / (ARate.var) - - # Individual strata attributable fraction for risk data (from Hanley 2001): - AFRisk.p <- ((RR.p - 1) / RR.p) - AFRisk.l <- min((RR.l - 1) / RR.l, (RR.u - 1) / RR.u) - AFRisk.u <- max((RR.l - 1) / RR.l, (RR.u - 1) / RR.u) - - # Individual strata attributable fraction for rate data (from Hanley 2001): - AFRate.p <- (IRR.p - 1) / IRR.p - AFRate.l <- min((IRR.l - 1) / IRR.l, (IRR.u - 1) / IRR.u) - AFRate.u <- max((IRR.l - 1) / IRR.l, (IRR.u - 1) / IRR.u) - - # Individual strata estimated attributable fraction (from Hanley 2001): - AFest.p <- (OR.p - 1) / OR.p - AFest.l <- min((OR.l - 1) / OR.l, (OR.u - 1) / OR.u) - AFest.u <- max((OR.l - 1) / OR.l, (OR.u - 1) / OR.u) - - # Individual strata population attributable risk (same as Rothman p 135 equation 7-2): - PARisk.p <- ((M1 / total) - (c / N0)) * units - PARisk.se <- (sqrt(((M1 * (total - M1))/total^3) + ((c * (N0 - c))/N0^3))) * units - PARisk.l <- PARisk.p - (z * PARisk.se) - PARisk.u <- PARisk.p + (z * PARisk.se) - - # Individual strata population attributable rate (same as Rothman p 137 equation 7-4): - PARate.p <- ((M1 / M0) - (c / d)) * units - PARate.se <- (sqrt((M1 / M0^2) + (c / d^2))) * units - PARate.l <- PARate.p - (z * PARate.se) - PARate.u <- PARate.p + (z * PARate.se) - # Individual strata population attributable fractions for risk data (from Hanley, 2001): - # PAFRisk.p <- ((RR.p - 1) / RR.p) * (a / M1) - # PAFRisk.l <- ((RR.l - 1) / RR.l) * (a / M1) - # PAFRisk.u <- ((RR.u - 1) / RR.u) * (a / M1) - # Individual strata population attributable fractions for risk data (from OpenEpi TwobyTwo): - # PAFRisk.p <- (IRiskpop.p - IRisko.p) / IRiskpop.p - # PAFRisk.l <- min((IRiskpop.l - IRisko.l) / IRiskpop.l, (IRiskpop.u - IRisko.u) / IRiskpop.u) - # PAFRisk.u <- max((IRiskpop.l - IRisko.l) / IRiskpop.l, (IRiskpop.u - IRisko.u) / IRiskpop.u) - - # Individual strata population attributable fractions for risk data (from Jewell, page 84): - PAFRisk.p <- ((a * d) - (b * c)) / ((a + c) * (c + d)) - PAFRisk.var <- (b + (PAFRisk.p * (a + d))) / (total * c) - PAFRisk.l <- 1 - exp(log(1 - PAFRisk.p) + (z * sqrt(PAFRisk.var))) - PAFRisk.u <- 1 - exp(log(1 - PAFRisk.p) - (z * sqrt(PAFRisk.var))) - - # Individual strata population attributable fractions for rate data (from Hanley, 2001): - # PAFRate.p <- ((IRR.p - 1) / IRR.p) * (a / M1) - # PAFRate.l <- ((IRR.l - 1) / IRR.l) * (a / M1) - # PAFRate.u <- ((IRR.u - 1) / IRR.u) * (a / M1) - - # Individual strata population attributable fractions for rate data (from OpenEpi TwobyTwo - Jewell doesn't provide a method for rate data): - PAFRate.p <- (IRatepop.p - IRateo.p) / IRatepop.p - PAFRate.l <- min((IRatepop.l - IRateo.l) / IRatepop.l, (IRatepop.u - IRateo.u) / IRatepop.u) - PAFRate.u <- max((IRatepop.l - IRateo.l) / IRatepop.l, (IRatepop.u - IRateo.u) / IRatepop.u) - - # Individual strata estimated population attributable fraction (from Hanley, 2001): - # PAFest.p <- ((OR.p - 1) / OR.p) * (a / M1) - # PAFest.l <- ((OR.l - 1) / OR.l) * (a / M1) - # PAFest.u <- ((OR.u - 1) / OR.u) * (a / M1) - - # Individual strata estimated population attributable fraction (from OpenEpi TwobyTwo): - # PAFest.p <- (Opop.p - Oo.p) / Opop.p - # PAFest.l <- min((Opop.l - Oo.l) / Opop.l, (Opop.u - Oo.u) / Opop.u) - # PAFest.u <- max((Opop.l - Oo.l) / Opop.l, (Opop.u - Oo.u) / Opop.u) - - # Individual strata population attributable fractions for risk data (from Jewell, page 84): - PAFest.p <- ((a * d) - (b * c)) / (d * (a + c)) - PAFest.var <- (a / (c * (a + c))) + (b / (d * (b + d))) - PAFest.l <- 1 - exp(log(1 - PAFest.p) + (z * sqrt(PAFest.var))) - PAFest.u <- 1 - exp(log(1 - PAFest.p) - (z * sqrt(PAFest.var))) - - # ============================= - # CRUDE MEASURES OF ASSOCIATION - # ============================= - - # Crude incidence risk ratio (Rothman p 135 equation 7-3): - cRR.p <- (sa / sN1) / (sc / sN0) - clnRR <- log(cRR.p) - clnRR.var <- (1 / sa) - (1 / sN1) + (1 / sc) - (1 / sN0) - # This line incorrect. Fixed 191208: - # clnRR.se <- sqrt((1 / sa) - (1 / sN1) + (1 / sb) - (1 / sN0)) - clnRR.se <- sqrt((1 / sa) - (1 / sN1) + (1 / sc) - (1 / sN0)) - clnRR.l <- clnRR - (z * clnRR.se) - clnRR.u <- clnRR + (z * clnRR.se) - cRR.se <- exp(clnRR.se) - cRR.l <- exp(clnRR.l) - cRR.u <- exp(clnRR.u) - - # Crude incidence rate ratio (exact confidence intervals http://www.folkesundhed.au.dk/uddannelse/software): - cIRR.p <- (sa / sb) / (sc / sd) - clnIRR <- log(cIRR.p) - clnIRR.se <- sqrt((1 / sa) + (1 / sc)) - cIRR.se <- exp(clnIRR.se) - pl <- sa / (sa + (sc + 1) * (1 / qf(1 - N., 2 * sa, 2 * sc + 2))) - ph <- (sa + 1) / (sa + 1 + sc / (1 / qf(1 - N., 2 * sc, 2 * sa + 2))) - cIRR.l <- pl * sd / ((1 - pl) * sb) - cIRR.u <- ph * sd / ((1 - ph) * sb) - # clnIRR.l <- clnIRR - (z * clnIRR.se) - # clnIRR.u <- clnIRR + (z * clnIRR.se) - # cIRR.l <- exp(clnIRR.l) - # cIRR.u <- exp(clnIRR.u) - - # Crude odds ratios (Rothman p 139 equation 7-6): - cOR.p <- (sa * sd) / (sb * sc) - clnOR <- log(cOR.p) - clnOR.se <- sqrt(1/sa + 1/sb + 1/sc + 1/sd) - clnOR.l <- clnOR - (z * clnOR.se) - clnOR.u <- clnOR + (z * clnOR.se) - cOR.se <- exp(clnOR.se) - cOR.l <- exp(clnOR.l) - cOR.u <- exp(clnOR.u) - - # Crude attributable risk (Rothman p 135 equation 7-2): - cARisk.p <- ((sa / sN1) - (sc / sN0)) * units - cARisk.se <- (sqrt(((sa * (sN1 - sa))/sN1^3) + ((sc * (sN0 - sc))/sN0^3))) * units - cARisk.l <- cARisk.p - (z * cARisk.se) - cARisk.u <- cARisk.p + (z * cARisk.se) - - # Crude attributable rate (Rothman p 137 equation 7-4): - cARate.p <- ((sa / sb) - (sc / sd)) * units - cARate.se <- (sqrt((sa / sb^2) + (sc / sd^2))) * units - cARate.l <- cARate.p - (z * cARate.se) - cARate.u <- cARate.p + (z * cARate.se) - # Crude attributable fraction for risk data (from Hanley 2001): - cAFRisk.p <- (cRR.p - 1) / cRR.p - cAFRisk.l <- min((cRR.l - 1) / cRR.l, (cRR.u - 1) / cRR.u) - cAFRisk.u <- max((cRR.l - 1) / cRR.l, (cRR.u - 1) / cRR.u) - - # Crude attributable fraction for rate data (from Hanley 2001): - cAFRate.p <- (cIRR.p - 1) / cIRR.p - cAFRate.l <- min((cIRR.l - 1) / cIRR.l, (cIRR.u - 1) / cIRR.u) - cAFRate.u <- max((cIRR.l - 1) / cIRR.l, (cIRR.u - 1) / cIRR.u) - - # Crude estimated attributable fraction (from Hanley 2001): - cAFest.p <- (cOR.p - 1) / cOR.p - cAFest.l <- min((cOR.l - 1) / cOR.l, (cOR.u - 1) / cOR.u) - cAFest.u <- max((cOR.l - 1) / cOR.l, (cOR.u - 1) / cOR.u) - - # Crude population attributable risk (same as Rothman p 135 equation 7-2): - cPARisk.p <- ((sM1 / stotal) - (sc / sN0)) * units - cPARisk.se <- (sqrt(((sM1 * (stotal - sM1))/stotal^3) + ((sc * (sN0 - sc))/sN0^3))) * units - cPARisk.l <- cPARisk.p - (z * cPARisk.se) - cPARisk.u <- cPARisk.p + (z * cPARisk.se) - - # Crude population attributable rate (same as Rothman p 137 equation 7-4): - cPARate.p <- ((sM1 / sM0) - (sc / sd)) * units - cPARate.se <- (sqrt((sM1 / sM0^2) + (sc / sd^2))) * units - cPARate.l <- cPARate.p - (z * cPARate.se) - cPARate.u <- cPARate.p + (z * cPARate.se) - # Crude population attributable fractions for risk data (from Hanley 2001): - # cPAFRisk.p <- ((cRR.p - 1) / cRR.p) * (sa / sM1) - # cPAFRisk.l <- ((cRR.l - 1) / cRR.l) * (sa / sM1) - # cPAFRisk.u <- ((cRR.u - 1) / cRR.u) * (sa / sM1) - - # Crude population attributable fractions for risk data (from OpenEpi TwobyTwo): - # Changed 160609 - cPAFRisk.p <- (cIRiskpop.p - cIRisko.p) / cIRiskpop.p - cPAFRisk.l <- min((cIRiskpop.l - cIRisko.l) / cIRiskpop.l, (cIRiskpop.u - cIRisko.u) / cIRiskpop.u) - cPAFRisk.u <- max((cIRiskpop.l - cIRisko.l) / cIRiskpop.l, (cIRiskpop.u - cIRisko.u) / cIRiskpop.u) - - # Crude population attributable fractions for rate data (from Hanley 2001): - cPAFRate.p <- ((cIRR.p - 1) / cIRR.p) * (sa / sM1) - cPAFRate.l <- ((cIRR.p - 1) / cIRR.p) * (sa / sM1) - cPAFRate.u <- ((cIRR.p - 1) / cIRR.p) * (sa / sM1) - - # Crude population attributable fractions for rate data (from OpenEpi TwobyTwo): - # Changed 160609 - cPAFRate.p <- (cIRatepop.p - cIRateo.p) / cIRatepop.p - cPAFRate.l <- min((cIRatepop.l - cIRateo.l) / cIRatepop.l, (cIRatepop.u - cIRateo.u) / cIRatepop.u) - cPAFRate.u <- max((cIRatepop.l - cIRateo.l) / cIRatepop.l, (cIRatepop.u - cIRateo.u) / cIRatepop.u) - - # Crude estimated population attributable fraction (from Hanley, 2001): - # cPAFest.p <- ((cOR.p - 1) / cOR.p) * (sa / sM1) - # cPAFest.l <- ((cOR.p - 1) / cOR.p) * (sa / sM1) - # cPAFest.u <- ((cOR.p - 1) / cOR.p) * (sa / sM1) - - # Crude estimated population attributable fraction (from OpenEpi TwobyTwo): - # Changed 160609 - cPAFest.p <- (cOpop.p - cOo.p) / cOpop.p - cPAFest.l <- min((cOpop.l - cOo.l) / cOpop.l, (cOpop.u - cOo.u) / cOpop.u) - cPAFest.u <- max((cOpop.l - cOo.l) / cOpop.l, (cOpop.u - cOo.u) / cOpop.u) - - - # =============================== - # CHI-SQUARED TESTS - # =============================== - - # Dawson Saunders and Trapp page 151: - exp.a <- (N1 * M1) / total - exp.b <- (N1 * M0) / total - exp.c <- (N0 * M1) / total - exp.d <- (N0 * M0) / total - chi2 <- (((a - exp.a)^2)/ exp.a) + (((b - exp.b)^2)/ exp.b) + (((c - exp.c)^2)/ exp.c) + (((d - exp.d)^2)/ exp.d) - p.chi2 <- 1 - pchisq(chi2, df = 1) - - # Summary chi-squared test statistic with 1 degree of freedom: - exp.sa <- (sN1 * sM1) / stotal - exp.sb <- (sN1 * sM0) / stotal - exp.sc <- (sN0 * sM1) / stotal - exp.sd <- (sN0 * sM0) / stotal - chi2s <- (((sa - exp.sa)^2)/ exp.sa) + (((sb - exp.sb)^2)/ exp.sb) + (((sc - exp.sc)^2)/ exp.sc) + (((sd - exp.sd)^2)/ exp.sd) - p.chi2s <- 1 - pchisq(chi2s, df = 1) - - # =============================== - # MANTEL-HAENZEL SUMMARY MEASURES - # ================================ - - # Summary incidence risk ratio (Rothman 2002 p 148 and 152, equation 8-2): - sRR.p <- sum((a * N0 / total)) / sum((c * N1 / total)) - varLNRR.s <- sum(((M1 * N1 * N0) / total^2) - ((a * c)/ total)) / - (sum((a * N0)/total) * sum((c * N1)/total)) - lnRR.s <- log(sRR.p) - sRR.se <- (sqrt(varLNRR.s)) - sRR.l <- exp(lnRR.s - (z * sqrt(varLNRR.s))) - sRR.u <- exp(lnRR.s + (z * sqrt(varLNRR.s))) - - # Summary incidence rate ratio (Rothman 2002 p 153, equation 8-5): - sIRR.p <- sum((a * d) / M0) / sum((c * b) / M0) - lnIRR.s <- log(sIRR.p) - varLNIRR.s <- (sum((M1 * b * d) / M0^2)) / (sum((a * d) / M0) * sum((c * b) / M0)) - sIRR.se <- sqrt(varLNIRR.s) - sIRR.l <- exp(lnIRR.s - (z * sqrt(varLNIRR.s))) - sIRR.u <- exp(lnIRR.s + (z * sqrt(varLNIRR.s))) - - # Summary odds ratio (Cord Heuer 211004): - sOR.p <- sum((a * d / total)) / sum((b * c / total)) - G <- a * d / total - H <- b * c / total - P <- (a + d) / total - Q <- (b + c) / total - GQ.HP <- G * Q + H * P - sumG <- sum(G) - sumH <- sum(H) - sumGP <- sum(G * P) - sumGH <- sum(G * H) - sumHQ <- sum(H * Q) - sumGQ <- sum(G * Q) - sumGQ.HP <- sum(GQ.HP) - varLNOR.s <- sumGP / (2 * sumG^2) + sumGQ.HP/(2 * sumGH) + sumHQ/(2 * sumH^2) - lnOR.s <- log(sOR.p) - sOR.se <- sqrt(varLNOR.s) - sOR.l <- exp(lnOR.s - z * sqrt(varLNOR.s)) - sOR.u <- exp(lnOR.s + z * sqrt(varLNOR.s)) - - # Summary attributable risk (Rothman 2002 p 147 and p 152, equation 8-1): - sARisk.p <- (sum(((a * N0) - (c * N1)) / total) / sum((N1 * N0) / total)) * units - w <- (N1 * N0) / total - var.p1 <- (((a * d) / (N1^2 * (N1 - 1))) + ((c * b) / (N0^2 * (N0 - 1)))) - var.p1[N0 == 1] <- 0 - var.p1[N1 == 1] <- 0 - varARisk.s <- sum(w^2 * var.p1) / sum(w)^2 - sARisk.se <- (sqrt(varARisk.s)) * units - sARisk.l <- sARisk.p - (z * sARisk.se) - sARisk.u <- sARisk.p + (z * sARisk.se) - - # Summary attributable rate (Rothman 2002 p 153, equation 8-4): - sARate.p <- sum(((a * d) - (c * b)) / M0) / sum((b * d) / M0) * units - varARate.s <- sum(((b * d) / M0)^2 * ((a / b^2) + (c / d^2 ))) / sum((b * d) / M0)^2 - sARate.se <- sqrt(varARate.s) * units - sARate.l <- sARate.p - (z * sARate.se) - sARate.u <- sARate.p + (z * sARate.se) - - # =============================== - # EFFECT OF CONFOUNDING - # =============================== - # Effect of confounding for risk ratio (Woodward p 172): - RR.conf.p <- (cRR.p/sRR.p) - RR.conf.l <- (cRR.l/sRR.l) - RR.conf.u <- (cRR.u/sRR.u) - - # Effect of confounding for incidence risk ratio (Woodward p 172): - IRR.conf.p <- (cIRR.p/sIRR.p) - IRR.conf.l <- (cIRR.l/sIRR.l) - IRR.conf.u <- (cIRR.u/sIRR.u) - - # Effect of confounding for odds ratio (Woodward p 172): - OR.conf.p <- (cOR.p/sOR.p) - OR.conf.l <- (cOR.l/sOR.l) - OR.conf.u <- (cOR.u/sOR.u) - - # Effect of confounding for attributable risk (Woodward p 172): - ARisk.conf.p <- (cARisk.p/sARisk.p) - ARisk.conf.l <- (cARisk.l/sARisk.l) - ARisk.conf.u <- (cARisk.u/sARisk.u) - # Effect of confounding for attributable rate (Woodward p 172): - ARate.conf.p <- (cARate.p/sARate.p) - ARate.conf.l <- (cARate.l/sARate.l) - ARate.conf.u <- (cARate.u/sARate.u) - - - # =============================== - # TESTS OF HOMOGENEITY AND EFFECT - # =============================== - - if(length(a) > 1){ - # Test of relative risk homogeneity: - RR.homogeneity <- sum((lnRR - lnRR.s)^2 / lnRR.var) - # Test of effect: - RR.homogeneity.p <- 1 - pchisq(RR.homogeneity, df = n.strata - 1) - RR.homog <- as.data.frame(cbind(test.statistic = RR.homogeneity, df = n.strata - 1, p.value = RR.homogeneity.p)) - - # Test of odds ratio homogeneity: - OR.homogeneity <- sum((lnOR - lnOR.s)^2) / var(lnOR) - # Test of effect: - OR.homogeneity.p <- 1 - pchisq(OR.homogeneity, df = n.strata - 1) - OR.homog <- as.data.frame(cbind(test.statistic = OR.homogeneity, df = n.strata - 1, p.value = OR.homogeneity.p)) - # Test of attributable risk homogeneity (see Woodward p 207): - # AR.homogeneity <- sum(AR.p - AR.s)^2 / SE.AR^2 - # Test of effect: - # AR.homogeneity.p <- 1 - pchisq(AR.homogeneity, df = n.strata - 1) - # AR.homog <- as.data.frame(cbind(test.statistic = AR.homogeneity, df = n.strata - 1, p.value = AR.homogeneity.p)) - } - - # =============================== - # RESULTS - # ================================ - - # Incidence risk ratio: - RR <- as.data.frame(cbind(RR.p, RR.se, RR.w, RR.l, RR.u)) - names(RR) <- c("est", "se", "weight", lower, upper) - - # Incidence rate ratio: - IRR <- as.data.frame(cbind(IRR.p, IRR.se, IRR.w, IRR.l, IRR.u)) - names(IRR) <- c("est", "se", "weight", lower, upper) - - # Odds ratio: - OR <- as.data.frame(cbind(OR.p, OR.se, OR.w, OR.l, OR.u)) - names(OR) <- c("est", "se", "weight", lower, upper) - - # Corrected incidence risk ratio: - cRR <- as.data.frame(cbind(cRR.p, cRR.l, cRR.u)) - names(cRR) <- c("est", lower, upper) - - # Attributable risk: - ARisk <- as.data.frame(cbind(ARisk.p, ARisk.se, ARisk.w, ARisk.l, ARisk.u)) - names(ARisk) <- c("est", "se", "weight", lower, upper) - - # Attributable rate: - ARate <- as.data.frame(cbind(ARate.p, ARate.se, ARate.l, ARate.u)) - names(ARate) <- c("est", "se", lower, upper) - - # Attributable fraction for risk data: - AFRisk <- as.data.frame(cbind(AFRisk.p, AFRisk.l, AFRisk.u)) - names(AFRisk) <- c("est", lower, upper) - - # Attributable fraction for rate data: - AFRate <- as.data.frame(cbind(AFRate.p, AFRate.l, AFRate.u)) - names(AFRate) <- c("est", lower, upper) - - # Estimated attributable fraction: - AFest <- as.data.frame(cbind(AFest.p, AFest.l, AFest.u)) - names(AFest) <- c("est", lower, upper) - - # Population attributable risk: - PARisk <- as.data.frame(cbind(PARisk.p, PARisk.se, PARisk.l, PARisk.u)) - names(PARisk) <- c("est", "se", lower, upper) - - # Population attributable rate: - PARate <- as.data.frame(cbind(PARate.p, PARate.se, PARate.l, PARate.u)) - names(PARate) <- c("est", "se", lower, upper) - - # Population attributable fraction for risk data: - PAFRisk <- as.data.frame(cbind(PAFRisk.p, PAFRisk.l, PAFRisk.u)) - names(PAFRisk) <- c("est", lower, upper) - - # Population attributable fraction for rate data: - PAFRate <- as.data.frame(cbind(PAFRate.p, PAFRate.l, PAFRate.u)) - names(PAFRate) <- c("est", lower, upper) - - # Estimated population attributable fraction: - PAFest <- as.data.frame(cbind(PAFest.p, PAFest.l, PAFest.u)) - names(PAFest) <- c("est", lower, upper) - - # Crude incidence risk ratio: - RR.crude <- as.data.frame(cbind(cRR.p, cRR.se, cRR.l, cRR.u)) - names(RR.crude) <- c("est", "se", lower, upper) - - # Crude incidence rate ratio: - IRR.crude <- as.data.frame(cbind(cIRR.p, cIRR.se, cIRR.l, cIRR.u)) - names(IRR.crude) <- c("est", "se", lower, upper) - - # Crude odds ratio: - OR.crude <- as.data.frame(cbind(cOR.p, cOR.se, cOR.l, cOR.u)) - names(OR.crude) <- c("est", "se", lower, upper) - - # Crude attributable risk: - ARisk.crude <- as.data.frame(cbind(cARisk.p, cARisk.se, cARisk.l, cARisk.u)) - names(ARisk.crude) <- c("est", "se", lower, upper) - - # Crude attributable rate: - ARate.crude <- as.data.frame(cbind(cARate.p, cARate.se, cARate.l, cARate.u)) - names(ARate.crude) <- c("est", "se", lower, upper) - - # Crude attributable fraction for risk data: - AFRisk.crude <- as.data.frame(cbind(cAFRisk.p, cAFRisk.l, cAFRisk.u)) - names(AFRisk.crude) <- c("est", lower, upper) - - # Crude attributable fraction for rate data: - AFRate.crude <- as.data.frame(cbind(cAFRate.p, cAFRate.l, cAFRate.u)) - names(AFRate.crude) <- c("est", lower, upper) - - # Crude estimated attributable fraction: - AFest.crude <- as.data.frame(cbind(cAFest.p, cAFest.l, cAFest.u)) - names(AFest.crude) <- c("est", lower, upper) - - # Crude population attributable risk: - PARisk.crude <- as.data.frame(cbind(cPARisk.p, cPARisk.se, cPARisk.l, cPARisk.u)) - names(PARisk.crude) <- c("est", "se", lower, upper) - - # Crude population attributable rate: - PARate.crude <- as.data.frame(cbind(cPARate.p, cPARate.se, cPARate.l, cPARate.u)) - names(PARate.crude) <- c("est", "se", lower, upper) - - # Crude population attributable fraction for risk data: - PAFRisk.crude <- as.data.frame(cbind(cPAFRisk.p, cPAFRisk.l, cPAFRisk.u)) - names(PAFRisk.crude) <- c("est", lower, upper) - - # Crude population attributable fraction for rate data: - PAFRate.crude <- as.data.frame(cbind(cPAFRate.p, cPAFRate.l, cPAFRate.u)) - names(PAFRate.crude) <- c("est", lower, upper) - - # Crude estimated population attributable fraction: - PAFest.crude <- as.data.frame(cbind(cPAFest.p, cPAFest.l, cPAFest.u)) - names(PAFest.crude) <- c("est", lower, upper) - - # Summary incidence risk ratio: - RR.summary <- as.data.frame(cbind(sRR.p, sRR.se, sRR.l, sRR.u)) - names(RR.summary) <- c("est", "se", lower, upper) - - # Summary incidence rate ratio: - IRR.summary <- as.data.frame(cbind(sIRR.p, sIRR.se, sIRR.l, sIRR.u)) - names(IRR.summary) <- c("est", "se", lower, upper) - - # Summary odds ratio: - OR.summary <- as.data.frame(cbind(sOR.p, sOR.se, sOR.l, sOR.u)) - names(OR.summary) <- c("est", "se", lower, upper) - - # Summary attributable risk: - ARisk.summary <- as.data.frame(cbind(sARisk.p, sARisk.se, sARisk.l, sARisk.u)) - names(ARisk.summary) <- c("est", "se", lower, upper) - - # Summary attributable rate: - ARate.summary <- as.data.frame(cbind(sARate.p, sARate.se, sARate.l, sARate.u)) - names(ARate.summary) <- c("est", "se", lower, upper) - - # Effect of confounding for risk ratio (Woodward p 172): - RR.conf <- as.data.frame(cbind(RR.conf.p, RR.conf.l, RR.conf.u)) - names(RR.conf) <- c("est", lower, upper) - - # Effect of confounding for risk ratio (Woodward p 172): - IRR.conf <- as.data.frame(cbind(IRR.conf.p, IRR.conf.l, IRR.conf.u)) - names(IRR.conf) <- c("est", lower, upper) - - # Effect of confounding for odds ratio (Woodward p 172): - OR.conf <- as.data.frame(cbind(OR.conf.p, OR.conf.l, OR.conf.u)) - names(OR.conf) <- c("est", lower, upper) - - # Effect of confounding for attributable risk (Woodward p 172): - ARisk.conf <- as.data.frame(cbind(ARisk.conf.p, ARisk.conf.l, ARisk.conf.u)) - names(ARisk.conf) <- c("est", lower, upper) - - # Effect of confounding for attributable risk (Woodward p 172): - ARate.conf <- as.data.frame(cbind(ARate.conf.p, ARate.conf.l, ARate.conf.u)) - names(ARate.conf) <- c("est", lower, upper) - - chisq <- as.data.frame(cbind(test.statistic = chi2, df = 1, p.value = p.chi2)) - chisq.summary <- as.data.frame(cbind(test.statistic = chi2s, df = 1, p.value = p.chi2s)) - - # Labelling for incidence prevalence units: - count.units <- ifelse(units == 1, "Cases per population unit", paste("Cases per ", units, " population units", sep = "")) - time.units <- ifelse(units == 1, "Cases per unit of population time at risk", paste("Cases per ", units, " units of population time at risk", sep = "")) - - # Results for method == "cohort.count": - if(method == "cohort.count" & length(a) == 1 & verbose == TRUE){ - rval <- list( - RR = RR.crude, - OR = OR.crude, - AR = ARisk, - ARp = PARisk, - AFe = AFRisk, - AFp = PAFRisk, - chisq = chisq) - } - - if(method == "cohort.count" & length(a) == 1 & verbose == FALSE){ - # Define tab: - r1 <- c(a, b, N1, cIRiske.p, cOe.p) - r2 <- c(c, d, N0, cIRisko.p, cOo.p) - r3 <- c(M1, M0, M0 + M1, cIRiskpop.p, cOpop.p) - tab <- as.data.frame(rbind(r1, r2, r3)) - colnames(tab) <- c(" Disease +", " Disease -", " Total", " Inc risk *", " Odds") - rownames(tab) <- c("Exposed +", "Exposed -", "Total") - tab <- format.data.frame(tab, digits = 3, justify = "right") - - print(tab) - cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") - cat("\n---------------------------------------------------------") - cat("\nInc risk ratio ", round(cRR.p, digits = 2), paste("(", round(cRR.l, digits = 2), ", ", round(cRR.u, digits = 2), ")", sep = "")) - cat("\nOdds ratio ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) - cat("\nAttrib risk * ", round(ARisk.p, digits = 2), paste("(", round(ARisk.l, digits = 2), ", ", round(ARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib risk in population * ", round(PARisk.p, digits = 2), paste("(", round(PARisk.l, digits = 2), ", ", round(PARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib fraction in exposed (%) ", round(AFRisk.p * 100, digits = 2), paste("(", round(AFRisk.l * 100, digits = 2), ", ", round(AFRisk.u * 100, digits = 2), ")", sep = "")) - cat("\nAttrib fraction in population (%) ", round(PAFRisk.p * 100, digits = 2), paste("(", round(PAFRisk.l * 100, digits = 2), ", ", round(PAFRisk.u * 100, digits = 2), ")", sep = "")) - cat("\n---------------------------------------------------------") - cat("\n", "*", count.units, "\n") - } - - if(method == "cohort.count" & length(a) > 1 & verbose == TRUE){ - rval <- list( - RR = RR, - RR.crude = RR.crude, - RR.summary = RR.summary, - - OR = OR, - OR.crude = OR.crude, - OR.summary = OR.summary, - - AR = ARisk, - AR.crude = ARisk.crude, - AR.summary = ARisk.summary, - ARp = PARisk, - - AFe = AFRisk, - AFp = PAFRisk, - - chisq = chisq, - chisq.summary = chisq.summary, - RR.homog = RR.homog, - OR.homog = OR.homog) - } - - if(method == "cohort.count" & length(a) > 1 & verbose == FALSE){ - # Define tab: - r1 <- c(sa, sb, sN1, cIRiske.p, cOe.p) - r2 <- c(sc, sd, sN0, cIRisko.p, cOo.p) - r3 <- c(sM1, sM0, sM0 + sM1, cIRiskpop.p, cOpop.p) - tab <- as.data.frame(rbind(r1, r2, r3)) - colnames(tab) <- c(" Disease +", " Disease -", " Total", " Inc risk *", " Odds") - rownames(tab) <- c("Exposed +", "Exposed -", "Total") - tab <- format.data.frame(tab, digits = 3, justify = "right") - print(tab) - - cat("\n") - cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") - cat("\n---------------------------------------------------------") - cat("\nInc risk ratio (crude) ", round(cRR.p, digits = 2), paste("(", round(cRR.l, digits = 2), ", ", round(cRR.u, digits = 2), ")", sep = "")) - cat("\nInc risk ratio (M-H) ", round(sRR.p, digits = 2), paste("(", round(sRR.l, digits = 2), ", ", round(sRR.u, digits = 2), ")", sep = "")) - cat("\nInc risk ratio (crude:M-H) ", round(RR.conf.p, digits = 2)) - cat("\nOdds ratio (crude) ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) - cat("\nOdds ratio (M-H) ", round(sOR.p, digits = 2), paste("(", round(sOR.l, digits = 2), ", ", round(sOR.u, digits = 2), ")", sep = "")) - cat("\nOdds ratio (crude:M-H) ", round(OR.conf.p, digits = 2)) - cat("\nAttrib risk (crude) * ", round(cARisk.p, digits = 2), paste("(", round(cARisk.l, digits = 2), ", ", round(cARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib risk (M-H) * ", round(sARisk.p, digits = 2), paste("(", round(sARisk.l, digits = 2), ", ", round(sARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib risk (crude:M-H) ", round(ARisk.conf.p, digits = 2)) - cat("\n---------------------------------------------------------") - cat("\n", "*", count.units, "\n") - } - - # Results for method == "cohort.time": - if(method == "cohort.time" & length(a) == 1 & verbose == TRUE){ - rval <- list( - IRR = IRR.crude, - AR = ARate, - ARp = PARate, - AFe = AFRate, - AFp = PAFRate, - chisq = chisq) - } - - if(method == "cohort.time" & length(a) == 1 & verbose == FALSE){ - # Define tab: - r1 <- c(a, b, cIRatee.p) - r2 <- c(c, d, cIRateo.p) - r3 <- c(M1, M0, cIRatepop.p) - tab <- as.data.frame(rbind(r1, r2, r3)) - colnames(tab) <- c(" Disease +", " Time at risk", " Inc rate *") - rownames(tab) <- c("Exposed +", "Exposed -", "Total") - tab <- format.data.frame(tab, digits = 3, justify = "right") - print(tab) - cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") - cat("\n---------------------------------------------------------") - cat("\nInc rate ratio ", round(cIRR.p, digits = 2), paste("(", round(cIRR.l, digits = 2), ", ", round(cIRR.u, digits = 2), ")", sep = "")) - cat("\nAttrib rate * ", round(ARate.p, digits = 2), paste("(", round(ARate.l, digits = 2), ", ", round(ARate.u, digits = 2), ")", sep = "")) - cat("\nAttrib rate in population * ", round(PARate.p, digits = 2), paste("(", round(PARate.l, digits = 2), ", ", round(PARate.u, digits = 2), ")", sep = "")) - cat("\nAttrib fraction in exposed (%) ", round(AFRate.p * 100, digits = 2), paste("(", round(AFRate.l * 100, digits = 2), ", ", round(AFRate.u * 100, digits = 2), ")", sep = "")) - cat("\nAttrib fraction in population (%) ", round(PAFRate.p * 100, digits = 2), paste("(", round(PAFRate.l * 100, digits = 2), ", ", round(PAFRate.u * 100, digits = 2), ")", sep = "")) - cat("\n---------------------------------------------------------") - cat("\n", "*", time.units, "\n") - } - - if(method == "cohort.time" & length(a) > 1 & verbose == TRUE){ - rval <- list( - IRR = IRR, - IRR.crude = IRR.crude, - IRR.summary = IRR.summary, - - AR = ARate, - AR.crude = ARate.crude, - AR.summary = ARate.summary, - - ARp = PARate, - AFp = PAFRate, - - chisq = chisq, - chisq.summary = chisq.summary) - # RR.homog = RR.homog, - # OR.homog = OR.homog) - } - - if(method == "cohort.time" & length(a) > 1 & verbose == FALSE){ - # Define tab: - r1 <- c(sa, sb, cIRatee.p) - r2 <- c(sc, sd, cIRateo.p) - r3 <- c(sM1, sM0, cIRatepop.p) - tab <- as.data.frame(rbind(r1, r2, r3)) - colnames(tab) <- c(" Disease +", " Time at risk", " Inc rate *") - rownames(tab) <- c("Exposed +", "Exposed -", "Total") - tab <- format.data.frame(tab, digits = 3, justify = "right") - print(tab) - cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") - cat("\n---------------------------------------------------------") - cat("\nInc rate ratio (crude) ", round(cIRR.p, digits = 2), paste("(", round(cIRR.l, digits = 2), ", ", round(cIRR.u, digits = 2), ")", sep = "")) - cat("\nInc rate ratio (M-H) ", round(sIRR.p, digits = 2), paste("(", round(sIRR.l, digits = 2), ", ", round(sIRR.u, digits = 2), ")", sep = "")) - cat("\nInc rate ratio (crude:M-H) ", round(IRR.conf.p, digits = 2)) - cat("\nAttrib rate (crude) * ", round(cARate.p, digits = 2), paste("(", round(cARate.l, digits = 2), ", ", round(cARate.u, digits = 2), ")", sep = "")) - cat("\nAttrib rate (M-H) * ", round(sARate.p, digits = 2), paste("(", round(sARate.l, digits = 2), ", ", round(sARate.u, digits = 2), ")", sep = "")) - cat("\nAttrib rate (crude:M-H) ", round(ARate.conf.p, digits = 2)) - cat("\n---------------------------------------------------------") - cat("\n", "*", time.units, "\n") - } - - # Results for method == "case.control": - if(method == "case.control" & length(a) == 1 & verbose == TRUE){ - rval <- list( - OR = OR.crude, - AR = ARisk, - ARp = PARisk, - - AFest = AFest, - AFp = PAFest, - chisq = chisq) - } - - if(method == "case.control" & length(a) == 1 & verbose == FALSE){ - # Define tab: - r1 <- c(a, b, N1, cIRiske.p, cOe.p) - r2 <- c(c, d, N0, cIRisko.p, cOo.p) - r3 <- c(M1, M0, M0 + M1, cIRiskpop.p, cOpop.p) - tab <- as.data.frame(rbind(r1, r2, r3)) - colnames(tab) <- c(" Disease +", " Disease -", " Total", " Prevalence *", " Odds") - rownames(tab) <- c("Exposed +", "Exposed -", "Total") - tab <- format.data.frame(tab, digits = 3, justify = "right") - - print(tab) - cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") - cat("\n---------------------------------------------------------") - cat("\nOdds ratio ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) - cat("\nAttrib prevalence * ", round(ARisk.p, digits = 2), paste("(", round(ARisk.l, digits = 2), ", ", round(ARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib prevalence in population * ", round(PARisk.p, digits = 2), paste("(", round(PARisk.l, digits = 2), ", ", round(PARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib fraction (est) in exposed (%) ", round(AFest.p * 100, digits = 2), paste("(", round(AFest.l * 100, digits = 2), ", ", round(AFest.u * 100, digits = 2), ")", sep = "")) - cat("\nAttrib fraction (est) in population (%) ", round(PAFest.p * 100, digits = 2), paste("(", round(PAFest.l * 100, digits = 2), ", ", round(PAFest.u * 100, digits = 2), ")", sep = "")) - cat("\n---------------------------------------------------------") - cat("\n", "*", count.units, "\n") - } - - if(method == "case.control" & length(a) > 1 & verbose == TRUE){ - rval <- list( - OR = OR, - OR.crude = OR.crude, - OR.summary = OR.summary, - - AR = ARisk, - AR.crude = ARisk.crude, - AR.summary = ARisk.summary, - - ARp = PARisk, - AFest = AFest, - AFpest = PAFest, - - chisq = chisq, - chisq.summary = chisq.summary, - OR.homog = OR.homog) - } - - if(method == "case.control" & length(a) > 1 & verbose == FALSE){ - # Define tab: - r1 <- c(sa, sb, sN1, cIRiske.p, cOe.p) - r2 <- c(sc, sd, sN0, cIRisko.p, cOo.p) - r3 <- c(sM1, sM0, sM0 + sM1, cIRiskpop.p, cOpop.p) - tab <- as.data.frame(rbind(r1, r2, r3)) - colnames(tab) <- c(" Disease +", " Disease -", " Total", " Prevalence *", " Odds") - rownames(tab) <- c("Exposed +", "Exposed -", "Total") - tab <- format.data.frame(tab, digits = 3, justify = "right") - print(tab) - - cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") - cat("\n---------------------------------------------------------") - cat("\nOdds ratio (crude) ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) - cat("\nOdds ratio (M-H) ", round(sOR.p, digits = 2), paste("(", round(sOR.l, digits = 2), ", ", round(sOR.u, digits = 2), ")", sep = "")) - cat("\nOdds ratio (crude:M-H) ", round(OR.conf.p, digits = 2)) - cat("\nAttrib prevalence (crude) * ", round(cARisk.p, digits = 2), paste("(", round(cARisk.l, digits = 2), ", ", round(cARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib prevalence (M-H) * ", round(sARisk.p, digits = 2), paste("(", round(sARisk.l, digits = 2), ", ", round(sARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib prevalence (crude:M-H) ", round(ARate.conf.p, digits = 2)) - cat("\n---------------------------------------------------------") - cat("\n", "*", count.units, "\n") - } - - # Results for method == "cross.sectional": - if(method == "cross.sectional" & length(a) == 1 & verbose == TRUE){ - rval <- list( - RR = RR.crude, - OR = OR.crude, - AR = ARisk, - ARp = PARisk, - AFe = AFRisk, - AFp = PAFRisk, - chisq = chisq) - } - - if(method == "cross.sectional" & length(a) == 1 & verbose == FALSE){ - # Define tab: - r1 <- c(a, b, N1, cIRiske.p, cOe.p) - r2 <- c(c, d, N0, cIRisko.p, cOo.p) - r3 <- c(M1, M0, M0 + M1, cIRiskpop.p, cOpop.p) - tab <- as.data.frame(rbind(r1, r2, r3)) - colnames(tab) <- c(" Disease +", " Disease -", " Total", " Prevalence *", " Odds") - rownames(tab) <- c("Exposed +", "Exposed -", "Total") - tab <- format.data.frame(tab, digits = 3, justify = "right") - - print(tab) - cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") - cat("\n---------------------------------------------------------") - cat("\nPrevalence ratio ", round(cRR.p, digits = 2), paste("(", round(cRR.l, digits = 2), ", ", round(cRR.u, digits = 2), ")", sep = "")) - cat("\nOdds ratio ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) - cat("\nAttrib prevalence * ", round(ARisk.p, digits = 2), paste("(", round(ARisk.l, digits = 2), ", ", round(ARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib prevalence in population * ", round(PARisk.p, digits = 2), paste("(", round(PARisk.l, digits = 2), ", ", round(PARisk.u, digits = 2), ")", sep = "")) - cat("\nAttrib fraction in exposed (%) ", round(AFRisk.p * 100, digits = 2), paste("(", round(AFRisk.l * 100, digits = 2), ", ", round(AFRisk.u * 100, digits = 2), ")", sep = "")) - cat("\nAttrib fraction in population (%) ", round(PAFRisk.p * 100, digits = 2), paste("(", round(PAFRisk.l * 100, digits = 2), ", ", round(PAFRisk.u * 100, digits = 2), ")", sep = "")) - cat("\n---------------------------------------------------------") - cat("\n", "*", count.units, "\n") - } - - if(method == "cross.sectional" & length(a) > 1 & verbose == TRUE){ - rval <- list( - RR = RR, - RR.crude = RR.crude, - RR.summary = RR.summary, - - OR = OR, - OR.crude = OR.crude, - OR.summary = OR.summary, - - AR = ARisk, - AR.crude = ARisk.crude, - AR.summary = ARisk.summary, - ARp = PARisk, - - AFe = AFRisk, - AFp = PAFRisk, - - chisq = chisq, - chisq.summary = chisq.summary, - RR.homog = RR.homog, - OR.homog = OR.homog) - } - - else if(method == "cross.sectional" & length(a) > 1 & verbose == FALSE){ - # Define tab: - r1 <- c(sa, sb, sN1, cIRiske.p, cOe.p) - r2 <- c(sc, sd, sN0, cIRisko.p, cOo.p) - r3 <- c(sM1, sM0, sM1 + sM0, cIRiskpop.p, cOpop.p) - tab <- as.data.frame(rbind(r1, r2, r3)) - colnames(tab) <- c(" Disease +", " Disease -", " Total", " Prevalence *", " Odds") - rownames(tab) <- c("Exposed +", "Exposed -", "Total") - tab <- format.data.frame(tab, digits = 3, justify = "right") - print(tab) - - cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") - cat("\n---------------------------------------------------------") - cat("\nPrevalence ratio (crude) ", round(cRR.p, digits = 2), paste("(", round(cRR.l, digits = 2), ", ", round(cRR.u, digits = 2), ")", sep = "")) - cat("\nPrevalence ratio (M-H) ", round(sRR.p, digits = 2), paste("(", round(sRR.l, digits = 2), ", ", round(sRR.u, digits = 2), ")", sep = "")) - cat("\nPrevalence ratio (crude:M-H) ", round(RR.conf.p, digits = 2)) - cat("\nOdds ratio (crude) ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) - cat("\nOdds ratio (M-H) ", round(sOR.p, digits = 2), paste("(", round(sOR.l, digits = 2), ", ", round(sOR.u, digits = 2), ")", sep = "")) - cat("\nOdds ratio (crude:M-H) ", round(OR.conf.p, digits = 2)) - cat("\nAtributable prevalence (crude) * ", round(cARisk.p, digits = 2), paste("(", round(cARisk.l, digits = 2), ", ", round(cARisk.u, digits = 2), ")", sep = "")) - cat("\nAtributable prevalence (M-H) * ", round(sARisk.p, digits = 2), paste("(", round(sARisk.l, digits = 2), ", ", round(sARisk.u, digits = 2), ")", sep = "")) - cat("\nAtributable prevalence (crude:M-H) ", round(ARisk.conf.p, digits = 2)) - cat("\n---------------------------------------------------------") - cat("\n", "*", count.units, "\n") - } -if(verbose == TRUE){ - return(rval) - } -} diff -Nru r-cran-epir-0.9-32/R/epi.2by2.r r-cran-epir-0.9-38/R/epi.2by2.r --- r-cran-epir-0.9-32/R/epi.2by2.r 1970-01-01 00:00:00.000000000 +0000 +++ r-cran-epir-0.9-38/R/epi.2by2.r 2012-02-01 20:46:16.000000000 +0000 @@ -0,0 +1,1232 @@ +"epi.2by2" <- function(dat, method = "cohort.count", conf.level = 0.95, units = 100, homogeneity = "breslow.day", verbose = FALSE){ + # Elwoood JM (1992). Causal Relationships in Medicine - A Practical System for Critical Appraisal. Oxford Medical Publications, London, p 266 - 293. + # Rothman KJ (2002). Epidemiology An Introduction. Oxford University Press, London, p 130 - 143. + # Hanley JA (2001). A heuristic approach to the formulas for population attributable fraction. J. Epidemiol. Community Health 55:508 - 514. + # Jewell NP (2004). Statistics for Epidemiology. Chapman & Hall/CRC, New York, p 84 - 85. + + # Incidence risk in exposed IRiske + # Incidence risk in unexposed IRisko + # Incidence risk in population IRpop + + # Incidence rate in exposed IRatee + # Incidence rate in unexposed IRateo + # Incidence rate in population IRatepop + + # Odds in exposed Oe + # Odds in unexposed Oo + # Odds in population Opop + + # Incidence risk ratio RR.p + # Incidence rate ratio IRR.p + # Odds ratio OR.p + # Corrected incidence risk ratio cRR.p + + # Attributable risk ARisk.p + # Attributable rate ARate.p + + # Attributable fraction risk data AFRisk.p + # Attributable fraction rate data AFRate.p + # Estimated attributable fraction AFest.p + + # Population attributable risk PARisk.p + # Population attributable rate PARate.p + + # Population attributable fraction risk data PAFRisk.p + # Population attributable fraction rate data PAFRate.p + + # Crude incidence risk ratio (strata): cRR.p + # Crude incidence rate ratio (strata): cIRR.p + # Crude incidence odds ratio (strata): cOR.p + # Crude attributable risk (strata): cARisk.p + # Crude attributable rate (strata): cARate.p + + # Summary incidence risk ratio: sRR.p + # Summary incidence rate ratio: sIRR.p + # Summary incidence odds ratio: sOR.p + # Summary attributable risk sARisk.p + # Summary attributable rate sARate.p + + # Reporting - method == cohort.count: + # Inc risk ratio; odds ratio + # Attributable risk; attributable risk in population + # Attributable fraction in exposed; attributable fraction in population + + # Reporting - method == cohort.time: + # Inc rate ratio + # Attributable rate; attributable rate in population + # Attributable fraction in exposed; attributable fraction in population + + # Reporting - method == case.control: + # Odds ratio + # Attributable prevalence; attributable prevalence in population + # Attributable fraction (est) in exposed; attributable fraction (est) in population + + # Reporting - method == cross.sectional: + # Prevalence ratio; odds ratio + # Attributable prevalence; attributable prevalence in population + # Attributable fraction in exposed; attributable fraction in population + + # Make a copy of the original data. These values used when sums of cells across all strata are greater than zero but + # some strata contain zero cell frequencies. + + if(length(dim(dat)) == 2){ + a <- dat[1]; A <- a + b <- dat[3]; B <- b + c <- dat[2]; C <- c + d <- dat[4]; D <- d + } + + if(length(dim(dat)) > 2){ + a <- dat[1,1,]; A <- a + b <- dat[1,2,]; B <- b + c <- dat[2,1,]; C <- c + d <- dat[2,2,]; D <- d + } + + # Test each strata for zero values. Add 0.5 to all cells if any cell has a zero value: + for(i in 1:length(a)){ + if(a[i] < 1 | b[i] < 1 | c[i] < 1 | d[i] < 1){ + a[i] <- a[i] + 0.5; b[i] <- b[i] + 0.5; c[i] <- c[i] + 0.5; d[i] <- d[i] + 0.5 + } + } + +.funincrisk <- function(dat, conf.level){ + # Exact binomial confidence limits from D. Collett (1999) Modelling binary data. Chapman & Hall/CRC, Boca Raton Florida, p. 24. + N. <- 1 - ((1 - conf.level) / 2) + a <- dat[,1] + n <- dat[,2] + b <- n - a + p <- a / n + + # Wilson's method (see Rothman, Epidemiology An Introduction, page 132): + # N. <- 1 - ((1 - conf.level) / 2) + # z <- qnorm(N., mean = 0, sd = 1) + # a <- dat[,1] + # n <- dat[,2] + # p <- dat[,1] / dat[,2] + + # a. <- n/(n + z^2) + # b. <- a/n + # c. <- z^2/(2 * n) + # d. <- (a * (n - a)) / n^3 + # e. <- z^2 / (4 * n^2) + # low <- a. * (b. + c. - (z * sqrt(d. + e.))) + # up <- a. * (b. + c. + (z * sqrt(d. + e.))) + + a. <- ifelse(a == 0, a + 1, a); b. <- ifelse(b == 0, b + 1, b) + low <- a. /(a. + (b. + 1) * (1 / qf(1 - N., 2 * a., 2 * b. + 2))) + up <- (a. + 1) / (a. + 1 + b. / (1 / qf(1 - N., 2 * b., 2 * a. + 2))) + low <- ifelse(a == 0, 0, low) + up <- ifelse(a == n, 1, up) + rval <- data.frame(p, low, up) + names(rval) <- c("est", "lower", "upper") + rval + } + +.funincrate <- function(dat, conf.level){ + N. <- 1 - ((1 - conf.level) / 2) + a <- dat[,1] + n <- dat[,2] + p <- a / n + low <- 0.5 * qchisq(p = N., df = 2 * a + 2, lower.tail = FALSE) / n + up <- 0.5 * qchisq(p = 1 - N., df = 2 * a + 2, lower.tail = FALSE) / n + # a.prime <- dat[,1] + 0.5 + # p <- dat[,1]/dat[,2] + # PT <- dat[,2] + # low <- (a.prime * (1 - (1/(9 * a.prime)) - (z/3 * sqrt(1/a.prime)))^3)/PT + # up <- (a.prime * (1 - (1/(9 * a.prime)) + (z/3 * sqrt(1/a.prime)))^3)/PT + + # Wilson's method (see Rothman, Epidemiology An Introduction, page 132): + # N. <- 1 - ((1 - conf.level) / 2) + # z <- qnorm(N., mean = 0, sd = 1) + # a <- dat[,1] + # n <- dat[,2] + # p <- dat[,1] / dat[,2] + # a. <- n/(n + z^2) + # b. <- a/n + # c. <- z^2/(2 * n) + # d. <- (a * (n - a)) / n^3 + # e. <- z^2 / (4 * n^2) + # low <- a. * (b. + c. - (z * sqrt(d. + e.))) + # up <- a. * (b. + c. + (z * sqrt(d. + e.))) + + rval <- data.frame(p, low, up) + names(rval) <- c("est", "lower", "upper") + rval + } + + # ================= + # DECLARE VARIABLES + # ================= + + # | D+ | D- | Total + # ---------------------------- + # Exp + | a | b | N1 + # Exp - | c | d | N0 + # -------|------|------|------ + # Total | M1 | M0 | Total + + + N. <- 1 - ((1 - conf.level) / 2) + z <- qnorm(N., mean = 0, sd = 1) + + # For large numbers you need to use floating point rather than integer representation. This will avoid "integer overflow" messages: + a <- as.numeric(a); A <- as.numeric(A) + b <- as.numeric(b); B <- as.numeric(B) + c <- as.numeric(c); C <- as.numeric(C) + d <- as.numeric(d); D <- as.numeric(D) + + # Total within strata cases: + M1 <- a + c + # Total within strata non-cases: + M0 <- b + d + # Total within strata exposed: + N1 <- a + b + # Total within strata unexposed: + N0 <- c + d + # Total within strata subjects: + total <- a + b + c + d + # Number of strata: + n.strata <- length(a) + + # Added 190809: + # If the sums across strata for all cells are greater than 0, use the sums of the crude data (cf the sums of the adjusted values): + if(sum(A) > 0 & sum(B) > 0 & sum(C) > 0 & sum(D) > 0){ + sa <- sum(A); sb <- sum(B); sc <- sum(C); sd <- sum(D) + } + + # If the sums across strata for all cells contain a 0, use the sums of the adjusted data: + if(sum(A) == 0 | sum(B) == 0 | sum(C) == 0 | sum(D) == 0){ + sa <- sum(a); sb <- sum(b); sc <- sum(c); sd <- sum(d) + } + + # sa <- sum(a); sb <- sum(b); sc <- sum(c); sd <- sum(d) + + # Grand total cases: + sM1 <- sa + sc + # Grand total non-cases: + sM0 <- sb + sd + # Grand total exposed: + sN1 <- sa + sb + # Grand total unexposed: + sN0 <- sc + sd + # Grand total: + stotal <- sa + sb + sc + sd + + # Within-strata incidence risk in exposed: + tmp <- .funincrisk(as.matrix(cbind(a, N1)), conf.level = conf.level) + IRiske.p <- as.numeric(tmp[,1]) * units + IRiske.l <- as.numeric(tmp[,2]) * units + IRiske.u <- as.numeric(tmp[,3]) * units + + # Within-strata incidence risk in unexposed: + tmp <- .funincrisk(as.matrix(cbind(c, N0)), conf.level = conf.level) + IRisko.p <- as.numeric(tmp[,1]) * units + IRisko.l <- as.numeric(tmp[,2]) * units + IRisko.u <- as.numeric(tmp[,3]) * units + + # Within-strata incidence risk in population: + tmp <- .funincrisk(as.matrix(cbind(M1, total)), conf.level = conf.level) + IRiskpop.p <- as.numeric(tmp[,1]) * units + IRiskpop.l <- as.numeric(tmp[,2]) * units + IRiskpop.u <- as.numeric(tmp[,3]) * units + + # Within-strata incidence rate in exposed: + tmp <- .funincrate(as.matrix(cbind(a, b)), conf.level = conf.level) + IRatee.p <- as.numeric(tmp[,1]) * units + IRatee.l <- as.numeric(tmp[,2]) * units + IRatee.u <- as.numeric(tmp[,3]) * units + + # Within-strata incidence rate in unexposed: + tmp <- .funincrate(as.matrix(cbind(c, d)), conf.level = conf.level) + IRateo.p <- as.numeric(tmp[,1]) * units + IRateo.l <- as.numeric(tmp[,2]) * units + IRateo.u <- as.numeric(tmp[,3]) * units + + # Within-strata incidence rate in population: + tmp <- .funincrate(as.matrix(cbind(M1, M0)), conf.level = conf.level) + IRatepop.p <- as.numeric(tmp[,1]) * units + IRatepop.l <- as.numeric(tmp[,2]) * units + IRatepop.u <- as.numeric(tmp[,3]) * units + + # Within-strata odds in exposed (based on Ederer F and Mantel N (1974) Confidence limits on the ratio of two Poisson variables. + # American Journal of Epidemiology 100: 165 - 167. + # Cited in Altman, Machin, Bryant, and Gardner (2000) Statistics with Confidence, British Medical Journal, page 69). + # Added 160609. + Al <- (qbinom(1 - N., size = a + b, prob = (a / (a + b)))) / (a + b) + Au <- (qbinom(N., size = a + b, prob = (a / (a + b)))) / (a + b) + Oe.p <- (a / b) + Oe.l <- (Al / (1 - Al)) + Oe.u <- (Au / (1 - Au)) + + # Within-strata odds in unexposed: + Al <- (qbinom(1 - N., size = c + d, prob = (c / (c + d)))) / (c + d) + Au <- (qbinom(N., size = c + d, prob = (c / (c + d)))) / (c + d) + Oo.p <- (c / d) + Oo.l <- (Al / (1 - Al)) + Oo.u <- (Au / (1 - Au)) + + # Within-strata odds in population: + Al <- (qbinom(1 - N., size = M1 + M0, prob = (M1 / (M1 + M0)))) / (M1 + M0) + Au <- (qbinom(N., size = M1 + M0, prob = (M1 / (M1 + M0)))) / (M1 + M0) + Opop.p <- (M1 / M0) + Opop.l <- (Al / (1 - Al)) + Opop.u <- (Au / (1 - Au)) + + # Crude incidence risk in exposed: + tmp <- .funincrisk(as.matrix(cbind(sa, sN1)), conf.level = conf.level) + cIRiske.p <- as.numeric(tmp[,1]) * units + cIRiske.l <- as.numeric(tmp[,2]) * units + cIRiske.u <- as.numeric(tmp[,3]) * units + + # Crude incidence risk in unexposed: + tmp <- .funincrisk(as.matrix(cbind(sc, sN0)), conf.level = conf.level) + cIRisko.p <- as.numeric(tmp[,1]) * units + cIRisko.l <- as.numeric(tmp[,2]) * units + cIRisko.u <- as.numeric(tmp[,3]) * units + + # Crude incidence risk in population: + tmp <- .funincrisk(as.matrix(cbind(sM1, stotal)), conf.level = conf.level) + cIRiskpop.p <- as.numeric(tmp[,1]) * units + cIRiskpop.l <- as.numeric(tmp[,2]) * units + cIRiskpop.u <- as.numeric(tmp[,3]) * units + + # Crude incidence rate in exposed: + tmp <- .funincrate(as.matrix(cbind(sa, sb)), conf.level = conf.level) + cIRatee.p <- as.numeric(tmp[,1]) * units + cIRatee.l <- as.numeric(tmp[,2]) * units + cIRatee.u <- as.numeric(tmp[,3]) * units + + # Crude incidence rate in unexposed: + tmp <- .funincrate(as.matrix(cbind(sc, sd)), conf.level = conf.level) + cIRateo.p <- as.numeric(tmp[,1]) * units + cIRateo.l <- as.numeric(tmp[,2]) * units + cIRateo.u <- as.numeric(tmp[,3]) * units + + # Crude incidence risk in population: + tmp <- .funincrate(as.matrix(cbind(sM1, sM0)), conf.level = conf.level) + cIRatepop.p <- as.numeric(tmp[,1]) * units + cIRatepop.l <- as.numeric(tmp[,2]) * units + cIRatepop.u <- as.numeric(tmp[,3]) * units + + # Crude odds in exposed (based on Ederer F and Mantel N (1974) Confidence limits on the ratio of two Poisson variables. + # American Journal of Epidemiology 100: 165 - 167. + # Cited in Altman, Machin, Bryant, and Gardner (2000) Statistics with Confidence, British Medical Journal, page 69). + # Added 160609 + Al <- (qbinom(1 - N., size = sa + sb, prob = (sa / (sa + sb)))) / (sa + sb) + u <- (qbinom(N., size = sa + sb, prob = (sa / (sa + sb)))) / (sa + sb) + cOe.p <- sa / sb + cOe.l <- Al / (1 - Al) + cOe.u <- Au / (1 - Au) + + # Crude odds in unexposed: + Al <- (qbinom(1 - N., size = sc + sd, prob = (sc / (sc + sd)))) / (sc + sd) + u <- (qbinom(N., size = sc + sd, prob = (sc / (sc + sd)))) / (sc + sd) + cOo.p <- sc / sd + cOo.l <- Al / (1 - Al) + cOo.u <- Au / (1 - Au) + + # Crude odds in population: + Al <- (qbinom(1 - N., size = sM1 + sM0, prob = (sM1 / (sM1 + sM0)))) / (sM1 + sM0) + u <- (qbinom(N., size = sM1 + sM0, prob = (sM1 / (sM1 + sM0)))) / (sM1 + sM0) + cOpop.p <- sM1 / sM0 + cOpop.l <- Al / (1 - Al) + cOpop.u <- Au / (1 - Au) + + + # ========================================= + # INDIVIDUAL STRATA MEASURES OF ASSOCIATION + # ========================================= + + # Individual strata incidence risk ratio (Rothman p 135 equation 7-3): + RR.p <- (a / N1) / (c / N0) + lnRR <- log(RR.p) + lnRR.var <- (1 / a) - (1 / N1) + (1 / c) - (1 / N0) + lnRR.se <- sqrt((1 / a) - (1 / N1) + (1 / c) - (1 / N0)) + RR.se <- exp(lnRR.se) + RR.l <- exp(lnRR - (z * lnRR.se)) + RR.u <- exp(lnRR + (z * lnRR.se)) + # Incidence risk ratio weights (equal to precision, the inverse of the variance of the RR. See Woodward page 168): + RR.w <- 1 / (exp(lnRR.var)) + + # Individual strata incidence rate ratio (exact confidence intervals http://www.folkesundhed.au.dk/uddannelse/software): + IRR.p <- (a / b) / (c / d) + lnIRR <- log(IRR.p) + lnIRR.var <- (1 / a) + (1 / c) + lnIRR.se <- sqrt((1 / a) + (1 / c)) + IRR.se <- exp(lnIRR.se) + pl <- a / (a + (c + 1) * (1 / qf(1 - N., 2 * a, 2 * c + 2))) + ph <- (a + 1) / (a + 1 + c / (1 / qf(1 - N., 2 * c, 2 * a + 2))) + IRR.l <- pl * d / ((1 - pl) * b) + IRR.u <- ph * d / ((1 - ph) * b) + # lnIRR.l <- lnIRR - (z * lnIRR.se) + # lnIRR.u <- lnIRR + (z * lnIRR.se) + # IRR.l <- exp(lnIRR.l) + # IRR.u <- exp(lnIRR.u) + # Incidence rate ratio weights (equal to precision, the inverse of the variance of the IRR. See Woodward page 168): + IRR.w <- 1 / (exp(lnIRR.var)) + + # Individual strata odds ratios (Rothman p 139 equation 7-6): + OR.p <- (a * d) / (b * c) + lnOR <- log(OR.p) + lnOR.var <- 1/a + 1/b + 1/c + 1/d + lnOR.se <- sqrt(1/a + 1/b + 1/c + 1/d) + lnOR.l <- lnOR - (z * lnOR.se) + lnOR.u <- lnOR + (z * lnOR.se) + OR.se <- exp(lnOR.se) + OR.l <- exp(lnOR.l) + OR.u <- exp(lnOR.u) + # Odds ratio weights (equal to precision, the inverse of the variance of the OR. See Woodward page 168): + OR.w <- 1 / (exp(lnOR.var)) + + # Individual strata corrected incidence risk ratio (Zhang and Khai 1998): + cRR.p <- OR.p / ((1 - N0) + (N0 * OR.p)) + cRR.l <- OR.l / ((1 - N0) + (N0 * OR.l)) + cRR.u <- OR.u / ((1 - N0) + (N0 * OR.u)) + + # Individual strata attributable risk (Rothman p 135 equation 7-2): + ARisk.p <- ((a / N1) - (c / N0)) * units + # ARisk.var <- (((a * b) / (N1^2 * (N1 - 1))) + ((c * d) / (N0^2 * (N0 - 1)))) + ARisk.se <- (sqrt(((a * (N1 - a))/N1^3) + ((c * (N0 - c))/N0^3))) * units + ARisk.l <- (ARisk.p - (z * ARisk.se)) + ARisk.u <- (ARisk.p + (z * ARisk.se)) + # Attribtable risk weights (equal to precision, the inverse of the variance of the RR. See Woodward page 168): + ARisk.w <- 1 / (ARisk.se / units)^2 + + # Individual strata attributable rate (Rothman p 137 equation 7-4): + ARate.p <- ((a / b) - (c / d)) * units + ARate.var <- (a / b^2) + (c / d^2) + ARate.se <- (sqrt((a / b^2) + (c / d^2))) * units + ARate.l <- ARate.p - (z * ARate.se) + ARate.u <- ARate.p + (z * ARate.se) + # Attribtable rate weights (equal to precision, the inverse of the variance of the RR. See Woodward page 168): + ARate.w <- 1 / (ARate.var) + + # Individual strata attributable fraction for risk data (from Hanley 2001): + AFRisk.p <- ((RR.p - 1) / RR.p) + AFRisk.l <- min((RR.l - 1) / RR.l, (RR.u - 1) / RR.u) + AFRisk.u <- max((RR.l - 1) / RR.l, (RR.u - 1) / RR.u) + + # Individual strata attributable fraction for rate data (from Hanley 2001): + AFRate.p <- (IRR.p - 1) / IRR.p + AFRate.l <- min((IRR.l - 1) / IRR.l, (IRR.u - 1) / IRR.u) + AFRate.u <- max((IRR.l - 1) / IRR.l, (IRR.u - 1) / IRR.u) + + # Individual strata estimated attributable fraction (from Hanley 2001): + AFest.p <- (OR.p - 1) / OR.p + AFest.l <- min((OR.l - 1) / OR.l, (OR.u - 1) / OR.u) + AFest.u <- max((OR.l - 1) / OR.l, (OR.u - 1) / OR.u) + + # Individual strata population attributable risk (same as Rothman p 135 equation 7-2): + PARisk.p <- ((M1 / total) - (c / N0)) * units + PARisk.se <- (sqrt(((M1 * (total - M1))/total^3) + ((c * (N0 - c))/N0^3))) * units + PARisk.l <- PARisk.p - (z * PARisk.se) + PARisk.u <- PARisk.p + (z * PARisk.se) + + # Individual strata population attributable rate (same as Rothman p 137 equation 7-4): + PARate.p <- ((M1 / M0) - (c / d)) * units + PARate.se <- (sqrt((M1 / M0^2) + (c / d^2))) * units + PARate.l <- PARate.p - (z * PARate.se) + PARate.u <- PARate.p + (z * PARate.se) + # Individual strata population attributable fractions for risk data (from Hanley, 2001): + # PAFRisk.p <- ((RR.p - 1) / RR.p) * (a / M1) + # PAFRisk.l <- ((RR.l - 1) / RR.l) * (a / M1) + # PAFRisk.u <- ((RR.u - 1) / RR.u) * (a / M1) + # Individual strata population attributable fractions for risk data (from OpenEpi TwobyTwo): + # PAFRisk.p <- (IRiskpop.p - IRisko.p) / IRiskpop.p + # PAFRisk.l <- min((IRiskpop.l - IRisko.l) / IRiskpop.l, (IRiskpop.u - IRisko.u) / IRiskpop.u) + # PAFRisk.u <- max((IRiskpop.l - IRisko.l) / IRiskpop.l, (IRiskpop.u - IRisko.u) / IRiskpop.u) + + # Individual strata population attributable fractions for risk data (from Jewell, page 84): + PAFRisk.p <- ((a * d) - (b * c)) / ((a + c) * (c + d)) + PAFRisk.var <- (b + (PAFRisk.p * (a + d))) / (total * c) + PAFRisk.l <- 1 - exp(log(1 - PAFRisk.p) + (z * sqrt(PAFRisk.var))) + PAFRisk.u <- 1 - exp(log(1 - PAFRisk.p) - (z * sqrt(PAFRisk.var))) + + # Individual strata population attributable fractions for rate data (from Hanley, 2001): + # PAFRate.p <- ((IRR.p - 1) / IRR.p) * (a / M1) + # PAFRate.l <- ((IRR.l - 1) / IRR.l) * (a / M1) + # PAFRate.u <- ((IRR.u - 1) / IRR.u) * (a / M1) + + # Individual strata population attributable fractions for rate data (from OpenEpi TwobyTwo - Jewell doesn't provide a method for rate data): + PAFRate.p <- (IRatepop.p - IRateo.p) / IRatepop.p + PAFRate.l <- min((IRatepop.l - IRateo.l) / IRatepop.l, (IRatepop.u - IRateo.u) / IRatepop.u) + PAFRate.u <- max((IRatepop.l - IRateo.l) / IRatepop.l, (IRatepop.u - IRateo.u) / IRatepop.u) + + # Individual strata estimated population attributable fraction (from Hanley, 2001): + # PAFest.p <- ((OR.p - 1) / OR.p) * (a / M1) + # PAFest.l <- ((OR.l - 1) / OR.l) * (a / M1) + # PAFest.u <- ((OR.u - 1) / OR.u) * (a / M1) + + # Individual strata estimated population attributable fraction (from OpenEpi TwobyTwo): + # PAFest.p <- (Opop.p - Oo.p) / Opop.p + # PAFest.l <- min((Opop.l - Oo.l) / Opop.l, (Opop.u - Oo.u) / Opop.u) + # PAFest.u <- max((Opop.l - Oo.l) / Opop.l, (Opop.u - Oo.u) / Opop.u) + + # Individual strata population attributable fractions for risk data (from Jewell, page 84): + PAFest.p <- ((a * d) - (b * c)) / (d * (a + c)) + PAFest.var <- (a / (c * (a + c))) + (b / (d * (b + d))) + PAFest.l <- 1 - exp(log(1 - PAFest.p) + (z * sqrt(PAFest.var))) + PAFest.u <- 1 - exp(log(1 - PAFest.p) - (z * sqrt(PAFest.var))) + + # ============================= + # CRUDE MEASURES OF ASSOCIATION + # ============================= + + # Crude incidence risk ratio (Rothman p 135 equation 7-3): + cRR.p <- (sa / sN1) / (sc / sN0) + clnRR <- log(cRR.p) + clnRR.var <- (1 / sa) - (1 / sN1) + (1 / sc) - (1 / sN0) + # Line below incorrect. Fixed 191208: + # clnRR.se <- sqrt((1 / sa) - (1 / sN1) + (1 / sb) - (1 / sN0)) + clnRR.se <- sqrt((1 / sa) - (1 / sN1) + (1 / sc) - (1 / sN0)) + clnRR.l <- clnRR - (z * clnRR.se) + clnRR.u <- clnRR + (z * clnRR.se) + cRR.se <- exp(clnRR.se) + cRR.l <- exp(clnRR.l) + cRR.u <- exp(clnRR.u) + + # Crude incidence rate ratio (exact confidence intervals http://www.folkesundhed.au.dk/uddannelse/software): + cIRR.p <- (sa / sb) / (sc / sd) + clnIRR <- log(cIRR.p) + clnIRR.se <- sqrt((1 / sa) + (1 / sc)) + cIRR.se <- exp(clnIRR.se) + pl <- sa / (sa + (sc + 1) * (1 / qf(1 - N., 2 * sa, 2 * sc + 2))) + ph <- (sa + 1) / (sa + 1 + sc / (1 / qf(1 - N., 2 * sc, 2 * sa + 2))) + cIRR.l <- pl * sd / ((1 - pl) * sb) + cIRR.u <- ph * sd / ((1 - ph) * sb) + # clnIRR.l <- clnIRR - (z * clnIRR.se) + # clnIRR.u <- clnIRR + (z * clnIRR.se) + # cIRR.l <- exp(clnIRR.l) + # cIRR.u <- exp(clnIRR.u) + + # Crude odds ratios (Rothman p 139 equation 7-6): + cOR.p <- (sa * sd) / (sb * sc) + clnOR <- log(cOR.p) + clnOR.se <- sqrt(1/sa + 1/sb + 1/sc + 1/sd) + clnOR.l <- clnOR - (z * clnOR.se) + clnOR.u <- clnOR + (z * clnOR.se) + cOR.se <- exp(clnOR.se) + cOR.l <- exp(clnOR.l) + cOR.u <- exp(clnOR.u) + + # Crude attributable risk (Rothman p 135 equation 7-2): + cARisk.p <- ((sa / sN1) - (sc / sN0)) * units + cARisk.se <- (sqrt(((sa * (sN1 - sa))/sN1^3) + ((sc * (sN0 - sc))/sN0^3))) * units + cARisk.l <- cARisk.p - (z * cARisk.se) + cARisk.u <- cARisk.p + (z * cARisk.se) + + # Crude attributable rate (Rothman p 137 equation 7-4): + cARate.p <- ((sa / sb) - (sc / sd)) * units + cARate.se <- (sqrt((sa / sb^2) + (sc / sd^2))) * units + cARate.l <- cARate.p - (z * cARate.se) + cARate.u <- cARate.p + (z * cARate.se) + # Crude attributable fraction for risk data (from Hanley 2001): + cAFRisk.p <- (cRR.p - 1) / cRR.p + cAFRisk.l <- min((cRR.l - 1) / cRR.l, (cRR.u - 1) / cRR.u) + cAFRisk.u <- max((cRR.l - 1) / cRR.l, (cRR.u - 1) / cRR.u) + + # Crude attributable fraction for rate data (from Hanley 2001): + cAFRate.p <- (cIRR.p - 1) / cIRR.p + cAFRate.l <- min((cIRR.l - 1) / cIRR.l, (cIRR.u - 1) / cIRR.u) + cAFRate.u <- max((cIRR.l - 1) / cIRR.l, (cIRR.u - 1) / cIRR.u) + + # Crude estimated attributable fraction (from Hanley 2001): + cAFest.p <- (cOR.p - 1) / cOR.p + cAFest.l <- min((cOR.l - 1) / cOR.l, (cOR.u - 1) / cOR.u) + cAFest.u <- max((cOR.l - 1) / cOR.l, (cOR.u - 1) / cOR.u) + + # Crude population attributable risk (same as Rothman p 135 equation 7-2): + cPARisk.p <- ((sM1 / stotal) - (sc / sN0)) * units + cPARisk.se <- (sqrt(((sM1 * (stotal - sM1))/stotal^3) + ((sc * (sN0 - sc))/sN0^3))) * units + cPARisk.l <- cPARisk.p - (z * cPARisk.se) + cPARisk.u <- cPARisk.p + (z * cPARisk.se) + + # Crude population attributable rate (same as Rothman p 137 equation 7-4): + cPARate.p <- ((sM1 / sM0) - (sc / sd)) * units + cPARate.se <- (sqrt((sM1 / sM0^2) + (sc / sd^2))) * units + cPARate.l <- cPARate.p - (z * cPARate.se) + cPARate.u <- cPARate.p + (z * cPARate.se) + # Crude population attributable fractions for risk data (from Hanley 2001): + # cPAFRisk.p <- ((cRR.p - 1) / cRR.p) * (sa / sM1) + # cPAFRisk.l <- ((cRR.l - 1) / cRR.l) * (sa / sM1) + # cPAFRisk.u <- ((cRR.u - 1) / cRR.u) * (sa / sM1) + + # Crude population attributable fractions for risk data (from OpenEpi TwobyTwo): + # Changed 160609 + cPAFRisk.p <- (cIRiskpop.p - cIRisko.p) / cIRiskpop.p + cPAFRisk.l <- min((cIRiskpop.l - cIRisko.l) / cIRiskpop.l, (cIRiskpop.u - cIRisko.u) / cIRiskpop.u) + cPAFRisk.u <- max((cIRiskpop.l - cIRisko.l) / cIRiskpop.l, (cIRiskpop.u - cIRisko.u) / cIRiskpop.u) + + # Crude population attributable fractions for rate data (from Hanley 2001): + cPAFRate.p <- ((cIRR.p - 1) / cIRR.p) * (sa / sM1) + cPAFRate.l <- ((cIRR.p - 1) / cIRR.p) * (sa / sM1) + cPAFRate.u <- ((cIRR.p - 1) / cIRR.p) * (sa / sM1) + + # Crude population attributable fractions for rate data (from OpenEpi TwobyTwo): + # Changed 160609 + cPAFRate.p <- (cIRatepop.p - cIRateo.p) / cIRatepop.p + cPAFRate.l <- min((cIRatepop.l - cIRateo.l) / cIRatepop.l, (cIRatepop.u - cIRateo.u) / cIRatepop.u) + cPAFRate.u <- max((cIRatepop.l - cIRateo.l) / cIRatepop.l, (cIRatepop.u - cIRateo.u) / cIRatepop.u) + + # Crude estimated population attributable fraction (from Hanley, 2001): + # cPAFest.p <- ((cOR.p - 1) / cOR.p) * (sa / sM1) + # cPAFest.l <- ((cOR.p - 1) / cOR.p) * (sa / sM1) + # cPAFest.u <- ((cOR.p - 1) / cOR.p) * (sa / sM1) + + # Crude estimated population attributable fraction (from OpenEpi TwobyTwo): + # Changed 160609 + cPAFest.p <- (cOpop.p - cOo.p) / cOpop.p + cPAFest.l <- min((cOpop.l - cOo.l) / cOpop.l, (cOpop.u - cOo.u) / cOpop.u) + cPAFest.u <- max((cOpop.l - cOo.l) / cOpop.l, (cOpop.u - cOo.u) / cOpop.u) + + + # =============================== + # CHI-SQUARED TESTS + # =============================== + + # Chi-squared test statistic for individual strata. See Dawson Saunders and Trapp page 151: + exp.a <- (N1 * M1) / total + exp.b <- (N1 * M0) / total + exp.c <- (N0 * M1) / total + exp.d <- (N0 * M0) / total + chi2 <- (((a - exp.a)^2)/ exp.a) + (((b - exp.b)^2)/ exp.b) + (((c - exp.c)^2)/ exp.c) + (((d - exp.d)^2)/ exp.d) + p.chi2 <- 1 - pchisq(chi2, df = 1) + + # Crude summary chi-squared test statistic with 1 degree of freedom: + exp.sa <- (sN1 * sM1) / stotal + exp.sb <- (sN1 * sM0) / stotal + exp.sc <- (sN0 * sM1) / stotal + exp.sd <- (sN0 * sM0) / stotal + chi2s <- (((sa - exp.sa)^2)/ exp.sa) + (((sb - exp.sb)^2)/ exp.sb) + (((sc - exp.sc)^2)/ exp.sc) + (((sd - exp.sd)^2)/ exp.sd) + p.chi2s <- 1 - pchisq(chi2s, df = 1) + + # Mantel-Haenszel X-squared test: + if(length(a) > 1){ + chi2m <- as.numeric(mantelhaen.test(dat)$statistic) + p.chi2m <- as.numeric(mantelhaen.test(dat)$p.value) + } + + # =============================== + # MANTEL-HAENZEL SUMMARY MEASURES + # ================================ + + # Summary incidence risk ratio (Rothman 2002 p 148 and 152, equation 8-2): + sRR.p <- sum((a * N0 / total)) / sum((c * N1 / total)) + varLNRR.s <- sum(((M1 * N1 * N0) / total^2) - ((a * c)/ total)) / + (sum((a * N0)/total) * sum((c * N1)/total)) + lnRR.s <- log(sRR.p) + sRR.se <- (sqrt(varLNRR.s)) + sRR.l <- exp(lnRR.s - (z * sqrt(varLNRR.s))) + sRR.u <- exp(lnRR.s + (z * sqrt(varLNRR.s))) + + # Summary incidence rate ratio (Rothman 2002 p 153, equation 8-5): + sIRR.p <- sum((a * d) / M0) / sum((c * b) / M0) + lnIRR.s <- log(sIRR.p) + varLNIRR.s <- (sum((M1 * b * d) / M0^2)) / (sum((a * d) / M0) * sum((c * b) / M0)) + sIRR.se <- sqrt(varLNIRR.s) + sIRR.l <- exp(lnIRR.s - (z * sqrt(varLNIRR.s))) + sIRR.u <- exp(lnIRR.s + (z * sqrt(varLNIRR.s))) + + # Summary odds ratio (Cord Heuer 211004): + sOR.p <- sum((a * d / total)) / sum((b * c / total)) + G <- a * d / total + H <- b * c / total + P <- (a + d) / total + Q <- (b + c) / total + GQ.HP <- G * Q + H * P + sumG <- sum(G) + sumH <- sum(H) + sumGP <- sum(G * P) + sumGH <- sum(G * H) + sumHQ <- sum(H * Q) + sumGQ <- sum(G * Q) + sumGQ.HP <- sum(GQ.HP) + varLNOR.s <- sumGP / (2 * sumG^2) + sumGQ.HP/(2 * sumGH) + sumHQ/(2 * sumH^2) + lnOR.s <- log(sOR.p) + sOR.se <- sqrt(varLNOR.s) + sOR.l <- exp(lnOR.s - z * sqrt(varLNOR.s)) + sOR.u <- exp(lnOR.s + z * sqrt(varLNOR.s)) + + # Summary attributable risk (Rothman 2002 p 147 and p 152, equation 8-1): + sARisk.p <- (sum(((a * N0) - (c * N1)) / total) / sum((N1 * N0) / total)) * units + w <- (N1 * N0) / total + var.p1 <- (((a * d) / (N1^2 * (N1 - 1))) + ((c * b) / (N0^2 * (N0 - 1)))) + var.p1[N0 == 1] <- 0 + var.p1[N1 == 1] <- 0 + varARisk.s <- sum(w^2 * var.p1) / sum(w)^2 + sARisk.se <- (sqrt(varARisk.s)) * units + sARisk.l <- sARisk.p - (z * sARisk.se) + sARisk.u <- sARisk.p + (z * sARisk.se) + + # Summary attributable rate (Rothman 2002 p 153, equation 8-4): + sARate.p <- sum(((a * d) - (c * b)) / M0) / sum((b * d) / M0) * units + varARate.s <- sum(((b * d) / M0)^2 * ((a / b^2) + (c / d^2 ))) / sum((b * d) / M0)^2 + sARate.se <- sqrt(varARate.s) * units + sARate.l <- sARate.p - (z * sARate.se) + sARate.u <- sARate.p + (z * sARate.se) + + # =============================== + # EFFECT OF CONFOUNDING + # =============================== + # Effect of confounding for risk ratio (Woodward p 172): + RR.conf.p <- (cRR.p/sRR.p) + RR.conf.l <- (cRR.l/sRR.l) + RR.conf.u <- (cRR.u/sRR.u) + + # Effect of confounding for incidence risk ratio (Woodward p 172): + IRR.conf.p <- (cIRR.p/sIRR.p) + IRR.conf.l <- (cIRR.l/sIRR.l) + IRR.conf.u <- (cIRR.u/sIRR.u) + + # Effect of confounding for odds ratio (Woodward p 172): + OR.conf.p <- (cOR.p/sOR.p) + OR.conf.l <- (cOR.l/sOR.l) + OR.conf.u <- (cOR.u/sOR.u) + + # Effect of confounding for attributable risk (Woodward p 172): + ARisk.conf.p <- (cARisk.p/sARisk.p) + ARisk.conf.l <- (cARisk.l/sARisk.l) + ARisk.conf.u <- (cARisk.u/sARisk.u) + + # Effect of confounding for attributable rate (Woodward p 172): + ARate.conf.p <- (cARate.p/sARate.p) + ARate.conf.l <- (cARate.l/sARate.l) + ARate.conf.u <- (cARate.u/sARate.u) + + + # =============================== + # TESTS OF HOMOGENEITY AND EFFECT + # =============================== + + if(length(a) > 1){ + if(homogeneity == "woolf"){ + # Test of homogeneity of risk ratios (Jewell 2004, page 154). First work out the Woolf estimate of the adjusted risk ratio (labelled lnRR.s. here) based on Jewell (2004, page 134): + lnRR. <- log((a / (a + b)) / (c / (c + d))) + lnRR.var. <- (b / (a * (a + b))) + (d / (c * (c + d))) + wRR. <- 1 / lnRR.var. + lnRR.s. <- sum(wRR. * lnRR.) / sum(wRR.) + + # Equation 10.3 from Jewell (2004): + RR.homogeneity <- sum(wRR. * (lnRR. - lnRR.s.)^2) + RR.homogeneity.p <- 1 - pchisq(RR.homogeneity, df = n.strata - 1) + RR.homog <- data.frame(test.statistic = RR.homogeneity, df = n.strata - 1, p.value = RR.homogeneity.p) + + # Test of homogeneity of odds ratios (Jewell 2004, page 154). First work out the Woolf estimate of the adjusted odds ratio (labelled lnOR.s. here) based on Jewell (2004, page 129): + lnOR. <- log(((a + 0.5) * (d + 0.5)) / ((b + 0.5) * (c + 0.5))) + lnOR.var. <- (1 / (a + 0.5)) + (1 / (b + 0.5)) + (1 / (c + 0.5)) + (1 / (d + 0.5)) + wOR. <- 1 / lnOR.var. + lnOR.s. <- sum((wOR. * lnOR.)) / sum(wOR.) + + # Equation 10.3 from Jewell (2004): + OR.homogeneity <- sum(wOR. * (lnOR. - lnOR.s.)^2) + OR.homogeneity.p <- 1 - pchisq(OR.homogeneity, df = n.strata - 1) + OR.homog <- data.frame(test.statistic = OR.homogeneity, df = n.strata - 1, p.value = OR.homogeneity.p) + } + + if(homogeneity == "breslow.day"){ + # Setup calculations. From Jim Robison-Cox, based on Jewell (2004, page 154). + n11k = dat[1,1,] + n21k = dat[2,1,] + n12k = dat[1,2,] + n22k = dat[2,2,] + row1sums = n11k + n12k + row2sums = n21k + n22k + col1sums = n11k + n21k + Amax = apply(cbind(row1sums, col1sums), 1, min) + + # Breslow-Day test of homogeneity of risk ratios. Astar must be no more than col1sums and no more than row1sums: + bb = row2sums + row1sums * sRR.p - col1sums * (1 - sRR.p) + determ = sqrt(bb^2 + 4 * (1 - sRR.p) * sRR.p * row1sums * col1sums) + Astar = (-bb + cbind(-determ, determ)) / (2 - 2 * sRR.p) + Astar = ifelse(Astar[,1] <= Amax & Astar[,1] >= 0, Astar[,1], Astar[,2]) + # print(Astar) + Bstar = row1sums - Astar + Cstar = col1sums - Astar + Dstar = row2sums - col1sums + Astar + Var = apply(1 / cbind(Astar, Bstar, Cstar, Dstar), 1, sum)^(-1) + # print(Var) + RR.homogeneity = sum((dat[1,1,] - Astar)^2 / Var) + RR.homogeneity.p = 1 - pchisq(RR.homogeneity, df = n.strata - 1) + RR.homog <- data.frame(test.statistic = RR.homogeneity, df = n.strata - 1, p.value = RR.homogeneity.p) + + # Breslow-Day test of homogeneity of odds ratios. Astar must be no more than col1sums and no more than row1sums: + bb = row2sums + row1sums * sOR.p - col1sums * (1 - sOR.p) + determ = sqrt(bb^2 + 4 * (1 - sOR.p) * sOR.p * row1sums * col1sums) + Astar = (-bb + cbind(-determ, determ)) / (2 - 2 * sOR.p) + Astar = ifelse(Astar[,1] <= Amax & Astar[,1] >= 0, Astar[,1], Astar[,2]) + # print(Astar) + Bstar = row1sums - Astar + Cstar = col1sums - Astar + Dstar = row2sums - col1sums + Astar + Var = apply(1 / cbind(Astar, Bstar, Cstar, Dstar), 1, sum)^(-1) + # print(Var) + OR.homogeneity = sum((dat[1,1,] - Astar)^2 / Var) + OR.homogeneity.p = 1 - pchisq(OR.homogeneity, df = n.strata - 1) + OR.homog <- data.frame(test.statistic = OR.homogeneity, df = n.strata - 1, p.value = OR.homogeneity.p) + } + } + + # Test of attributable risk homogeneity (see Woodward p 207): + # AR.homogeneity <- sum(AR.p - AR.s)^2 / SE.AR^2 + # Test of effect: + # AR.homogeneity.p <- 1 - pchisq(AR.homogeneity, df = n.strata - 1) + # AR.homog <- data.frame(test.statistic = AR.homogeneity, df = n.strata - 1, p.value = AR.homogeneity.p) + #} + + # =============================== + # RESULTS + # ================================ + + # Incidence risk ratio: + RR.strata <- data.frame(est = RR.p, se = RR.se, weight = RR.w, lower = RR.l, upper = RR.u) + + # Incidence rate ratio: + IRR.strata <- data.frame(est = IRR.p, se = IRR.se, weight = IRR.w, lower = IRR.l, upper = IRR.u) + + # Odds ratio: + OR.strata <- data.frame(est = OR.p, se = OR.se, weight = OR.w, lower = OR.l, upper = OR.u) + + # Corrected incidence risk ratio: + cRR.strata <- data.frame(est = cRR.p, lower = cRR.l, upper = cRR.u) + + # Attributable risk: + ARisk.strata <- data.frame(est = ARisk.p, se = ARisk.se, weight = ARisk.w, lower = ARisk.l, upper = ARisk.u) + + # Attributable rate: + ARate.strata <- data.frame(est = ARate.p, se = ARate.se, lower = ARate.l, upper = ARate.u) + + # Attributable fraction for risk data: + AFRisk.strata <- data.frame(est = AFRisk.p, lower = AFRisk.l, upper = AFRisk.u) + + # Attributable fraction for rate data: + AFRate.strata <- data.frame(est = AFRate.p, lower = AFRate.l, upper = AFRate.u) + + # Estimated attributable fraction: + AFest.strata <- data.frame(est = AFest.p, lower = AFest.l, upper = AFest.u) + + # Population attributable risk: + PARisk.strata <- data.frame(est = PARisk.p, se = PARisk.se, lower = PARisk.l, upper = PARisk.u) + + # Population attributable rate: + PARate.strata <- data.frame(est = PARate.p, se = PARate.se, lower = PARate.l, upper = PARate.u) + + # Population attributable fraction for risk data: + PAFRisk.strata <- data.frame(est = PAFRisk.p, lower = PAFRisk.l, upper = PAFRisk.u) + + # Population attributable fraction for rate data: + PAFRate.strata <- data.frame(est = PAFRate.p, lower = PAFRate.l, upper = PAFRate.u) + + # Estimated population attributable fraction: + PAFest.strata <- data.frame(est = PAFest.p, lower = PAFest.l, upper = PAFest.u) + + # Crude incidence risk ratio: + RR.crude <- data.frame(est = cRR.p, se = cRR.se, lower = cRR.l, upper = cRR.u) + + # Crude incidence rate ratio: + IRR.crude <- data.frame(est = cIRR.p, se = cIRR.se, lower = cIRR.l, upper = cIRR.u) + + # Crude odds ratio: + OR.crude <- data.frame(est = cOR.p, se = cOR.se, lower = cOR.l, upper = cOR.u) + + # Crude attributable risk: + ARisk.crude <- data.frame(est = cARisk.p, se = cARisk.se, lower = cARisk.l, upper = cARisk.u) + + # Crude attributable rate: + ARate.crude <- data.frame(est = cARate.p, se = cARate.se, lower = cARate.l, upper = cARate.u) + + # Crude attributable fraction for risk data: + AFRisk.crude <- data.frame(est = cAFRisk.p, lower = cAFRisk.l, upper = cAFRisk.u) + + # Crude attributable fraction for rate data: + AFRate.crude <- data.frame(est = cAFRate.p, lower = cAFRate.l, upper = cAFRate.u) + + # Crude estimated attributable fraction: + AFest.crude <- data.frame(est = cAFest.p, lower = cAFest.l, upper = cAFest.u) + + # Crude population attributable risk: + PARisk.crude <- data.frame(est = cPARisk.p, se = cPARisk.se, lower = cPARisk.l, upper = cPARisk.u) + + # Crude population attributable rate: + PARate.crude <- data.frame(est = cPARate.p, se = cPARate.se, lower = cPARate.l, upper = cPARate.u) + + # Crude population attributable fraction for risk data: + PAFRisk.crude <- data.frame(est = cPAFRisk.p, lower = cPAFRisk.l, upper = cPAFRisk.u) + + # Crude population attributable fraction for rate data: + PAFRate.crude <- data.frame(est = cPAFRate.p, lower = cPAFRate.l, upper = cPAFRate.u) + + # Crude estimated population attributable fraction: + PAFest.crude <- data.frame(est = cPAFest.p, lower = cPAFest.l, upper = cPAFest.u) + + # Mantel-Haenszel adjusted incidence risk ratio: + RR.mh <- data.frame(est = sRR.p, se = sRR.se, lower = sRR.l, upper = sRR.u) + + # Mantel-Haenszel adjusted incidence rate ratio: + IRR.mh <- data.frame(est = sIRR.p, se = sIRR.se, lower = sIRR.l, upper = sIRR.u) + + # Mantel-Haenszel adjusted odds ratio: + OR.mh <- data.frame(est = sOR.p, se = sOR.se, lower = sOR.l, upper = sOR.u) + + # Mantel-Haenszel adjusted attributable risk: + ARisk.mh <- data.frame(est = sARisk.p, se = sARisk.se, lower = sARisk.l, upper = sARisk.u) + + # Mantel-Haenszel adjusted attributable rate: + ARate.mh <- data.frame(est = sARate.p, se = sARate.se, lower = sARate.l, upper = sARate.u) + + # Effect of confounding for risk ratio (Woodward p 172): + RR.conf <- data.frame(est = RR.conf.p, lower = RR.conf.l, upper = RR.conf.u) + + # Effect of confounding for risk ratio (Woodward p 172): + IRR.conf <- data.frame(est = IRR.conf.p, lower = IRR.conf.l, upper = IRR.conf.u) + + # Effect of confounding for odds ratio (Woodward p 172): + OR.conf <- data.frame(est = OR.conf.p, lower = OR.conf.l, upper = OR.conf.u) + + # Effect of confounding for attributable risk (Woodward p 172): + ARisk.conf <- data.frame(est = ARisk.conf.p, lower = ARisk.conf.l, upper = ARisk.conf.u) + + # Effect of confounding for attributable risk (Woodward p 172): + ARate.conf <- data.frame(est = ARate.conf.p, lower = ARate.conf.l, upper = ARate.conf.u) + + # Chi-squared tests: + chisq.strata <- data.frame(test.statistic = chi2, df = 1, p.value = p.chi2) + chisq.crude <- data.frame(test.statistic = chi2s, df = 1, p.value = p.chi2s) + + if(length(dim(dat)) > 2){ + chisq.mh <- data.frame(test.statistic = chi2m, df = 1, p.value = p.chi2m) + } + + # Labelling for incidence prevalence units: + count.units <- ifelse(units == 1, "Cases per population unit", paste("Cases per ", units, " population units", sep = "")) + time.units <- ifelse(units == 1, "Cases per unit of population time at risk", paste("Cases per ", units, " units of population time at risk", sep = "")) + + # Results for method == "cohort.count": + if(method == "cohort.count" & length(a) == 1 & verbose == TRUE){ + rval <- list( + RR = RR.strata, + OR = OR.strata, + AR = ARisk.strata, + ARp = PARisk.strata, + AFe = AFRisk.strata, + AFp = PAFRisk.strata, + chisq = chisq.strata) + } + + if(method == "cohort.count" & length(a) == 1 & verbose == FALSE){ + # Define tab: + r1 <- c(a, b, N1, cIRiske.p, cOe.p) + r2 <- c(c, d, N0, cIRisko.p, cOo.p) + r3 <- c(M1, M0, M0 + M1, cIRiskpop.p, cOpop.p) + tab <- as.data.frame(rbind(r1, r2, r3)) + colnames(tab) <- c(" Disease +", " Disease -", " Total", " Inc risk *", " Odds") + rownames(tab) <- c("Exposed +", "Exposed -", "Total") + tab <- format.data.frame(tab, digits = 3, justify = "right") + + print(tab) + cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") + cat("\n---------------------------------------------------------") + cat("\nInc risk ratio ", round(cRR.p, digits = 2), paste("(", round(cRR.l, digits = 2), ", ", round(cRR.u, digits = 2), ")", sep = "")) + cat("\nOdds ratio ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) + cat("\nAttrib risk * ", round(ARisk.p, digits = 2), paste("(", round(ARisk.l, digits = 2), ", ", round(ARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib risk in population * ", round(PARisk.p, digits = 2), paste("(", round(PARisk.l, digits = 2), ", ", round(PARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib fraction in exposed (%) ", round(AFRisk.p * 100, digits = 2), paste("(", round(AFRisk.l * 100, digits = 2), ", ", round(AFRisk.u * 100, digits = 2), ")", sep = "")) + cat("\nAttrib fraction in population (%) ", round(PAFRisk.p * 100, digits = 2), paste("(", round(PAFRisk.l * 100, digits = 2), ", ", round(PAFRisk.u * 100, digits = 2), ")", sep = "")) + cat("\n---------------------------------------------------------") + cat("\n", "*", count.units, "\n") + } + + if(method == "cohort.count" & length(a) > 1 & verbose == TRUE){ + rval <- list( + RR.strata = RR.strata, + RR.crude = RR.crude, + RR.mh = RR.mh, + + OR.strata = OR.strata, + OR.crude = OR.crude, + OR.mh = OR.mh, + + AR.strata = ARisk.strata, + AR.crude = ARisk.crude, + AR.mh = ARisk.mh, + + ARp.strata = PARisk.strata, + AFe.strata = AFRisk.strata, + AFp.strata = PAFRisk.strata, + + chisq.strata = chisq.strata, + chisq.crude = chisq.crude, + chisq.mh = chisq.mh, + + RR.homog = RR.homog, + OR.homog = OR.homog) + } + + if(method == "cohort.count" & length(a) > 1 & verbose == FALSE){ + # Define tab: + r1 <- c(sa, sb, sN1, cIRiske.p, cOe.p) + r2 <- c(sc, sd, sN0, cIRisko.p, cOo.p) + r3 <- c(sM1, sM0, sM0 + sM1, cIRiskpop.p, cOpop.p) + tab <- as.data.frame(rbind(r1, r2, r3)) + colnames(tab) <- c(" Disease +", " Disease -", " Total", " Inc risk *", " Odds") + rownames(tab) <- c("Exposed +", "Exposed -", "Total") + tab <- format.data.frame(tab, digits = 3, justify = "right") + print(tab) + + cat("\n") + cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") + cat("\n---------------------------------------------------------") + cat("\nInc risk ratio (crude) ", round(cRR.p, digits = 2), paste("(", round(cRR.l, digits = 2), ", ", round(cRR.u, digits = 2), ")", sep = "")) + cat("\nInc risk ratio (M-H) ", round(sRR.p, digits = 2), paste("(", round(sRR.l, digits = 2), ", ", round(sRR.u, digits = 2), ")", sep = "")) + cat("\nInc risk ratio (crude:M-H) ", round(RR.conf.p, digits = 2)) + cat("\nOdds ratio (crude) ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) + cat("\nOdds ratio (M-H) ", round(sOR.p, digits = 2), paste("(", round(sOR.l, digits = 2), ", ", round(sOR.u, digits = 2), ")", sep = "")) + cat("\nOdds ratio (crude:M-H) ", round(OR.conf.p, digits = 2)) + cat("\nAttrib risk (crude) * ", round(cARisk.p, digits = 2), paste("(", round(cARisk.l, digits = 2), ", ", round(cARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib risk (M-H) * ", round(sARisk.p, digits = 2), paste("(", round(sARisk.l, digits = 2), ", ", round(sARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib risk (crude:M-H) ", round(ARisk.conf.p, digits = 2)) + cat("\n---------------------------------------------------------") + cat("\n", "*", count.units, "\n") + } + + # Results for method == "cohort.time": + if(method == "cohort.time" & length(a) == 1 & verbose == TRUE){ + rval <- list( + IRR = IRR.strata, + AR = ARate.strata, + ARp = PARate.strata, + AFe = AFRate.strata, + AFp = PAFRate.strata, + chisq = chisq.strata) + } + + if(method == "cohort.time" & length(a) == 1 & verbose == FALSE){ + # Define tab: + r1 <- c(a, b, cIRatee.p) + r2 <- c(c, d, cIRateo.p) + r3 <- c(M1, M0, cIRatepop.p) + tab <- as.data.frame(rbind(r1, r2, r3)) + colnames(tab) <- c(" Disease +", " Time at risk", " Inc rate *") + rownames(tab) <- c("Exposed +", "Exposed -", "Total") + tab <- format.data.frame(tab, digits = 3, justify = "right") + print(tab) + cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") + cat("\n---------------------------------------------------------") + cat("\nInc rate ratio ", round(cIRR.p, digits = 2), paste("(", round(cIRR.l, digits = 2), ", ", round(cIRR.u, digits = 2), ")", sep = "")) + cat("\nAttrib rate * ", round(ARate.p, digits = 2), paste("(", round(ARate.l, digits = 2), ", ", round(ARate.u, digits = 2), ")", sep = "")) + cat("\nAttrib rate in population * ", round(PARate.p, digits = 2), paste("(", round(PARate.l, digits = 2), ", ", round(PARate.u, digits = 2), ")", sep = "")) + cat("\nAttrib fraction in exposed (%) ", round(AFRate.p * 100, digits = 2), paste("(", round(AFRate.l * 100, digits = 2), ", ", round(AFRate.u * 100, digits = 2), ")", sep = "")) + cat("\nAttrib fraction in population (%) ", round(PAFRate.p * 100, digits = 2), paste("(", round(PAFRate.l * 100, digits = 2), ", ", round(PAFRate.u * 100, digits = 2), ")", sep = "")) + cat("\n---------------------------------------------------------") + cat("\n", "*", time.units, "\n") + } + + if(method == "cohort.time" & length(a) > 1 & verbose == TRUE){ + rval <- list( + IRR.strata = IRR.strata, + IRR.crude = IRR.crude, + IRR.mh = IRR.mh, + + AR.strata = ARate.strata, + AR.crude = ARate.crude, + AR.mh = ARate.mh, + + ARp.strata = PARate.strata, + AFp.strata = PAFRate.strata, + + chisq.strata = chisq.strata, + chisq.crude = chisq.crude, + chisq.mh = chisq.mh) + # RR.homog = RR.homog, + # OR.homog = OR.homog) + } + + if(method == "cohort.time" & length(a) > 1 & verbose == FALSE){ + # Define tab: + r1 <- c(sa, sb, cIRatee.p) + r2 <- c(sc, sd, cIRateo.p) + r3 <- c(sM1, sM0, cIRatepop.p) + tab <- as.data.frame(rbind(r1, r2, r3)) + colnames(tab) <- c(" Disease +", " Time at risk", " Inc rate *") + rownames(tab) <- c("Exposed +", "Exposed -", "Total") + tab <- format.data.frame(tab, digits = 3, justify = "right") + print(tab) + cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") + cat("\n---------------------------------------------------------") + cat("\nInc rate ratio (crude) ", round(cIRR.p, digits = 2), paste("(", round(cIRR.l, digits = 2), ", ", round(cIRR.u, digits = 2), ")", sep = "")) + cat("\nInc rate ratio (M-H) ", round(sIRR.p, digits = 2), paste("(", round(sIRR.l, digits = 2), ", ", round(sIRR.u, digits = 2), ")", sep = "")) + cat("\nInc rate ratio (crude:M-H) ", round(IRR.conf.p, digits = 2)) + cat("\nAttrib rate (crude) * ", round(cARate.p, digits = 2), paste("(", round(cARate.l, digits = 2), ", ", round(cARate.u, digits = 2), ")", sep = "")) + cat("\nAttrib rate (M-H) * ", round(sARate.p, digits = 2), paste("(", round(sARate.l, digits = 2), ", ", round(sARate.u, digits = 2), ")", sep = "")) + cat("\nAttrib rate (crude:M-H) ", round(ARate.conf.p, digits = 2)) + cat("\n---------------------------------------------------------") + cat("\n", "*", time.units, "\n") + } + + # Results for method == "case.control": + if(method == "case.control" & length(a) == 1 & verbose == TRUE){ + rval <- list( + OR = OR.strata, + AR = ARisk.strata, + ARp = PARisk.strata, + + AFest = AFest.strata, + AFp = PAFest.strata, + chisq = chisq.strata) + } + + if(method == "case.control" & length(a) == 1 & verbose == FALSE){ + # Define tab: + r1 <- c(a, b, N1, cIRiske.p, cOe.p) + r2 <- c(c, d, N0, cIRisko.p, cOo.p) + r3 <- c(M1, M0, M0 + M1, cIRiskpop.p, cOpop.p) + tab <- as.data.frame(rbind(r1, r2, r3)) + colnames(tab) <- c(" Disease +", " Disease -", " Total", " Prevalence *", " Odds") + rownames(tab) <- c("Exposed +", "Exposed -", "Total") + tab <- format.data.frame(tab, digits = 3, justify = "right") + + print(tab) + cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") + cat("\n---------------------------------------------------------") + cat("\nOdds ratio ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) + cat("\nAttrib prevalence * ", round(ARisk.p, digits = 2), paste("(", round(ARisk.l, digits = 2), ", ", round(ARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib prevalence in population * ", round(PARisk.p, digits = 2), paste("(", round(PARisk.l, digits = 2), ", ", round(PARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib fraction (est) in exposed (%) ", round(AFest.p * 100, digits = 2), paste("(", round(AFest.l * 100, digits = 2), ", ", round(AFest.u * 100, digits = 2), ")", sep = "")) + cat("\nAttrib fraction (est) in population (%) ", round(PAFest.p * 100, digits = 2), paste("(", round(PAFest.l * 100, digits = 2), ", ", round(PAFest.u * 100, digits = 2), ")", sep = "")) + cat("\n---------------------------------------------------------") + cat("\n", "*", count.units, "\n") + } + + if(method == "case.control" & length(a) > 1 & verbose == TRUE){ + rval <- list( + OR.strata = OR.strata, + OR.crude = OR.crude, + OR.mh = OR.mh, + + AR.strata = ARisk.strata, + AR.crude = ARisk.crude, + AR.mh = ARisk.mh, + + ARp.strata = PARisk.strata, + AFest.strata = AFest.strata, + AFpest.strata = PAFest.strata, + + chisq.strata = chisq.strata, + chisq.crude = chisq.crude, + chisq.mh = chisq.mh, + OR.homog = OR.homog) + } + + if(method == "case.control" & length(a) > 1 & verbose == FALSE){ + # Define tab: + r1 <- c(sa, sb, sN1, cIRiske.p, cOe.p) + r2 <- c(sc, sd, sN0, cIRisko.p, cOo.p) + r3 <- c(sM1, sM0, sM0 + sM1, cIRiskpop.p, cOpop.p) + tab <- as.data.frame(rbind(r1, r2, r3)) + colnames(tab) <- c(" Disease +", " Disease -", " Total", " Prevalence *", " Odds") + rownames(tab) <- c("Exposed +", "Exposed -", "Total") + tab <- format.data.frame(tab, digits = 3, justify = "right") + print(tab) + + cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") + cat("\n---------------------------------------------------------") + cat("\nOdds ratio (crude) ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) + cat("\nOdds ratio (M-H) ", round(sOR.p, digits = 2), paste("(", round(sOR.l, digits = 2), ", ", round(sOR.u, digits = 2), ")", sep = "")) + cat("\nOdds ratio (crude:M-H) ", round(OR.conf.p, digits = 2)) + cat("\nAttrib prevalence (crude) * ", round(cARisk.p, digits = 2), paste("(", round(cARisk.l, digits = 2), ", ", round(cARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib prevalence (M-H) * ", round(sARisk.p, digits = 2), paste("(", round(sARisk.l, digits = 2), ", ", round(sARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib prevalence (crude:M-H) ", round(ARate.conf.p, digits = 2)) + cat("\n---------------------------------------------------------") + cat("\n", "*", count.units, "\n") + } + + # Results for method == "cross.sectional": + if(method == "cross.sectional" & length(a) == 1 & verbose == TRUE){ + rval <- list( + RR = RR.strata, + OR = OR.strata, + AR = ARisk.strata, + ARp = PARisk.strata, + AFe = AFRisk.strata, + AFp = PAFRisk.strata, + chisq = chisq.strata) + } + + if(method == "cross.sectional" & length(a) == 1 & verbose == FALSE){ + # Define tab: + r1 <- c(a, b, N1, cIRiske.p, cOe.p) + r2 <- c(c, d, N0, cIRisko.p, cOo.p) + r3 <- c(M1, M0, M0 + M1, cIRiskpop.p, cOpop.p) + tab <- as.data.frame(rbind(r1, r2, r3)) + colnames(tab) <- c(" Disease +", " Disease -", " Total", " Prevalence *", " Odds") + rownames(tab) <- c("Exposed +", "Exposed -", "Total") + tab <- format.data.frame(tab, digits = 3, justify = "right") + + print(tab) + cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") + cat("\n---------------------------------------------------------") + cat("\nPrevalence ratio ", round(cRR.p, digits = 2), paste("(", round(cRR.l, digits = 2), ", ", round(cRR.u, digits = 2), ")", sep = "")) + cat("\nOdds ratio ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) + cat("\nAttrib prevalence * ", round(ARisk.p, digits = 2), paste("(", round(ARisk.l, digits = 2), ", ", round(ARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib prevalence in population * ", round(PARisk.p, digits = 2), paste("(", round(PARisk.l, digits = 2), ", ", round(PARisk.u, digits = 2), ")", sep = "")) + cat("\nAttrib fraction in exposed (%) ", round(AFRisk.p * 100, digits = 2), paste("(", round(AFRisk.l * 100, digits = 2), ", ", round(AFRisk.u * 100, digits = 2), ")", sep = "")) + cat("\nAttrib fraction in population (%) ", round(PAFRisk.p * 100, digits = 2), paste("(", round(PAFRisk.l * 100, digits = 2), ", ", round(PAFRisk.u * 100, digits = 2), ")", sep = "")) + cat("\n---------------------------------------------------------") + cat("\n", "*", count.units, "\n") + } + + if(method == "cross.sectional" & length(a) > 1 & verbose == TRUE){ + rval <- list( + RR.strata = RR.strata, + RR.crude = RR.crude, + RR.mh = RR.mh, + + OR.strata = OR.strata, + OR.crude = OR.crude, + OR.mh = OR.mh, + + AR.strata = ARisk.strata, + AR.crude = ARisk.crude, + AR.mh = ARisk.mh, + ARp.strata = PARisk.strata, + + AFe.strata = AFRisk.strata, + AFp.strata = PAFRisk.strata, + + chisq.strata = chisq.strata, + chisq.crude = chisq.crude, + chisq.mh = chisq.mh, + + RR.homog = RR.homog, + OR.homog = OR.homog) + } + + else if(method == "cross.sectional" & length(a) > 1 & verbose == FALSE){ + # Define tab: + r1 <- c(sa, sb, sN1, cIRiske.p, cOe.p) + r2 <- c(sc, sd, sN0, cIRisko.p, cOo.p) + r3 <- c(sM1, sM0, sM1 + sM0, cIRiskpop.p, cOpop.p) + tab <- as.data.frame(rbind(r1, r2, r3)) + colnames(tab) <- c(" Disease +", " Disease -", " Total", " Prevalence *", " Odds") + rownames(tab) <- c("Exposed +", "Exposed -", "Total") + tab <- format.data.frame(tab, digits = 3, justify = "right") + print(tab) + + cat("\nPoint estimates and", conf.level * 100, "%", "CIs:") + cat("\n---------------------------------------------------------") + cat("\nPrevalence ratio (crude) ", round(cRR.p, digits = 2), paste("(", round(cRR.l, digits = 2), ", ", round(cRR.u, digits = 2), ")", sep = "")) + cat("\nPrevalence ratio (M-H) ", round(sRR.p, digits = 2), paste("(", round(sRR.l, digits = 2), ", ", round(sRR.u, digits = 2), ")", sep = "")) + cat("\nPrevalence ratio (crude:M-H) ", round(RR.conf.p, digits = 2)) + cat("\nOdds ratio (crude) ", round(cOR.p, digits = 2), paste("(", round(cOR.l, digits = 2), ", ", round(cOR.u, digits = 2), ")", sep = "")) + cat("\nOdds ratio (M-H) ", round(sOR.p, digits = 2), paste("(", round(sOR.l, digits = 2), ", ", round(sOR.u, digits = 2), ")", sep = "")) + cat("\nOdds ratio (crude:M-H) ", round(OR.conf.p, digits = 2)) + cat("\nAtributable prevalence (crude) * ", round(cARisk.p, digits = 2), paste("(", round(cARisk.l, digits = 2), ", ", round(cARisk.u, digits = 2), ")", sep = "")) + cat("\nAtributable prevalence (M-H) * ", round(sARisk.p, digits = 2), paste("(", round(sARisk.l, digits = 2), ", ", round(sARisk.u, digits = 2), ")", sep = "")) + cat("\nAtributable prevalence (crude:M-H) ", round(ARisk.conf.p, digits = 2)) + cat("\n---------------------------------------------------------") + cat("\n", "*", count.units, "\n") + } +if(verbose == TRUE){ + return(rval) + } +} diff -Nru r-cran-epir-0.9-32/R/epi.about.R r-cran-epir-0.9-38/R/epi.about.R --- r-cran-epir-0.9-32/R/epi.about.R 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/R/epi.about.R 2012-02-01 20:46:16.000000000 +0000 @@ -2,8 +2,12 @@ { cat("\n") cat("-----------------------------------------------------------\n") - ver <- package.description("epi", lib = NULL, field="Version") - cat(paste("epi version", ver, "is now loaded")) + ver <- packageDescription("epiR", lib.loc = NULL, fields = "Version") + cat(paste("epiR version", ver)) + cat("\n") + cat("An R package for the analysis of epidemiological data.") + cat("\n") + cat("See http://epicentre.massey.ac.nz/ for details.") cat("\n") cat("-----------------------------------------------------------\n") invisible() diff -Nru r-cran-epir-0.9-32/R/epi.conf.R r-cran-epir-0.9-38/R/epi.conf.R --- r-cran-epir-0.9-32/R/epi.conf.R 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/R/epi.conf.R 2012-02-01 20:46:16.000000000 +0000 @@ -77,8 +77,8 @@ diff <- as.vector(dat[,2] - dat[,1]) n <- length(dat[,1]) mean.diff <- mean(diff) - sd.diff <- sqrt(var(diff)) - se.diff <- mean.diff/sqrt(n) + sd.diff <- sd(diff) + se.diff <- sd.diff / sqrt(n) P <- (1 - conf.level)/2 t <- abs(qt(P, (n - 1))) @@ -87,7 +87,7 @@ up <- mean.diff + (t * se.diff) rval <- as.data.frame(cbind(mean.diff, se.diff, low, up)) names(rval) <- c("est", "se", "lower", "upper") - rval + rval } if(ctype == "prop.single"){ diff -Nru r-cran-epir-0.9-32/R/epi.kappa.R r-cran-epir-0.9-38/R/epi.kappa.R --- r-cran-epir-0.9-32/R/epi.kappa.R 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/R/epi.kappa.R 2012-02-01 20:46:16.000000000 +0000 @@ -1,34 +1,81 @@ -"epi.kappa" <- function(a, b, c, d, conf.level = 0.95) - { - N. <- 1 - ((1 - conf.level) / 2) - z <- qnorm(N., mean = 0, sd = 1) - lower <- "lower" - upper <- "upper" - - # a: actual + predicted + - # b: actual + predicted - - # c: actual - predicted + - # d: actual - predicted - - - n <- a + b + c + d - pO <- (a + d)/n - pE.pos <- ((a + b)*(a + c))/n^2 - pE.neg <- ((c + d)*(b + d))/n^2 - pE <- pE.pos + pE.neg - kappa <- (pO - pE) / (1 - pE) +"epi.kappa" <- function(dat, method = "fleiss", alternative = c("two.sided", "less", "greater"), conf.level = 0.95){ + N. <- 1 - ((1 - conf.level) / 2) + z <- qnorm(N., mean = 0, sd = 1) + lower <- "lower" + upper <- "upper" + + n <- sum(dat) + +if(method == "fleiss"){ + # Turn cell frequencies into proportions: x + dat <- dat / n + + # Overall proportion of observed agreement, pO + pO <- sum(diag(dat)) + + # Overall proportion of chance-expected agreement, pE + r.totals <- apply(dat, MARGIN = 1, FUN = sum) + c.totals <- apply(dat, MARGIN = 2, FUN = sum) + pE <- sum(r.totals * c.totals) + + # Overall kappa (Equation 18.12 in Fleiss): + kappa <- (pO - pE) / (1 - pE) + + # Standard error of kappa (Equation 18.13 in Fleiss): + tmp.1 <- 1 / ((1 - pE) * sqrt(n)) + tmp.2 <- sqrt(pE + pE^2 - sum((r.totals * c.totals) * (r.totals + c.totals))) + se.kappa <- tmp.1 * tmp.2 + kappa.low <- kappa - (z * se.kappa) + kappa.up <- kappa + (z * se.kappa) + + # Test of effect (Equation 18.14 in Fleiss). Code for p-value taken from z.test function in TeachingDemos package: + effect.z <- kappa / se.kappa + alternative <- match.arg(alternative) + p.effect <- switch(alternative, two.sided = 2 * pnorm(abs(effect.z), lower.tail = FALSE), less = pnorm(effect.z), greater = pnorm(effect.z, lower.tail = FALSE)) + + # Results: + kappa <- data.frame(est = kappa, se = se.kappa, lower = kappa.low, upper = kappa.up) + z <- data.frame(test.statistic = effect.z, p.value = p.effect) + rval <- list(kappa = kappa, z = z) + return(rval) + } + +if(method == "altman"){ + + # Overall proportion of observed agreement, pO + n <- sum(dat) + pO <- sum(diag(dat)) / n + + # Overall proportion of chance-expected agreement, pE + r.totals <- apply(dat, MARGIN = 1, FUN = sum) + c.totals <- apply(dat, MARGIN = 2, FUN = sum) + pE <- sum(r.totals * c.totals) / n^2 + + kappa <- (pO - pE) / (1 - pE) + + se.kappa <- sqrt((pO * (1 - pO)) / (n * (1 - pE)^2)) + kappa.low <- kappa - (z * se.kappa) + kappa.up <- kappa + (z * se.kappa) + + # Test of effect. Code for p-value taken from z.test function in TeachingDemos package: + effect.z <- kappa / se.kappa + alternative <- match.arg(alternative) + p.effect <- switch(alternative, two.sided = 2 * pnorm(abs(effect.z), lower.tail = FALSE), less = pnorm(effect.z), greater = pnorm(effect.z, lower.tail = FALSE)) - se.kappa <- sqrt( (pO*(1 - pO)) / (n*(1 - pE)^2)) - kappa.low <- kappa - (z * se.kappa) - kappa.up <- kappa + (z * se.kappa) - - # McNemar's test (Dohoo, Martin, Stryhn): - mcnemar <- (b - c)^2 / (b + c) - p.chi2 <- 1 - pchisq(mcnemar, df = 1) + # Results: + kappa <- data.frame(est = kappa, se = se.kappa, lower = kappa.low, upper = kappa.up) + z <- data.frame(test.statistic = effect.z, p.value = p.effect) + rval <- list(kappa = kappa, z = z) + return(rval) + + # McNemar's test (Dohoo, Martin, Stryhn): + # mcnemar <- (b - c)^2 / (b + c) + # p.chi2 <- 1 - pchisq(mcnemar, df = 1) - # Results: - kappa <- as.data.frame(cbind(kappa, kappa.low, kappa.up)) - names(kappa) <- c("est", lower, upper) - mcnemar <- as.data.frame(cbind(test.statistic = mcnemar, df = 1, p.value = p.chi2)) - rval <- list(kappa = kappa, mcnemar = mcnemar) - return(rval) + # Results: + # kappa <- data.frame(est = kappa, se = se.kappa, lower = kappa.low, upper = kappa.up) + # mcnemar <- data.frame(test.statistic = mcnemar, df = 1, p.value = p.chi2) + # rval <- list(kappa = kappa, mcnemar = mcnemar) + # return(rval) + } } diff -Nru r-cran-epir-0.9-32/R/epi.prev.R r-cran-epir-0.9-38/R/epi.prev.R --- r-cran-epir-0.9-32/R/epi.prev.R 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/R/epi.prev.R 2012-02-01 20:46:16.000000000 +0000 @@ -1,62 +1,257 @@ -"epi.prev" <- function(pos, tested, se, sp, conf.level = 0.95) - { - # Exact binomial confidence limits from D. Collett (1999) Modelling binary data. Chapman & Hall/CRC, Boca Raton Florida, p. 24. - .funincrisk <- function(dat, conf.level){ - N. <- 1 - ((1 - conf.level) / 2) - a <- dat[,1] - n <- dat[,2] - b <- n - a - p <- a / n - - a. <- ifelse(a == 0, a + 1, a); b. <- ifelse(b == 0, b + 1, b) - low <- a. /(a. + (b. + 1) * (1 / qf(1 - N., 2 * a., 2 * b. + 2))) - up <- (a. + 1) / (a. + 1 + b. / (1 / qf(1 - N., 2 * b., 2 * a. + 2))) - low <- ifelse(a == 0, 0, low) - up <- ifelse(a == n, 1, up) - rval <- as.data.frame(cbind(p, low, up)) - names(rval) <- c("est", "lower", "upper") - rval - } - - - N. <- 1 - ((1 - conf.level) / 2) - z <- qnorm(N., mean = 0, sd = 1) +"epi.prev" <- function(pos, tested, se, sp, method = "wilson", conf.level = 0.95){ - r <- pos; n <- tested - tdat <- as.matrix(cbind(r, n)) - q <- 1 - r/n - trval <- .funincrisk(tdat, conf.level) - ap.p <- trval$est; ap.lo <- trval$lower; ap.up <- trval$upper - ap.se <- sqrt((ap.p * q) / n) - - # Aap <- (2 * r) + (z * z) - # Bap <- z * sqrt((z * z) + (4 * r * q)) - # Cap <- 2 * (n + (z * z)) - # ap.lo <- (Aap - Bap) / Cap - # ap.up <- (Aap + Bap) / Cap - - # Rogan and Gladen (1978) estimate of true prevalence. - # Don't correct TP estimates of less than 0 or greater than 1. - tp.p <- (ap.p + sp - 1) / (se + sp - 1) - # tp.p <- ifelse(tp.p > 0, tp.p, 0) - # tp.p <- ifelse(tp.p < 1, tp.p, 1) - - # From Locksley et al. (2008) assuming Se and Sp are known with certainty: - J <- (se + sp) - 1 - tp.se <- ((ap.p * (1 - ap.p)) / (n * J^2))^0.5 - tp.lo <- (tp.p - (z * tp.se)) - # tp.lo <- ifelse(tp.lo > 0, tp.lo, 0) - # tp.lo <- ifelse(tp.lo < 1, tp.lo, 1) - tp.up <- (tp.p + (z * tp.se)) - # tp.up <- ifelse(tp.up > 0, tp.up, 0) - # tp.up <- ifelse(tp.up < 1, tp.up, 1) - - result.01 <- as.data.frame(cbind(ap.p, ap.se, ap.lo, ap.up)) - names(result.01) <- c("est", "se", "lower", "upper") + # Apparent prevalence: + ap.p <- pos / tested + if(method == "c-p") ap.cl = .bin.conf(pos, tested, method = "e", alpha = 1 - conf.level)[2:3] + else if (method == "sterne") ap.cl = .sterne.int(pos, tested, alpha = 1 - conf.level) + else if (method == "blaker") ap.cl = .blakerci(pos, tested, conf.level) + else if (method == "wilson") ap.cl = .bin.conf(pos, tested, method = "w", alpha = 1 - conf.level)[2:3] + else stop('Valid methods are "c-p", "sterne", "blaker", or "wilson"') + + # True prevalence: + if(method == "c-p") tp.cl = .bin.conf(pos, tested, method = "e", alpha = 1 - conf.level)[2:3] + else if (method == "sterne") tp.cl = .sterne.int(pos, tested, alpha = 1 - conf.level) + else if (method == "blaker") tp.cl = .blakerci(pos, tested, conf.level) + else if (method == "wilson") tp.cl = .bin.conf(pos, tested, method = "w", alpha = 1 - conf.level)[2:3] + else stop('Valid methods are "c-p", "sterne", "blaker", or "wilson"') - result.02 <- as.data.frame(cbind(tp.p, tp.se, tp.lo, tp.up)) - names(result.02) <- c("est", "se", "lower", "upper") + tp.p <- (ap.p + sp - 1) / (se + sp - 1) + tp.p[tp.p < 0] <- 0 + tp.p[tp.p > 1] <- 1 + adj.cl <- (tp.cl + sp - 1) / (se + sp - 1) + adj.cl <- pmax(adj.cl, c(0, 0)) + adj.cl <- pmin(adj.cl, c(1, 1)) + + result.01 <- as.data.frame(cbind(ap.p, ap.cl[1], ap.cl[2])) + names(result.01) <- NULL + names(result.01) <- c("est", "lower", "upper") + + result.02 <- as.data.frame(cbind(tp.p, adj.cl[1], adj.cl[2])) + names(result.02) <- NULL + names(result.02) <- c("est", "lower", "upper") - rval <- list(ap = result.01, tp = result.02) - return(rval) + rval <- list(ap = result.01, tp = result.02) + return(rval) +} + +library(Hmisc) + +# ----------------------------------- +# Blaker's interval (by Helge Blaker). Computes the Blaker exact CI (Canadian J. Stat 2000) for a binomial success probability for x successes out of n trials with confidence coefficient = conf.level. +# uses acceptbin function. + +.blakerci <- function(x, n, conf.level, tolerance = 1e-04){ + lower = 0 + upper = 1 + if (x != 0){lower = qbeta((1 - conf.level) / 2, x, n - x + 1) + while (.acceptbin(x, n, lower + tolerance) < (1 - conf.level)) + lower = lower + tolerance } + if (x != n){upper = qbeta(1 - (1 - conf.level) / 2, x + 1, n - x) + while (.acceptbin(x, n, upper - tolerance) < (1 - conf.level)) + upper = upper - tolerance + } + c(lower, upper) + } + + +.acceptbin = function(x, n, p){ + # Computes the Blaker acceptability of p when x is observed and X is bin(n, p) + p1 = 1 - pbinom(x - 1, n, p) + p2 = pbinom(x, n, p) + a1 = p1 + pbinom(qbinom(p1, n, p) - 1, n, p) + a2 = p2 + 1 - pbinom(qbinom(1 - p2, n, p), n, p) + return(min(a1,a2)) +} + +# ----------------------------------- +# Exact confidence intervals +# ----------------------------------- + +.sterne.int <- function(x, n, alpha = 0.05, del = 10^-5){ + logit <- function(p){log(p / (1 - p))} + invlogit <- function(y){exp(y) / (1 + exp(y))} + theta <- function(k, x, n){(lchoose(n, x) - lchoose(n, k)) / (k - x)} + Feta <- function(x, eta){pbinom(x, n, invlogit(eta))} + +# The function piXeta(x, eta) automatically accounts for the fact that if k_alpha(X) = min(J) then a_alpha^st(X) = a_alpha(X) +.piXeta <- function(x, eta){ + if (invlogit(eta) >= 1){f <- 0} else { + J <- c(0:(x - 1),(x + 1):n) + + # on (-infinity, theta_0] + t1 <- theta(0, x, n) + if (is.na(t1) != 1 && eta <= t1){f <- 1 - Feta(x - 1, eta)} + + # on [theta_0,mode] + k1 <- J[J < (x - 1)] + + if (length(k1) > 0){ + the1 <- theta(k1, x, n) + the2 <- theta(k1 + 1, x, n) + pos <- (the1 <= eta) * (eta < the2) + if (sum(pos) > 0){f <- 1 - Feta(x - 1, eta) + Feta(max(k1 * pos), eta)} + } + + # mode + the1 <- theta(x - 1, x, n) + the2 <- theta(x + 1, x, n) + if (eta >= the1 && eta <= the2){f <- 1} + } + + # on [mode,theta_n] + k2 <- J[J > (x + 1)] + if (length(k2) > 0){ + the1 <- theta(k2 - 1, x, n) + the2 <- theta(k2, x, n) + kre <- sum(k2 * (the1 < eta) * (eta <= the2)) + if (kre > 0){ + f <- 1 - Feta(kre - 1, eta) + Feta(x, eta)} + } + + # on [theta_n,infty) + t2 <- theta(n, x, n) + if (is.na(t2) != 1 && eta >= t2){f <- Feta(x, eta)} + f} + + # Lower bound a_alpha^st(X) + if (x ==0 ){pu <- 0} else { + J <- c(0:(x - 1), (x + 1):n) + k1 <- min(J) + pi1 <- .piXeta(x, theta(k1, x, n)) + + # Calculation of k_alpha(X) + if (pi1 >= alpha){kal <- k1} else { + k <- x-1 + while (k1 < k - 1){ + k2 <- floor((k + k1) / 2) + pi2 <- .piXeta(x, theta(k2, x, n)) + if (pi2 >= alpha){k <- k2} + else {k1 <- k2} + } + kal <- k + } + + # Calculation of a_alpha^st(X): + b1 <- theta(kal, x, n) + pi1 <- 1 - Feta(x - 1, b1) + Feta(kal - 1, b1) + if (pi1 <= alpha){b <- b1} else { + b <- max(theta(kal - 1, x, n),logit(del)) + pi <- 1 - Feta(x - 1, b) + Feta(kal - 1, b) + while (b1 - b > del || pi1 - pi > del){ + b2 <- (b + b1) / 2 + pi2 <- 1 - Feta(x - 1, b2) + Feta(kal - 1, b2) + if (pi2 > alpha){ + b1 <- b2 + pi1 <- pi2} else { + b <- b2 + pi <- pi2}}} + pu <- invlogit(b)} + + # Upper bound b_alpha^st(X): + if (x == n){po <- 1} else { + J <- c(0:(x - 1),(x + 1):n) + k1 <- max(J) + pi1 <- .piXeta(x, theta(k1, x, n)) + + # Calculation of k_alpha(X): + if (pi1 >= alpha){kau <- k1} else { + k <- x + 1 + pi <- 1 + while (k1 > k + 1){ + k2 <- floor((k + k1) / 2) + pi2 <- .piXeta(x, theta(k2, x, n)) + if (pi2 >= alpha){k <- k2} + else {k1 <- k2} + } + kau <- k + } + + # Calculation of b_alpha^st(X): + b1 <- theta(kau, x, n) + pi1 <- 1 - Feta(kau, b1) + Feta(x, b1) + + if (pi1 <= alpha){ + b <- b1 + po <- pi1} else { + b <- min(theta(kau + 1, x, n), b1 + n) + pi <- 1 - Feta(kau, b) + Feta(x, b) + while (b - b1 > del || pi1 - pi > del){ + b2 <- (b + b1) / 2 + pi2 <- 1 - Feta(kau, b2) + Feta(x, b2) + if (pi2 > alpha){ + b1 <- b2 + pi1 <- pi2} else { + b <- b2 + pi <- pi2}}} + po <- invlogit(b)} + + # c("a_alpha^St" = pu, "b_alpha^St" = po) + c(pu, po) +} + +.bin.conf <- function (x, n, alpha = 0.05, method = c("wilson", "exact", "asymptotic", "all"), include.x = FALSE, include.n = FALSE, return.df = FALSE){ + method <- match.arg(method) + bc <- function(x, n, alpha, method) { + nu1 <- 2 * (n - x + 1) + nu2 <- 2 * x + ll <- if (x > 0) + x/(x + qf(1 - alpha/2, nu1, nu2) * (n - x + 1)) + else 0 + nu1p <- nu2 + 2 + nu2p <- nu1 - 2 + pp <- if (x < n) + qf(1 - alpha/2, nu1p, nu2p) + else 1 + ul <- ((x + 1) * pp)/(n - x + (x + 1) * pp) + zcrit <- -qnorm(alpha/2) + z2 <- zcrit * zcrit + p <- x/n + cl <- (p + z2/2/n + c(-1, 1) * zcrit * sqrt((p * (1 - + p) + z2/4/n)/n))/(1 + z2/n) + if (x == 1) + cl[1] <- -log(1 - alpha)/n + if (x == (n - 1)) + cl[2] <- 1 + log(1 - alpha)/n + asymp.lcl <- x/n - qnorm(1 - alpha/2) * sqrt(((x/n) * + (1 - x/n))/n) + asymp.ucl <- x/n + qnorm(1 - alpha/2) * sqrt(((x/n) * + (1 - x/n))/n) + res <- rbind(c(ll, ul), cl, c(asymp.lcl, asymp.ucl)) + res <- cbind(rep(x/n, 3), res) + switch(method, wilson = res[2, ], exact = res[1, ], asymptotic = res[3, + ], all = res, res) + } + if ((length(x) != length(n)) & length(x) == 1) + x <- rep(x, length(n)) + if ((length(x) != length(n)) & length(n) == 1) + n <- rep(n, length(x)) + if ((length(x) > 1 | length(n) > 1) & method == "all") { + method <- "wilson" + warning("method = all will not work with vectors ... setting method to wilson") + } + if (method == "all" & length(x) == 1 & length(n) == 1) { + mat <- bc(x, n, alpha, method) + dimnames(mat) <- list(c("Exact", "Wilson", "Asymptotic"), + c("PointEst", "Lower", "Upper")) + if (include.n) + mat <- cbind(N = n, mat) + if (include.x) + mat <- cbind(X = x, mat) + if (return.df) + mat <- as.data.frame(mat) + return(mat) + } + mat <- matrix(ncol = 3, nrow = length(x)) + for (i in 1:length(x)) mat[i, ] <- bc(x[i], n[i], alpha = alpha, + method = method) + dimnames(mat) <- list(rep("", dim(mat)[1]), c("PointEst", + "Lower", "Upper")) + if (include.n) + mat <- cbind(N = n, mat) + if (include.x) + mat <- cbind(X = x, mat) + if (return.df) + mat <- as.data.frame(mat, row.names = NULL) + mat +} diff -Nru r-cran-epir-0.9-32/R/zzz.R r-cran-epir-0.9-38/R/zzz.R --- r-cran-epir-0.9-32/R/zzz.R 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/R/zzz.R 2012-02-01 20:46:16.000000000 +0000 @@ -2,7 +2,10 @@ { ver <- read.dcf(file.path(libname, pkgname, "DESCRIPTION"), "Version") ver <- as.character(ver) - message("Package epiR ", ver, " is loaded") - message("Type help(epi.about) for summary information") - message("\n") -} \ No newline at end of file + packageStartupMessage("Package epiR ", ver, " is loaded", appendLF = TRUE) + packageStartupMessage("Type help(epi.about) for summary information") + packageStartupMessage("\n") +} + + + diff -Nru r-cran-epir-0.9-32/debian/README.Debian r-cran-epir-0.9-38/debian/README.Debian --- r-cran-epir-0.9-32/debian/README.Debian 2010-07-30 21:14:10.000000000 +0000 +++ r-cran-epir-0.9-38/debian/README.Debian 1970-01-01 00:00:00.000000000 +0000 @@ -1,7 +0,0 @@ -Notes on how this package can be tested. -──────────────────────────────────────── - -This package can be tested by loading it into R with the command -‘library(epiR)’ in order to confirm its integrity. - - -- Andreas Tille Fri, 30 Jul 2010 23:09:30 +0200 diff -Nru r-cran-epir-0.9-32/debian/README.test r-cran-epir-0.9-38/debian/README.test --- r-cran-epir-0.9-32/debian/README.test 1970-01-01 00:00:00.000000000 +0000 +++ r-cran-epir-0.9-38/debian/README.test 2010-07-30 21:14:10.000000000 +0000 @@ -0,0 +1,7 @@ +Notes on how this package can be tested. +──────────────────────────────────────── + +This package can be tested by loading it into R with the command +‘library(epiR)’ in order to confirm its integrity. + + -- Andreas Tille Fri, 30 Jul 2010 23:09:30 +0200 diff -Nru r-cran-epir-0.9-32/debian/changelog r-cran-epir-0.9-38/debian/changelog --- r-cran-epir-0.9-32/debian/changelog 2011-10-26 09:49:09.000000000 +0000 +++ r-cran-epir-0.9-38/debian/changelog 2013-05-05 16:12:36.000000000 +0000 @@ -1,3 +1,27 @@ +r-cran-epir (0.9-38-1precise0) precise; urgency=low + + * Compilation for Ubuntu 12.04.2 LTS + + -- Michael Rutter Sun, 05 May 2013 16:12:36 +0000 + +r-cran-epir (0.9-38-1) unstable; urgency=low + + * New upstream version + * debian/control: + - Standards-Version: 3.9.3 (no changes needed) + - Build-Depends: r-base-dev (>= 2.14.2~20120222), + r-cran-survival, r-cran-hmisc + - Depends: r-cran-survival, r-cran-hmisc + * debian/rules: + - Remove R:Depends substitution variable which is now + included in /usr/share/R/debian/r-cran.mk + * debian/README.Debian is rather README.test + * debian/docs: Install README.test + * debian/copyright: Enhanced DEP5 compatibility and verified using + cme fix dpkg-copyright + + -- Andreas Tille Mon, 26 Mar 2012 16:09:03 +0200 + r-cran-epir (0.9-32-1) unstable; urgency=low * New upstream version diff -Nru r-cran-epir-0.9-32/debian/control r-cran-epir-0.9-38/debian/control --- r-cran-epir-0.9-32/debian/control 2011-10-26 07:06:20.000000000 +0000 +++ r-cran-epir-0.9-38/debian/control 2012-03-26 14:26:43.000000000 +0000 @@ -4,15 +4,16 @@ Maintainer: Debian Med Packaging Team Uploaders: Andreas Tille DM-Upload-Allowed: yes -Build-Depends: debhelper (>= 8), cdbs, r-base-dev -Standards-Version: 3.9.2 +Build-Depends: debhelper (>= 8), cdbs, r-base-dev (>= 2.14.2~20120222), r-cran-survival, + r-cran-hmisc +Standards-Version: 3.9.3 Homepage: http://cran.r-project.org/web/packages/epiR Vcs-Browser: http://svn.debian.org/wsvn/debian-med/trunk/packages/R/r-cran-epir/trunk/ Vcs-Svn: svn://svn.debian.org/debian-med/trunk/packages/R/r-cran-epir/trunk/ Package: r-cran-epir Architecture: all -Depends: ${shlibs:Depends}, ${R:Depends} +Depends: ${shlibs:Depends}, ${R:Depends}, r-cran-survival, r-cran-hmisc Suggests: r-cran-surveillance, r-cran-epi, r-cran-epibasix, r-cran-epitools Description: GNU R Functions for analysing epidemiological data A package for analysing epidemiological data. Contains functions for diff -Nru r-cran-epir-0.9-32/debian/copyright r-cran-epir-0.9-38/debian/copyright --- r-cran-epir-0.9-32/debian/copyright 2009-11-20 08:02:11.000000000 +0000 +++ r-cran-epir-0.9-38/debian/copyright 2012-03-26 14:13:39.000000000 +0000 @@ -1,33 +1,31 @@ -Format: Machine-readable license summary, see http://dep.debian.net/deps/dep5/ - -Name: EpiR -Contact: Mark Stevenson +Format: http://www.debian.org/doc/packaging-manuals/copyright-format/1.0/ +Upstream-Name: EpiR +Upstream-Contact: Mark Stevenson Source: http://cran.r-project.org/web/packages/epiR +Files: * +Copyright: 2009-2012 Mark Stevenson with contributions from Telmo Nunes, Javier Sanchez, and Ron Thornton License: GPL-2+ -Copyright: 2009 Mark Stevenson with contributions from Telmo Nunes, Javier Sanchez, and Ron Thornton - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 2 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . +Files: debian/* +Copyright: 2008-2012 Andreas Tille + 2009 Charles Plessy +License: GPL-2+ -Comment: On Debian systems, the complete text of the GNU Public +License: GPL-2+ + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 2 of the License, or + (at your option) any later version. + . + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + . + You should have received a copy of the GNU General Public License + along with this program. If not, see . + . + On Debian systems, the complete text of the GNU Public License version 2 can be found in `/usr/share/common-licenses/GPL-2'. - -Files: debian/* -Copyright: 2008 Andreas Tille - 2009 Charles Plessy -License: Same as r-cran-epir itelf - (see above) -Packaged-By: Andreas Tille -Packaged-Date: Thu, 19 Nov 2009 15:38:39 +0100 diff -Nru r-cran-epir-0.9-32/debian/docs r-cran-epir-0.9-38/debian/docs --- r-cran-epir-0.9-32/debian/docs 1970-01-01 00:00:00.000000000 +0000 +++ r-cran-epir-0.9-38/debian/docs 2012-03-23 22:41:02.000000000 +0000 @@ -0,0 +1 @@ +debian/README.test diff -Nru r-cran-epir-0.9-32/debian/rules r-cran-epir-0.9-38/debian/rules --- r-cran-epir-0.9-32/debian/rules 2011-10-26 09:54:30.000000000 +0000 +++ r-cran-epir-0.9-38/debian/rules 2012-03-26 14:26:26.000000000 +0000 @@ -6,7 +6,5 @@ include /usr/share/R/debian/r-cran.mk # Require a number equal or superior than the R version the package was built with. -install/r-$(debRreposname)-$(cranName):: - echo "R:Depends=r-base-core (>= $(shell R --version | head -n1 | perl -ne 'print / +([0-9]\.[0-9]+\.[0-9])/')~)" >> debian/r-$(debRreposname)-$(cranName).substvars +install/$(package):: chmod a-x debian/$(package)/usr/lib/R/site-library/epiR/DESCRIPTION -# chmod a-x debian/$(package)/usr/lib/R/site-library/$(cranName)/DESCRIPTION diff -Nru r-cran-epir-0.9-32/doc/epiR.pdf r-cran-epir-0.9-38/doc/epiR.pdf --- r-cran-epir-0.9-32/doc/epiR.pdf 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/doc/epiR.pdf 2012-02-01 20:46:16.000000000 +0000 @@ -267,400 +267,412 @@ 177 0 obj << /S /GoTo /D [178 0 R /Fit ] >> endobj -199 0 obj << -/Length 1197 +198 0 obj << +/Length 1250 /Filter /FlateDecode >> stream -xZ[o6~lfHJԥ؊](HFKE\oulyɰ=!%;w.`oa 鋫>(FaH7{OP^H#2M3ØOB'b%o>}#%<|Q,o~iҠ7n$( iX Ix$*-Kuw~HBQ2I6F"#i4lBI"#I<&? 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endobj -1045 0 obj << -/Names [(section*.130) 558 0 R (section*.131) 559 0 R (section*.132) 561 0 R (section*.133) 562 0 R (section*.134) 567 0 R (section*.135) 568 0 R] +1046 0 obj << +/Names [(section*.130) 558 0 R (section*.131) 562 0 R (section*.132) 564 0 R (section*.133) 565 0 R (section*.134) 566 0 R (section*.135) 567 0 R] /Limits [(section*.130) (section*.135)] >> endobj -1046 0 obj << -/Names [(section*.136) 569 0 R (section*.137) 570 0 R (section*.138) 571 0 R (section*.139) 572 0 R (section*.14) 321 0 R (section*.140) 577 0 R] +1047 0 obj << +/Names [(section*.136) 568 0 R (section*.137) 569 0 R (section*.138) 570 0 R (section*.139) 574 0 R (section*.14) 321 0 R (section*.140) 576 0 R] /Limits [(section*.136) (section*.140)] >> endobj -1047 0 obj << -/Names [(section*.141) 578 0 R (section*.142) 579 0 R (section*.143) 580 0 R (section*.144) 581 0 R (section*.145) 586 0 R (section*.146) 587 0 R] +1048 0 obj << +/Names [(section*.141) 577 0 R (section*.142) 578 0 R 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610 0 R (section*.161) 611 0 R (section*.162) 616 0 R] +1051 0 obj << +/Names [(section*.158) 607 0 R (section*.159) 608 0 R (section*.16) 328 0 R (section*.160) 613 0 R (section*.161) 614 0 R (section*.162) 615 0 R] /Limits [(section*.158) (section*.162)] >> endobj -1051 0 obj << -/Names [(section*.163) 617 0 R (section*.164) 618 0 R (section*.165) 619 0 R (section*.166) 620 0 R (section*.167) 624 0 R (section*.168) 625 0 R] +1052 0 obj << +/Names [(section*.163) 616 0 R (section*.164) 617 0 R (section*.165) 618 0 R (section*.166) 623 0 R (section*.167) 627 0 R (section*.168) 628 0 R] /Limits [(section*.163) (section*.168)] >> endobj -1052 0 obj << -/Names [(section*.169) 631 0 R (section*.17) 326 0 R (section*.170) 632 0 R (section*.171) 633 0 R (section*.172) 640 0 R (section*.173) 641 0 R] +1053 0 obj << +/Names [(section*.169) 629 0 R (section*.17) 329 0 R (section*.170) 630 0 R (section*.171) 631 0 R (section*.172) 640 0 R (section*.173) 641 0 R] /Limits [(section*.169) (section*.173)] >> endobj -1053 0 obj << -/Names [(section*.174) 642 0 R (section*.175) 643 0 R (section*.176) 645 0 R (section*.177) 646 0 R (section*.178) 647 0 R (section*.179) 652 0 R] +1054 0 obj << +/Names [(section*.174) 642 0 R (section*.175) 643 0 R (section*.176) 648 0 R (section*.177) 649 0 R (section*.178) 650 0 R (section*.179) 651 0 R] /Limits [(section*.174) (section*.179)] >> endobj -1054 0 obj << -/Names [(section*.18) 330 0 R (section*.180) 653 0 R (section*.181) 654 0 R (section*.182) 656 0 R (section*.183) 657 0 R (section*.184) 663 0 R] +1055 0 obj << +/Names [(section*.18) 330 0 R (section*.180) 652 0 R (section*.181) 653 0 R (section*.182) 659 0 R (section*.183) 660 0 R (section*.184) 662 0 R] /Limits [(section*.18) (section*.184)] >> endobj -1055 0 obj << -/Names [(section*.185) 664 0 R (section*.186) 665 0 R (section*.187) 666 0 R (section*.188) 667 0 R (section*.189) 668 0 R (section*.19) 331 0 R] +1056 0 obj << +/Names [(section*.185) 663 0 R (section*.186) 664 0 R (section*.187) 668 0 R (section*.188) 669 0 R (section*.189) 670 0 R (section*.19) 331 0 R] /Limits [(section*.185) (section*.19)] >> endobj -1056 0 obj << -/Names [(section*.190) 669 0 R (section*.191) 673 0 R (section*.192) 674 0 R (section*.193) 675 0 R (section*.194) 676 0 R (section*.195) 684 0 R] +1057 0 obj << +/Names [(section*.190) 671 0 R (section*.191) 672 0 R (section*.192) 673 0 R (section*.193) 678 0 R (section*.194) 679 0 R (section*.195) 680 0 R] /Limits [(section*.190) (section*.195)] >> endobj -1057 0 obj << -/Names [(section*.196) 685 0 R (section*.197) 686 0 R (section*.198) 687 0 R (section*.199) 688 0 R (section*.2) 206 0 R (section*.20) 332 0 R] +1058 0 obj << +/Names [(section*.196) 681 0 R (section*.197) 688 0 R (section*.198) 689 0 R (section*.199) 690 0 R (section*.2) 205 0 R (section*.20) 332 0 R] /Limits [(section*.196) (section*.20)] >> endobj -1058 0 obj << -/Names [(section*.200) 692 0 R (section*.201) 694 0 R (section*.202) 695 0 R (section*.203) 696 0 R (section*.204) 697 0 R (section*.205) 698 0 R] +1059 0 obj << +/Names [(section*.200) 691 0 R (section*.201) 693 0 R (section*.202) 694 0 R (section*.203) 695 0 R (section*.204) 699 0 R (section*.205) 700 0 R] /Limits [(section*.200) (section*.205)] >> endobj -1059 0 obj << -/Names [(section*.206) 699 0 R (section*.207) 704 0 R (section*.208) 705 0 R (section*.209) 706 0 R (section*.21) 336 0 R (section*.210) 707 0 R] +1060 0 obj << +/Names [(section*.206) 701 0 R (section*.207) 703 0 R (section*.208) 704 0 R (section*.209) 705 0 R (section*.21) 339 0 R (section*.210) 706 0 R] /Limits [(section*.206) (section*.210)] >> endobj -1060 0 obj << -/Names [(section*.211) 708 0 R (section*.212) 709 0 R (section*.213) 710 0 R (section*.214) 715 0 R (section*.215) 716 0 R (section*.216) 717 0 R] +1061 0 obj << +/Names [(section*.211) 710 0 R (section*.212) 711 0 R (section*.213) 712 0 R (section*.214) 714 0 R (section*.215) 715 0 R (section*.216) 716 0 R] /Limits [(section*.211) (section*.216)] >> endobj -1061 0 obj << -/Names [(section*.217) 718 0 R (section*.218) 719 0 R (section*.219) 720 0 R (section*.22) 337 0 R (section*.220) 726 0 R (section*.221) 727 0 R] +1062 0 obj << +/Names [(section*.217) 717 0 R (section*.218) 722 0 R (section*.219) 723 0 R (section*.22) 340 0 R (section*.220) 725 0 R (section*.221) 726 0 R] /Limits [(section*.217) (section*.221)] >> endobj -1062 0 obj << -/Names [(section*.222) 728 0 R (section*.223) 729 0 R (section*.224) 730 0 R (section*.225) 731 0 R (section*.226) 736 0 R (section*.227) 737 0 R] +1063 0 obj << +/Names [(section*.222) 727 0 R (section*.223) 728 0 R (section*.224) 729 0 R (section*.225) 733 0 R (section*.226) 735 0 R (section*.227) 736 0 R] /Limits [(section*.222) (section*.227)] >> endobj -1063 0 obj << -/Names [(section*.228) 738 0 R (section*.229) 739 0 R (section*.23) 338 0 R (section*.230) 740 0 R (section*.231) 741 0 R (section*.232) 742 0 R] +1064 0 obj << +/Names [(section*.228) 737 0 R (section*.229) 738 0 R (section*.23) 341 0 R (section*.230) 739 0 R (section*.231) 740 0 R (section*.232) 744 0 R] /Limits [(section*.228) (section*.232)] >> endobj -1064 0 obj << -/Names [(section*.233) 747 0 R (section*.234) 748 0 R (section*.235) 749 0 R (section*.236) 750 0 R (section*.237) 751 0 R (section*.238) 752 0 R] +1065 0 obj << +/Names [(section*.233) 746 0 R (section*.234) 747 0 R (section*.235) 748 0 R (section*.236) 749 0 R (section*.237) 750 0 R (section*.238) 755 0 R] /Limits [(section*.233) (section*.238)] >> endobj -1065 0 obj << -/Names [(section*.239) 756 0 R (section*.24) 342 0 R (section*.240) 757 0 R (section*.241) 759 0 R (section*.242) 760 0 R (section*.243) 761 0 R] +1066 0 obj << +/Names [(section*.239) 756 0 R (section*.24) 342 0 R (section*.240) 757 0 R (section*.241) 762 0 R (section*.242) 763 0 R (section*.243) 764 0 R] /Limits [(section*.239) (section*.243)] >> endobj -1066 0 obj << -/Names [(section*.244) 762 0 R (section*.245) 763 0 R (section*.246) 767 0 R (section*.247) 769 0 R (section*.248) 770 0 R (section*.249) 771 0 R] +1067 0 obj << +/Names [(section*.244) 765 0 R (section*.245) 766 0 R (section*.246) 767 0 R (section*.247) 769 0 R (section*.248) 770 0 R (section*.249) 774 0 R] /Limits [(section*.244) (section*.249)] >> endobj -1067 0 obj << -/Names [(section*.25) 343 0 R (section*.250) 772 0 R (section*.251) 773 0 R (section*.252) 778 0 R (section*.253) 779 0 R (section*.254) 780 0 R] +1068 0 obj << +/Names [(section*.25) 343 0 R (section*.250) 775 0 R (section*.251) 776 0 R (section*.252) 778 0 R (section*.253) 779 0 R (section*.254) 780 0 R] /Limits [(section*.25) (section*.254)] >> endobj -1068 0 obj << -/Names [(section*.255) 781 0 R (section*.256) 782 0 R (section*.257) 783 0 R (section*.258) 789 0 R (section*.259) 793 0 R (section*.26) 344 0 R] +1069 0 obj << +/Names [(section*.255) 786 0 R (section*.256) 787 0 R (section*.257) 788 0 R (section*.258) 789 0 R (section*.259) 793 0 R (section*.26) 344 0 R] /Limits [(section*.255) (section*.26)] >> endobj -1069 0 obj << -/Names [(section*.260) 794 0 R (section*.261) 795 0 R (section*.262) 796 0 R (section*.263) 803 0 R (section*.264) 804 0 R (section*.265) 805 0 R] +1070 0 obj << +/Names [(section*.260) 794 0 R (section*.261) 802 0 R (section*.262) 803 0 R (section*.263) 804 0 R (section*.264) 805 0 R (section*.265) 806 0 R] /Limits [(section*.260) (section*.265)] >> endobj -1070 0 obj << -/Names [(section*.266) 806 0 R (section*.267) 808 0 R (section*.268) 809 0 R (section*.269) 814 0 R (section*.27) 346 0 R (section*.270) 815 0 R] +1071 0 obj << +/Names [(section*.266) 810 0 R (section*.267) 812 0 R (section*.268) 813 0 R (section*.269) 814 0 R (section*.27) 346 0 R (section*.270) 819 0 R] /Limits [(section*.266) (section*.270)] >> endobj -1071 0 obj << -/Names [(section*.271) 816 0 R (section*.272) 817 0 R (section*.273) 818 0 R (section*.274) 822 0 R (section*.275) 824 0 R (section*.276) 825 0 R] +1072 0 obj << +/Names [(section*.271) 820 0 R (section*.272) 821 0 R (section*.273) 822 0 R (section*.274) 823 0 R (section*.275) 828 0 R (section*.276) 829 0 R] /Limits [(section*.271) (section*.276)] >> endobj -1072 0 obj << -/Names [(section*.277) 830 0 R (section*.278) 831 0 R (section*.279) 837 0 R (section*.28) 347 0 R (section*.280) 838 0 R (section*.281) 839 0 R] +1073 0 obj << +/Names [(section*.277) 830 0 R (section*.278) 836 0 R (section*.279) 837 0 R (section*.28) 347 0 R (section*.280) 842 0 R (section*.281) 843 0 R] /Limits [(section*.277) (section*.281)] >> endobj -1073 0 obj << -/Names [(section*.282) 844 0 R (section*.283) 853 0 R (section*.284) 854 0 R (section*.285) 855 0 R (section*.286) 860 0 R (section*.287) 861 0 R] +1074 0 obj << +/Names [(section*.282) 844 0 R (section*.283) 857 0 R (section*.284) 858 0 R (section*.285) 859 0 R (section*.286) 860 0 R (section*.287) 861 0 R] /Limits [(section*.282) (section*.287)] >> endobj -1074 0 obj << -/Names [(section*.288) 862 0 R (section*.289) 864 0 R (section*.29) 348 0 R (section*.290) 869 0 R (section*.3) 254 0 R (section*.30) 349 0 R] +1075 0 obj << +/Names [(section*.288) 862 0 R (section*.289) 868 0 R (section*.29) 352 0 R (section*.290) 869 0 R (section*.3) 253 0 R (section*.30) 353 0 R] /Limits [(section*.288) (section*.30)] >> endobj -1075 0 obj << -/Names [(section*.31) 354 0 R (section*.32) 355 0 R (section*.33) 356 0 R (section*.34) 361 0 R (section*.35) 362 0 R (section*.36) 363 0 R] +1076 0 obj << +/Names [(section*.31) 354 0 R (section*.32) 355 0 R (section*.33) 360 0 R (section*.34) 365 0 R (section*.35) 366 0 R (section*.36) 367 0 R] /Limits [(section*.31) (section*.36)] >> endobj -1076 0 obj << -/Names [(section*.37) 367 0 R (section*.38) 368 0 R (section*.39) 369 0 R (section*.4) 255 0 R (section*.40) 371 0 R (section*.41) 372 0 R] +1077 0 obj << +/Names [(section*.37) 368 0 R (section*.38) 369 0 R (section*.39) 370 0 R (section*.4) 254 0 R (section*.40) 376 0 R (section*.41) 377 0 R] /Limits [(section*.37) (section*.41)] >> endobj -1077 0 obj << -/Names [(section*.42) 378 0 R (section*.43) 379 0 R (section*.44) 380 0 R (section*.45) 381 0 R (section*.46) 382 0 R (section*.47) 387 0 R] +1078 0 obj << +/Names [(section*.42) 378 0 R (section*.43) 379 0 R (section*.44) 380 0 R (section*.45) 381 0 R (section*.46) 385 0 R (section*.47) 387 0 R] /Limits [(section*.42) (section*.47)] >> endobj -1078 0 obj << -/Names [(section*.48) 388 0 R (section*.49) 389 0 R (section*.5) 288 0 R (section*.50) 390 0 R (section*.51) 391 0 R (section*.52) 392 0 R] +1079 0 obj << +/Names [(section*.48) 388 0 R (section*.49) 389 0 R (section*.5) 288 0 R (section*.50) 390 0 R (section*.51) 391 0 R (section*.52) 395 0 R] /Limits [(section*.48) (section*.52)] >> endobj -1079 0 obj << -/Names [(section*.53) 396 0 R (section*.54) 398 0 R (section*.55) 399 0 R (section*.56) 400 0 R (section*.57) 401 0 R (section*.58) 407 0 R] +1080 0 obj << +/Names [(section*.53) 396 0 R (section*.54) 398 0 R (section*.55) 399 0 R (section*.56) 400 0 R (section*.57) 405 0 R (section*.58) 407 0 R] /Limits [(section*.53) (section*.58)] >> endobj -1080 0 obj << +1081 0 obj << /Names [(section*.59) 412 0 R (section*.6) 289 0 R (section*.60) 422 0 R (section*.61) 423 0 R (section*.62) 424 0 R (section*.63) 425 0 R] /Limits [(section*.59) (section*.63)] >> endobj -1081 0 obj << -/Names [(section*.64) 426 0 R (section*.65) 428 0 R (section*.66) 429 0 R (section*.67) 433 0 R (section*.68) 434 0 R (section*.69) 435 0 R] +1082 0 obj << +/Names [(section*.64) 429 0 R (section*.65) 431 0 R (section*.66) 432 0 R (section*.67) 433 0 R (section*.68) 434 0 R (section*.69) 435 0 R] /Limits [(section*.64) (section*.69)] >> endobj -1082 0 obj << -/Names [(section*.7) 292 0 R (section*.70) 436 0 R (section*.71) 437 0 R (section*.72) 439 0 R (section*.73) 445 0 R (section*.74) 446 0 R] +1083 0 obj << +/Names [(section*.7) 292 0 R (section*.70) 436 0 R (section*.71) 437 0 R (section*.72) 444 0 R (section*.73) 445 0 R (section*.74) 446 0 R] /Limits [(section*.7) (section*.74)] >> endobj -1083 0 obj << -/Names [(section*.75) 447 0 R (section*.76) 448 0 R (section*.77) 449 0 R (section*.78) 450 0 R (section*.79) 452 0 R (section*.8) 297 0 R] +1084 0 obj << +/Names [(section*.75) 447 0 R (section*.76) 448 0 R (section*.77) 449 0 R (section*.78) 453 0 R (section*.79) 455 0 R (section*.8) 297 0 R] /Limits [(section*.75) (section*.8)] >> endobj -1084 0 obj << -/Names [(section*.80) 456 0 R (section*.81) 457 0 R (section*.82) 458 0 R (section*.83) 460 0 R (section*.84) 461 0 R (section*.85) 462 0 R] +1085 0 obj << +/Names [(section*.80) 456 0 R (section*.81) 457 0 R (section*.82) 458 0 R (section*.83) 460 0 R (section*.84) 461 0 R (section*.85) 466 0 R] /Limits [(section*.80) (section*.85)] >> endobj -1085 0 obj << +1086 0 obj << /Names [(section*.86) 467 0 R (section*.87) 468 0 R (section*.88) 469 0 R (section*.89) 470 0 R (section*.9) 298 0 R (section*.90) 475 0 R] /Limits [(section*.86) (section*.90)] >> endobj -1086 0 obj << -/Names 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<> endobj 1110 0 obj <>stream + + + + + application/pdf + + + + + + + + + + + + + + + + + + 2012-02-02T09:44:31+13:00 + LaTeX with hyperref package + 2012-02-02T09:44:57+13:00 + 2012-02-02T09:44:57+13:00 + + + + pdfTeX-1.40.11 + False + + + This is MiKTeX-pdfTeX 2.9.3962 (1.40.11) + + + uuid:68ee70ac-38f3-4d54-956d-be3540fc8daf + uuid:d8e48df7-39c1-4912-b432-8e27a4d99080 + + + + + + + + + + + + + + + + + + + + + + + + + +endstream endobj 1111 0 obj <> endobj xref +0 1 +0000000000 65535 f +1108 4 +0000351833 00000 n +0000351976 00000 n +0000352244 00000 n +0000356348 00000 n +trailer +<<67E26C91443C724C95481008AC88BFA0>]/Prev 329467>> +startxref +356411 +%%EOF diff -Nru r-cran-epir-0.9-32/man/epi.2by2.Rd r-cran-epir-0.9-38/man/epi.2by2.Rd --- r-cran-epir-0.9-32/man/epi.2by2.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.2by2.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -7,12 +7,12 @@ } \description{ -Computes summary measures of risk and a chi-squared test for difference in the observed proportions from count data presented in a 2 by 2 table. Multiple strata may be represented by additional rows of count data and in this case crude and Mantel-Haenszel adjusted measures of risk are calculated and chi-squared tests of homogeneity are performed. +Computes summary measures of risk and a chi-squared test for difference in the observed proportions from count data presented in a 2 by 2 table. Multiple strata may be represented by additional rows of count data and in this case crude and Mantel-Haenszel adjusted measures of association are calculated and chi-squared tests of homogeneity are performed. } \usage{ epi.2by2(dat, method = "cohort.count", conf.level = 0.95, - units = 100, verbose = FALSE) + units = 100, homogeneity = "breslow.day", verbose = FALSE) } \arguments{ @@ -20,6 +20,7 @@ \item{method}{a character string indicating the experimental design on which the tabular data has been based. Options are \code{cohort.count}, \code{cohort.time}, \code{case.control}, or \code{cross.sectional}.} \item{conf.level}{magnitude of the returned confidence interval. Must be a single number between 0 and 1.} \item{units}{multiplier for prevalence and incidence estimates.} + \item{homogeneity}{a character string indicating the type of homogeneity test to perform. Options are \code{breslow.day} or \code{woolf}.} \item{verbose}{logical indicating whether detailed or summary results are to be returned.} } @@ -50,7 +51,9 @@ When method equals \code{cross.sectional} the following measures of association are returned: the prevalence ratio (PR), the odds ratio (OR), the attributable prevalence (AR), the attributable prevalence in the population (ARp), the attributable fraction in the exposed (AFe), and the attributable fraction in the population (AFp). -When there are multiple strata, the function returns the appropriate measure of association for each strata (e.g. \code{OR}), the crude measure of association across all strata (e.g. \code{OR.crude}) and the Mantel-Haenszel adjusted measure of association (e.g. \code{OR.summary}). Strata-level weights (i.e. inverse variance of the strata-level measures of assocation) are provided --- these are useful to understand the relationship between the crude strata-level measures of association and the Mantel-Haenszel adjusted measure of association. \code{chisq} returns the results of a chi-squared test for difference in exposed and non-exposed proportions for each strata. \code{chisq.summary} returns the results of a chi-squared test for difference in exposed and non-exposed proportions across all strata. The chi-squared test of homogeneity (e.g. \code{OR.homogeneity}) provides a test of homogeneity of the strata-level measures of association. +When there are multiple strata, the function returns the appropriate measure of association for each strata (e.g. \code{OR.strata}), the crude measure of association across all strata (e.g. \code{OR.crude}) and the Mantel-Haenszel adjusted measure of association (e.g. \code{OR.mh}). Strata-level weights (i.e. inverse variance of the strata-level measures of assocation) are provided --- these are useful to understand the relationship between the crude strata-level measures of association and the Mantel-Haenszel adjusted measure of association. \code{chisq.strata} returns the results of a chi-squared test for difference in exposed and non-exposed proportions for each strata. \code{chisq.crude} returns the results of a chi-squared test for difference in exposed and non-exposed proportions across all strata. \code{chisq.mh} returns the results of the Mantel-Haenszel chi-squared test. + +The tests of homogeneity (e.g. \code{OR.homogeneity}) assess the similarity of the strata-level measures of association. } \references{ @@ -82,7 +85,7 @@ } \author{ -Mark Stevenson and Cord Heuer (EpiCentre, IVABS, Massey University, Palmerston North, New Zealand). +Mark Stevenson, Cord Heuer (EpiCentre, IVABS, Massey University, Palmerston North, New Zealand), Jim Robison-Cox (Department of Math Sciences, Montana State University, Montana, USA). } \note{Measures of strength of association include the prevalence ratio, the incidence risk ratio, the incidence rate ratio and the odds ratio. The incidence risk ratio is the ratio of the incidence risk of disease in the exposed group to the incidence risk of disease in the unexposed group. The odds ratio (also known as the cross-product ratio) is an estimate of the incidence risk ratio. When the incidence of an outcome in the study population is low (say, less than 5\%) the odds ratio will provide a reliable estimate of the incidence risk ratio. The more frequent the outcome becomes, the more the odds ratio will overestimate the incidence risk ratio when it is greater than than 1 or understimate the incidence risk ratio when it is less than 1. @@ -95,10 +98,9 @@ The function checks each strata for cells with zero frequencies. If a zero frequency is found in any cell, 0.5 is added to all cells within the strata. -The chi-squared test of homogeneity is equivalent to the Breslow Day test for interaction. Mantel-Haenszel adjusted measures of association are valid when the measures of association across the different strata are similar (homogenous), that is when the chi-squared test of homogeneity is not significant. -} +The Mantel-Haenszel adjusted measures of association are valid when the measures of association across the different strata are similar (homogenous), that is when the test of homogeneity of the odds (risk) ratios is not significant. -\seealso{ +The tests of homogeneity of the odds (risk) ratio where \code{homogeneity = "breslow.day"} and \code{homogeneity = "woolf"} are based on Jewell (2004, p 152 - 158). Thanks to Jim Robison-Cox for sharing his implementation of these analyses. } \examples{ @@ -112,7 +114,8 @@ dat <- as.table(matrix(c(13,2163,5,3349), nrow = 2, byrow = TRUE)) epi.2by2(dat = dat, method = "cross.sectional", - conf.level = 0.95, units = 100, verbose = FALSE) + conf.level = 0.95, units = 100, homogeneity = "breslow.day", + verbose = FALSE) ## Prevalence ratio: ## The prevalence of FUS in DCF exposed cats is 4.01 times (95\% CI 1.43 to @@ -149,7 +152,8 @@ ## 2 by 2 table analysis: epi.2by2(dat = dat, method = "case.control", - conf.level = 0.95, units = 100, verbose = FALSE) + conf.level = 0.95, units = 100, homogeneity = "breslow.day", + verbose = FALSE) ## EXAMPLE 3 @@ -161,7 +165,7 @@ dat <- as.table(matrix(c(136,22050,1709,127650), nrow = 2, byrow = TRUE)) rval <- epi.2by2(dat = dat, method = "cohort.time", conf.level = 0.90, - units = 1000, verbose = TRUE) + units = 1000, homogeneity = "breslow.day", verbose = TRUE) round(rval$AR, digits = 3) ## The incidence rate of cancer was 7.22 cases per 1000 person-years less in the @@ -175,6 +179,7 @@ ## EXAMPLE 4 +## Adapted from Elwood (2007, pages 194 -- 295): ## The results of an unmatched case control study of the association between ## smoking and cervical cancer were stratified by age. Counts of individuals ## in each group were as follows: @@ -191,9 +196,13 @@ ## Smokers: 23, 14 ## Non-smokers: 37, 62 -## Coerce the count data that has been provided into tabular format: -dat <- data.frame(strata = rep(c("20-29 yrs", "30-39 yrs", "+40 yrs"), each = 2), - exp = rep(c("+","-"), times = 3), dis = rep(c("+","-"), times = 3)) +## Coerce the count data that has been provided into tabular format (take +## care when setting strata labels to make sure they match up with appropriate +## contingency table data): +slabel <- c("20-29 yrs", "30-39 yrs", "40+ yrs") +dat <- data.frame(strata = rep(slabel, each = 2), + exp = rep(c("+","-"), times = length(slabel)), dis = rep(c("+","-"), + times = length(slabel))) dat$exp <- factor(dat$exp, levels = c("+", "-")) dat$dis <- factor(dat$dis, levels = c("+", "-")) dat <- table(dat$exp, dat$dis, dat$strata, @@ -205,7 +214,7 @@ dat[2,2,] <- c(53,83,62) tmp.2by2 <- epi.2by2(dat = dat, method = "case.control", conf.level = 0.95, - units = 100, verbose = TRUE) + units = 100, homogeneity = "breslow.day", verbose = TRUE) tmp.2by2 ## Crude odds ratio: @@ -214,14 +223,14 @@ ## Mantel-Haenszel adjusted odds ratio: ## 6.27 (95\% CI 3.52 to 11.17) -## Summary chi-squared test for difference in proportions: +## Mantel-Haenszel chi-squared test for difference in proportions: ## Test statistic 83.31; df = 1; P < 0.01 -## Test of homeogeneity of odds ratios: -## Test statistic 2.09; df = 2; P = 0.35 +## Breslow Day test of homeogeneity of odds ratios: +## Test statistic 12.66; df = 2; P < 0.01 -## We accept the null hypothesis that the strata level odds ratios -## are homogenous. The crude odds ratio is 6.57 (95\% CI 4.31 -- 10.03). +## We reject the null hypothesis and conclude that the strata level odds ratios +## are inhomogenous. The crude odds ratio is 6.57 (95\% CI 4.31 -- 10.03). ## The Mantel-Haenszel adjusted odds ratio is 6.27 (95\% CI 3.52 to 11.17). ## The crude odds ratio is 1.05 times the magnitude of the Mantel-Haenszel ## adjusted odds ratio so we conclude that age does not confound the association @@ -239,16 +248,16 @@ y.labels <- c("Mantel-Haenszel", strata.lab) x.labels <- c(0.5, 1, 2, 4, 8, 16, 32, 64, 128) -or.l <- c(tmp.2by2$OR.summary$lower, tmp.2by2$OR$lower) -or.u <- c(tmp.2by2$OR.summary$upper, tmp.2by2$OR$upper) -or.p <- c(tmp.2by2$OR.summary$est, tmp.2by2$OR$est) +or.l <- c(tmp.2by2$OR.mh$lower, tmp.2by2$OR.strata$lower) +or.u <- c(tmp.2by2$OR.mh$upper, tmp.2by2$OR.strata$upper) +or.p <- c(tmp.2by2$OR.mh$est, tmp.2by2$OR.strata$est) vert <- 1:length(or.p) segplot(vert ~ or.l + or.u, centers = or.p, horizontal = TRUE, aspect = 1/2, col = "grey", ylim = c(0,vert + 1), xlab = "Odds ratio", ylab = "", - scales = list(y = list(at = y.at, labels = y.labels, ticks = FALSE)), + scales = list(y = list(at = y.at, labels = y.labels)), main = "Strata level and summary measures of association")} ## End(Not run) diff -Nru r-cran-epir-0.9-32/man/epi.RtoBUGS.Rd r-cran-epir-0.9-38/man/epi.RtoBUGS.Rd --- r-cran-epir-0.9-32/man/epi.RtoBUGS.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.RtoBUGS.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -21,9 +21,6 @@ Does not check to ensure that only numbers are being produced. In particular, factor labels in a data frame will be output to the file, which normally won't be desired. } -\value{ -} - \references{ Best, NG. WinBUGS 1.3.1 Short Course, Brisbane, November 2000. } @@ -32,14 +29,5 @@ Terry Elrod (Terry.Elrod@UAlberta.ca), Kenneth Rice. } -\note{ -} - -\seealso{ -} - -\examples{ -} - \keyword{univar}% at least one, from doc/KEYWORDS \keyword{univar}% __ONLY ONE__ keyword per line diff -Nru r-cran-epir-0.9-32/man/epi.about.Rd r-cran-epir-0.9-38/man/epi.about.Rd --- r-cran-epir-0.9-32/man/epi.about.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.about.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -5,7 +5,7 @@ \title{The library epiR: summary information} \description{ -Functions for quantitative epidemiology. +An R package for the analysis of epidemiological data. } \usage{ @@ -16,12 +16,6 @@ The most recent version of the \code{epiR} package can be obtained from: \url{http://epicentre.massey.ac.nz/} } -\value{ -} - -\references{ -} - \author{ Mark Stevenson, EpiCentre, IVABS, Massey University, Palmerston North New Zealand. @@ -32,13 +26,4 @@ Ron Thornton, MAF New Zealand, PO Box 2526 Wellington, New Zealand. } -\note{ -} - -\seealso{ -} - -\examples{ -} - \keyword{univar} diff -Nru r-cran-epir-0.9-32/man/epi.asc.Rd r-cran-epir-0.9-38/man/epi.asc.Rd --- r-cran-epir-0.9-32/man/epi.asc.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.asc.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -23,19 +23,10 @@ \item{na}{scalar, defines null values in the matrix. NAs are converted to this value.} } -\details{ -} - \value{ Writes an ASCII raster file (typically with \code{*.asc} extension), suitable for display in a GIS package. } -\references{ -} - -\author{ -} - \note{ The \code{image} function in R rotates tabular data counter clockwise by 90 degrees for display. A matrix of the form: @@ -54,12 +45,5 @@ It is recommended that the source data for this function is a matrix. Replacement of \code{NA}s in a data frame extends processing time for this function. } -\seealso{ -} - -\examples{ - -} - \keyword{univar} diff -Nru r-cran-epir-0.9-32/man/epi.bohning.Rd r-cran-epir-0.9-38/man/epi.bohning.Rd --- r-cran-epir-0.9-32/man/epi.bohning.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.bohning.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -18,12 +18,8 @@ \item{alpha}{alpha level to be used for the test of significance. Must be a single number between 0 and 1.} } -\details{ - -} - \value{ -A data frame with two elements: \code{test.statistic} Bohning's test statistic, \code{p.value} the associated P-value. +A data frame with two elements: \code{test.statistic}, Bohning's test statistic, \code{p.value} the associated P-value. } \references{ @@ -32,15 +28,6 @@ Ugarte MD, Ibanez B, Militino AF (2006). Modelling risks in disease mapping. Statistical Methods in Medical Research 15: 21 - 35. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ data(epi.SClip) obs <- epi.SClip$cases diff -Nru r-cran-epir-0.9-32/man/epi.ccc.Rd r-cran-epir-0.9-38/man/epi.ccc.Rd --- r-cran-epir-0.9-32/man/epi.ccc.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.ccc.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -37,12 +37,6 @@ \item{nmissing}{a count of the number of measurement pairs ignored due to missingness.} } -\note{ -} - -\author{ -} - \references{ Bland J, Altman D (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet 327: 307 - 310. @@ -66,9 +60,6 @@ Snedecor G, Cochran W (1989). Statistical Methods. Ames: Iowa State University Press. } -\seealso{ -} - \examples{ ## Concordance correlation plot: set.seed(seed = 1234) diff -Nru r-cran-epir-0.9-32/man/epi.cluster1size.Rd r-cran-epir-0.9-38/man/epi.cluster1size.Rd --- r-cran-epir-0.9-32/man/epi.cluster1size.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.cluster1size.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -24,9 +24,6 @@ \item{conf.level}{scalar, defining the level of confidence in the computed result.} } -\details{ -} - \value{ Returns an integer defining the required number of clusters to be sampled. } @@ -35,15 +32,6 @@ Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 258. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## We intend to conduct a survey of residents to estimate the total number ## over 65 years of age that require the services of a nurse. There are diff -Nru r-cran-epir-0.9-32/man/epi.cluster2size.Rd r-cran-epir-0.9-38/man/epi.cluster2size.Rd --- r-cran-epir-0.9-32/man/epi.cluster2size.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.cluster2size.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -37,15 +37,6 @@ Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 292. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## We intend to conduct a survey of nurse practitioners to estimate the ## average number of patients seen by each nurse. There are five health diff -Nru r-cran-epir-0.9-32/man/epi.clustersize.Rd r-cran-epir-0.9-38/man/epi.clustersize.Rd --- r-cran-epir-0.9-32/man/epi.clustersize.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.clustersize.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -21,9 +21,6 @@ \item{conf.level}{scalar, defining the level of confidence in the computed result.} } -\details{ -} - \value{ A list containing the following: \item{clusters}{the estimated number of clusters to be sampled.} @@ -38,16 +35,10 @@ Bennett S, Woods T, Liyanage WM, Smith DL (1991). A simplified general method for cluster-sample surveys of health in developing countries. Raport trimestriel de statistiques sanitaires modiales 44: 98 - 106. } -\author{ -} - \note{ The intra-cluster correlation (\code{rho}) will be higher for those situations where the between-cluster variation is greater than within-cluster variation. The design effect is dependent on \code{rho} and \code{b} (the number of units sampled per cluster): \code{rho = (D - 1) / (b - 1)}. Design effects of 2, 4, and 7 can be used to estimate \code{rho} when intra-cluster correlation is low, medium, and high (respectively). A design effect of 7.5 should be used when the intra-cluster correlation is unknown. } -\seealso{ -} - \examples{ ## The expected prevalence of disease in a population of cattle is 0.10. ## We wish to conduct a survey, sampling 50 animals per farm. No data diff -Nru r-cran-epir-0.9-32/man/epi.conf.Rd r-cran-epir-0.9-38/man/epi.conf.Rd --- r-cran-epir-0.9-32/man/epi.conf.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.conf.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -50,9 +50,6 @@ The design effect is used to adjust the confidence interval around a prevalence or incidence risk estimate in the presence of clustering. The design effect is a measure of the variability between clusters and is calculated as the ratio of the variance calculated assuming a complex sample design divided by the variance calculated assuming simple random sampling. Adjustment for the effect of clustering can only be done on those prevalence and incidence risk methods that return a standard error (i.e. \code{method = "wilson"} or \code{method = "fleiss"}). } -\value{ -} - \references{ Altman DG, Machin D, Bryant TN, and Gardner MJ (2000). Statistics with Confidence, second edition. British Medical Journal, London, pp. 28 - 29 and pp. 45 - 56. @@ -69,15 +66,6 @@ Rothman KJ (2002). Epidemiology An Introduction. Oxford University Press, London, pp. 130 - 143. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## EXAMPLE 1 dat <- rnorm(n = 100, mean = 0, sd = 1) @@ -91,10 +79,21 @@ epi.conf(dat, ctype = "mean.unpaired") ## EXAMPLE 3 -grp1 <- as.vector(round(rnorm(n = 100, mean = 10, sd = 5), digits = 0)) -grp2 <- as.vector(round(rnorm(n = 100, mean = 7, sd = 5), digits = 0)) -dat <- data.frame(cbind(grp1 = grp1, grp2 = grp2)) -epi.conf(dat, ctype = "mean.paired") +## Two paired samples (Altman et al. 2000, page 31): +## Systolic blood pressure levels were measured in 16 middle-aged men +## before and after a standard exercise test. The mean rise in systolic +## blood pressure was 6.6 mmHg. The standard deviation of the difference +## was 6.0 mm Hg. The standard error of the mean difference was 1.49 mm Hg. + +before <- c(148,142,136,134,138,140,132,144,128,170,162,150,138,154,126,116) +after <- c(152,152,134,148,144,136,144,150,146,174,162,162,146,156,132,126) +dat <- data.frame(before, after) +dat <- data.frame(cbind(before, after)) +epi.conf(dat, ctype = "mean.paired", conf.level = 0.95) + +## The 95\% confidence interval for the population value of the mean +## systolic blood pressure increase after standard exercise was 3.4 to 9.8 +## mm Hg. ## EXAMPLE 4 ## Single sample (Altman et al. 2000, page 47): diff -Nru r-cran-epir-0.9-32/man/epi.convgrid.Rd r-cran-epir-0.9-38/man/epi.convgrid.Rd --- r-cran-epir-0.9-32/man/epi.convgrid.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.convgrid.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -16,26 +16,10 @@ \item{os.refs}{a vector of character strings listing the British National Grid georeferences to be converted.} } -\details{ - -} - -\value{ -} - -\references{ -} - -\author{ -} - \note{ If an invalid georeference is encountered in the vector \code{os.ref} the method returns a \code{NA}. } -\seealso{ -} - \examples{ os.refs <- c("SJ505585","SJ488573","SJ652636") epi.convgrid(os.refs) diff -Nru r-cran-epir-0.9-32/man/epi.cp.Rd r-cran-epir-0.9-38/man/epi.cp.Rd --- r-cran-epir-0.9-32/man/epi.cp.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.cp.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -28,19 +28,10 @@ \item{id}{a vector listing the covariate pattern identifier for each observation.} } -\note{ -} - -\author{ -} - \references{ Dohoo I, Martin W, Stryhn H (2003). Veterinary Epidemiologic Research. AVC Inc, Charlottetown, Prince Edward Island, Canada. } -\seealso{ -} - \examples{ ## Generate a set of covariates: set.seed(seed = 1234) diff -Nru r-cran-epir-0.9-32/man/epi.cpresids.Rd r-cran-epir-0.9-38/man/epi.cpresids.Rd --- r-cran-epir-0.9-32/man/epi.cpresids.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.cpresids.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -20,9 +20,6 @@ \item{covpattern}{a \code{\link{epi.cp}} object.} } -\details{ -} - \value{ A data frame with 13 elements: \code{cpid} the covariate pattern identifier, \code{n} the number of subjects in this covariate pattern, \code{obs} the observed number of successes, \code{pred} the predicted number of successes, \code{raw} the raw residuals, \code{sraw} the standardised raw residuals, \code{pearson} the Pearson residuals, \code{spearson} the standardised Pearson residuals, \code{deviance} the deviance residuals, \code{leverage} leverage, \code{deltabeta} the delta-betas, \code{sdeltabeta} the standardised delta-betas, and \code{deltachi} delta chi statistics. } @@ -31,12 +28,6 @@ Hosmer DW, Lemeshow S (1989). Applied Logistic Regression. John Wiley & Sons, New York, USA, pp. 137 - 138. } -\author{ -} - -\note{ -} - \seealso{ \code{\link{epi.cp}} } diff -Nru r-cran-epir-0.9-32/man/epi.descriptives.Rd r-cran-epir-0.9-38/man/epi.descriptives.Rd --- r-cran-epir-0.9-32/man/epi.descriptives.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.descriptives.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -18,24 +18,6 @@ \item{quantile}{vector of length two specifying quantiles to be calculated.} } -\details{ -} - -\value{ -} - -\references{ -} - -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ tmp <- rnorm(1000, mean = 0, sd = 1) epi.descriptives(tmp, quantile = c(0.025, 0.975)) diff -Nru r-cran-epir-0.9-32/man/epi.detectsize.Rd r-cran-epir-0.9-38/man/epi.detectsize.Rd --- r-cran-epir-0.9-32/man/epi.detectsize.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.detectsize.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -26,9 +26,6 @@ \item{finite.correction}{logial, should a finite correction factor be applied?} } -\details{ -} - \value{ A list containing the following: \item{performance}{The sensitivity and specificity of the testing strategy.} @@ -39,18 +36,12 @@ Dohoo I, Martin W, Stryhn H (2003). Veterinary Epidemiologic Research. AVC Inc, Charlottetown, Prince Edward Island, Canada, pp. 47 and pp 102 - 103. } -\author{ -} - \note{ The finite correction factor reduces the variance of the sample as the sample size approaches the population size. As a rule of thumb, set \code{finite.correction = TRUE} when the sample size is greater than 5\% of the population size. Define \code{se1} and \code{se2} as the sensitivity for the first and second test, \code{sp1} and \code{sp2} as the specificity for the first and second test, \code{p111} as the proportion of disease-positive subjects with a positive test result to both tests and \code{p000} as the proportion of disease-negative subjects with a negative test result to both tests. The covariance between test results for the disease-positive group is \code{p111 - se1 * se2}. The covariance between test results for the disease-negative group is \code{p000 - sp1 * sp2}. } -\seealso{ -} - \examples{ ## EXAMPLE 1 ## We would like to confirm the absence of disease in a single 1000-cow diff -Nru r-cran-epir-0.9-32/man/epi.dgamma.Rd r-cran-epir-0.9-38/man/epi.dgamma.Rd --- r-cran-epir-0.9-32/man/epi.dgamma.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.dgamma.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -17,8 +17,6 @@ \item{rr}{the lower and upper limits of relative risk, estimated \emph{a priori}.} \item{quantiles}{a vector of length two defining the quantiles of the lower and upper relative risk estimates.} } -\details{ -} \value{ Returns the precision (the inverse variance) of the heterogeneity term. @@ -28,15 +26,6 @@ Best, NG. WinBUGS 1.3.1 Short Course, Brisbane, November 2000. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## Suppose we are expecting the lower 5\% and upper 95\% confidence interval ## of relative risk in a data set to be 0.5 and 3.0, respectively. diff -Nru r-cran-epir-0.9-32/man/epi.directadj.Rd r-cran-epir-0.9-38/man/epi.directadj.Rd --- r-cran-epir-0.9-32/man/epi.directadj.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.directadj.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -18,9 +18,6 @@ \item{conf.level}{magnitude of the returned confidence interval. Must be a single number between 0 and 1.} } -\details{ -} - \value{ A list containing the following: \item{crude.strata}{the crude rates for each strata.} @@ -33,13 +30,6 @@ Fay M, Feuer E (1997). Confidence intervals for directly standardized rates: A method based on the gamma distribution. Statistics in Medicine 16: 791 - 801. } -\author{} - -\note{ -} - -\seealso{} - \examples{ ## A study was conducted to estimate the seroprevalence of leptospirosis ## in dogs in Glasgow and Edinburgh, Scotland. The following data were diff -Nru r-cran-epir-0.9-32/man/epi.dms.Rd r-cran-epir-0.9-38/man/epi.dms.Rd --- r-cran-epir-0.9-32/man/epi.dms.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.dms.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -17,24 +17,6 @@ \item{dat}{the data. A one-column matrix is assumed when converting decimal degrees to degrees, minutes, and seconds. A two-column matrix is assumed when converting degrees and decimal minutes to decimal degrees. A three-column matrix is assumed when converting degrees, minutes and seconds to decimal degrees.} } -\details{ -} - -\value{ -} - -\references{ -} - -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## EXAMPLE 1 Degrees, minutes, seconds to decimal degrees: dat <- matrix(c(41, 38, 7.836, -40, 40, 27.921), diff -Nru r-cran-epir-0.9-32/man/epi.dsl.Rd r-cran-epir-0.9-38/man/epi.dsl.Rd --- r-cran-epir-0.9-32/man/epi.dsl.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.dsl.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -51,9 +51,6 @@ Higgins J, Thompson S (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21: 1539 - 1558. } -\author{ -} - \note{ Under the random-effects model, the assumption of a common treatment effect is relaxed, and the effect sizes are assumed to have a normal distribution with variance \code{tau.sq}. diff -Nru r-cran-epir-0.9-32/man/epi.edr.Rd r-cran-epir-0.9-38/man/epi.edr.Rd --- r-cran-epir-0.9-32/man/epi.edr.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.edr.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -32,18 +32,6 @@ Returns the point estimate of the EDR and the lower and upper bounds of the confidence interval of the EDR. } -\references{ -} - -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ set.seed(123) dat <- rpois(n = 50, lambda = 2) diff -Nru r-cran-epir-0.9-32/man/epi.empbayes.Rd r-cran-epir-0.9-38/man/epi.empbayes.Rd --- r-cran-epir-0.9-32/man/epi.empbayes.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.empbayes.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -31,15 +31,6 @@ Langford IH (1994). Using empirical Bayes estimates in the geographical analysis of disease risk. Area 26: 142 - 149. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ data(epi.SClip) obs <- epi.SClip$cases diff -Nru r-cran-epir-0.9-32/man/epi.epidural.Rd r-cran-epir-0.9-38/man/epi.epidural.Rd --- r-cran-epir-0.9-32/man/epi.epidural.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.epidural.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -23,11 +23,8 @@ } } -\source{ -Deeks JJ, Altman DG, Bradburn MJ (2001). Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis. In: Egger M, Davey Smith G, Altman D (eds). Systematic Review in Health Care Meta-Analysis in Context. British Medical Journal, London, pp. 291 - 299. -} - \references{ +Deeks JJ, Altman DG, Bradburn MJ (2001). Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis. In: Egger M, Davey Smith G, Altman D (eds). Systematic Review in Health Care Meta-Analysis in Context. British Medical Journal, London, pp. 291 - 299. } \keyword{datasets} diff -Nru r-cran-epir-0.9-32/man/epi.herdtest.Rd r-cran-epir-0.9-38/man/epi.herdtest.Rd --- r-cran-epir-0.9-32/man/epi.herdtest.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.herdtest.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -23,9 +23,6 @@ \item{k}{scalar, defining the critical number of individuals testing positive that will denote the group as test positive.} } -\details{ -} - \value{ A data frame with four elements: \code{APpos} the probability of obtaining a positive test, \code{APneg} the probability of obtaining a negative test, \code{HSe} the estimated group (herd) sensitivity, and \code{HSp} the estimated group (herd) specificity. } @@ -42,9 +39,6 @@ The method implemented in this function is based on the hypergeometric distribution. } -\seealso{ -} - \examples{ ## EXAMPLE 1 ## We wish to estimate the herd-level sensitivity and specificity of diff -Nru r-cran-epir-0.9-32/man/epi.indirectadj.Rd r-cran-epir-0.9-38/man/epi.indirectadj.Rd --- r-cran-epir-0.9-32/man/epi.indirectadj.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.indirectadj.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -49,12 +49,6 @@ Thanks to Dr. Telmo Nunes (UISEE/DETSA, Faculdade de Medicina Veterinaria - UTL, Rua Prof. Cid dos Santos, 1300-477 Lisboa Portugal) for details and code for the confidence interval calculations. } -\note{ -} - -\seealso{ -} - \examples{ ## EXAMPLE 1 ## Data have been collected on the incidence of tuberculosis in two diff -Nru r-cran-epir-0.9-32/man/epi.insthaz.Rd r-cran-epir-0.9-38/man/epi.insthaz.Rd --- r-cran-epir-0.9-32/man/epi.insthaz.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.insthaz.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -32,17 +32,8 @@ Singer J, Willett J (2003). Applied Longitudinal Data Analysis Modeling Change and Event Occurrence. Oxford University Press, London, pp. 348. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ -library(survival) +require(survival) ovarian.km <- survfit(Surv(futime,fustat) ~ 1, data = ovarian) ovarian.haz <- epi.insthaz(ovarian.km, conf.level = 0.95) diff -Nru r-cran-epir-0.9-32/man/epi.interaction.Rd r-cran-epir-0.9-38/man/epi.interaction.Rd --- r-cran-epir-0.9-32/man/epi.interaction.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.interaction.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -43,15 +43,6 @@ Rothman K, Keller AZ (1972). The effect of joint exposure to alcohol and tabacco on risk of cancer of the mouth and pharynx. Journal of Chronic Diseases 23: 711 - 716. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## Data from Rothman and Keller (1972) evaluating the effect of joint exposure ## to alcohol and tabacco on risk of cancer of the mouth and pharynx (cited in diff -Nru r-cran-epir-0.9-32/man/epi.iv.Rd r-cran-epir-0.9-38/man/epi.iv.Rd --- r-cran-epir-0.9-32/man/epi.iv.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.iv.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -51,9 +51,6 @@ Higgins JP, Thompson SG (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21: 1539 - 1558. } -\author{ -} - \note{ The inverse variance method performs poorly when data are sparse, both in terms of event rates being low and trials being small. The Mantel-Haenszel method (\code{\link{epi.mh}}) is more robust when data are sparse. diff -Nru r-cran-epir-0.9-32/man/epi.kappa.Rd r-cran-epir-0.9-38/man/epi.kappa.Rd --- r-cran-epir-0.9-32/man/epi.kappa.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.kappa.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -9,14 +9,13 @@ } \usage{ -epi.kappa(a, b, c, d, conf.level = 0.95) +epi.kappa(dat, method = "fleiss", alternative = c("two.sided", "less", "greater"), conf.level = 0.95) } \arguments{ - \item{a}{number of observations where observer 1 positive and observer 2 positive.} - \item{b}{number of observations where observer 1 negative and observer 2 positive.} - \item{c}{number of observations where observer 1 positive and observer 2 negative.} - \item{d}{number of observations where observer 1 negative and observer 2 negative.} + \item{dat}{an object of class table with the individual cell frequencies.} + \item{method}{a character string indicating the method to use. Options are \code{fleiss} or \code{altman}.} + \item{alternative}{a character string specifying the alternative hypothesis, must be one of \code{two.sided}, \code{greater} or \code{less}.} \item{conf.level}{magnitude of the returned confidence interval. Must be a single number between 0 and 1.} } @@ -24,35 +23,34 @@ Kappa is a measure of agreement beyond the level of agreement expected by chance alone. The observed agreement is the proportion of samples for which both methods (or observers) agree. Common interpretations for the kappa statistic are as follows: < 0.2 slight agreement, 0.2 - 0.4 fair agreement, 0.4 - 0.6 moderate agreement, 0.6 - 0.8 substantial agreement, > 0.8 almost perfect agreement. + +\code{alternative = "greater"} tests the hypothesis that kappa is greater than 0. + } \value{ A list containing the following: - \item{kappa}{a data frame with the kappa statistic and the lower and upper bounds of the confidence interval for the kappa statistic.} - \item{mcnemar}{a data frame containing McNemar's test statistic and its associated P-value.} + \item{kappa}{a data frame with the kappa statistic, the standard error of the kappa statistic and the lower and upper bounds of the confidence interval for the kappa statistic.} + \item{z}{a data frame containing the z test statistic and its associated P-value.} } \references{ Altman DG, Machin D, Bryant TN, Gardner MJ (2000). Statistics with Confidence, second edition. British Medical Journal, London, pp. 116 - 118. -Dohoo I, Martin W, Stryhn H (2003). Veterinary Epidemiologic Research. AVC Inc, Charlottetown, Prince Edward Island, Canada, pp. 92. -} +Dohoo I, Martin W, Stryhn H (2010). Veterinary Epidemiologic Research, second edition. AVC Inc, Charlottetown, Prince Edward Island, Canada, pp. 98 - 99. -\author{ +Fleiss JL, Levin B, Paik MC (2003). Statistical Methods for Rates and Proportions, third edition. John Wiley & Sons, London, 598 - 626. } \note{ \tabular{llll}{ - \tab Obs1 + \tab Obs1 - \tab Total \cr -Obs 2 + \tab a \tab b \tab a + b \cr -Obs 2 - \tab c \tab d \tab c + d \cr -Total \tab a + c \tab b + d \tab a + b + c + d\cr -} - -McNemar's test is used to test for the presence of bias. Bias would be present if the proportion positive to each test differed. A non-significant McNemar's test would indicate that the two proportions do not differ, and that the kappa statistic is a valid measure of agreeement + \tab Obs1 + \tab Obs1 - \tab Total \cr +Obs 2 + \tab a \tab b \tab a + b \cr +Obs 2 - \tab c \tab d \tab c + d \cr +Total \tab a + c \tab b + d \tab a + b + c + d\cr } -\seealso{ +McNemar's test is used to test for the presence of bias. Bias would be present if the proportion positive to each test differed. A non-significant McNemar's test would indicate that the two proportions do not differ, and that the kappa statistic is a valid measure of agreeement. } \examples{ @@ -61,18 +59,22 @@ ## was run on each sample. The following results were obtained: ## Lab 1 positive, lab 2 positive: 19 -## Lab 1 positive, lab 2 negative: 10 ## Lab 1 negative, lab 2 positive: 6 +## Lab 1 positive, lab 2 negative: 10 ## Lab 1 negative, lab 2 negative: 256 -epi.kappa(a = 19, b = 10, c = 6, d = 256, conf.level = 0.95) - -## The McNemar's chi-squared test statistic is 1.00 (P = 0.32). We -## conclude that there is little evidence that the two laboratories -## found different proportions positive. +dat <- as.table(matrix(c(19,6,10,256), nrow = 2, byrow = TRUE)) +colnames(dat) <- c("L1-pos","L1-neg") +rownames(dat) <- c("L2-pos","L2-neg") + +epi.kappa(dat, method = "fleiss", alternative = "greater", conf.level = 0.95) + +## FIX +## The z test statistic is 11.53 (P < 0.01). We accept the alternative +## hypothesis that the kappa statistic is greater than zero. ## The proportion of agreements after chance has been excluded is -## 0.67 (95\% CI 0.52 to 0.83). We conclude that, on the basis of +## 0.67 (95\% CI 0.56 to 0.79). We conclude that, on the basis of ## this sample, that there is substantial agreement between the two ## laboratories. } diff -Nru r-cran-epir-0.9-32/man/epi.ltd.Rd r-cran-epir-0.9-38/man/epi.ltd.Rd --- r-cran-epir-0.9-32/man/epi.ltd.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.ltd.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -26,9 +26,6 @@ A data frame with nine elements: \code{ckey} cow identifier, \code{lact} lactation number, \code{llen} lactation length, \code{vltd} milk volume (litres) to last herd test or dry off date (computed on the basis of lactation length, \code{fltd} fat yield (kilograms) to last herd test or dry off date (computed on the basis of lactation length, \code{pltd} protein yield (kilograms) to last herd test or dry off date (computed on the basis of lactation length, \code{vstd} 305-day or 270-day milk volume yield (litres), \code{fstd} 305-day or 270-day milk fat yield (kilograms), and \code{pstd} 305-day or 270-day milk protein yield (kilograms). } -\note{ -} - \author{ Nicolas Lopez-Villalobos and Mark Stevenson (IVABS, Massey University, Palmerston North New Zealand). } @@ -37,9 +34,6 @@ Kirkpatrick M, Lofsvold D, Bulmer M (1990). Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124: 979 - 993. } -\seealso{ -} - \examples{ ## Generate herd test data: ckey <- rep(1, times = 12) diff -Nru r-cran-epir-0.9-32/man/epi.mh.Rd r-cran-epir-0.9-38/man/epi.mh.Rd --- r-cran-epir-0.9-32/man/epi.mh.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.mh.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -49,9 +49,6 @@ Higgins JP, Thompson SG (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21: 1539 - 1558. } -\author{ -} - \note{ Using this method, the pooled odds and risk ratios are computed using the raw individual study weights. The methodology for computing the Mantel-Haenszel summary odds ratio follows the approach decribed in Deeks, Altman and Bradburn MJ (2001, pp 291 - 299). diff -Nru r-cran-epir-0.9-32/man/epi.nomogram.Rd r-cran-epir-0.9-38/man/epi.nomogram.Rd --- r-cran-epir-0.9-32/man/epi.nomogram.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.nomogram.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -19,9 +19,6 @@ \item{verbose}{logical, indicating whether detailed or summary results are to be returned.} } -\details{ -} - \value{ A list containing the following: \item{likelihood.ratio}{the likelihood ratio of a positive and negative test.} @@ -32,15 +29,6 @@ Hunink M, Glasziou P (2001). Decision Making in Health and Medicine - Integrating Evidence and Values. Cambridge University Press, pp. 128 - 156. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## You are presented with a dog with lethargy, exercise intolerance, ## weight gain and bilaterally symmetric truncal alopecia. You are diff -Nru r-cran-epir-0.9-32/man/epi.offset.Rd r-cran-epir-0.9-38/man/epi.offset.Rd --- r-cran-epir-0.9-32/man/epi.offset.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.offset.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -32,15 +32,6 @@ } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ dat <- c(1,1,1,2,2,2,2,3,3,3) dat <- as.factor(dat) diff -Nru r-cran-epir-0.9-32/man/epi.pooled.Rd r-cran-epir-0.9-38/man/epi.pooled.Rd --- r-cran-epir-0.9-32/man/epi.pooled.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.pooled.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -22,9 +22,6 @@ \item{r}{scalar, defining the number of pooled samples per group (or herd).} } -\details{ -} - \value{ A list containing the following: \item{HAPneg}{the apparent prevalence in a disease negative herd.} @@ -39,15 +36,6 @@ Christensen J, Gardner IA (2000). Herd-level interpretation of test results for epidemiologic studies of animal diseases. Preventive Veterinary Medicine 45: 83 - 106. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## We want to test dairy herds for Johne's disease using faecal culture ## which has a sensitivity and specificity of 0.647 and 0.981, respectively. diff -Nru r-cran-epir-0.9-32/man/epi.popsize.Rd r-cran-epir-0.9-38/man/epi.popsize.Rd --- r-cran-epir-0.9-32/man/epi.popsize.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.popsize.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -22,9 +22,6 @@ \item{verbose}{logical indicating whether detailed or summary results are to be returned.} } -\details{ -} - \value{ Returns the estimated population size and an estimate of the numbers of individuals that remain untested. } @@ -33,14 +30,6 @@ Cannon RM, Roe RT (1982). Livestock Disease Surveys A Field Manual for Veterinarians. Australian Government Publishing Service, Canberra, pp. 34. } -\author{ -} - -\note{ -} - -\seealso{ -} \examples{ ## In a field survey 400 feral pigs are captured, marked and then released. ## On a second occassion 40 of the orignal capture are found when another 400 diff -Nru r-cran-epir-0.9-32/man/epi.prcc.Rd r-cran-epir-0.9-38/man/epi.prcc.Rd --- r-cran-epir-0.9-32/man/epi.prcc.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.prcc.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -34,15 +34,6 @@ Sanchez M, Blower S (1997) Uncertainty and sensitivity analysis of the basic reproductive rate. American Journal of Epidemiology, 145: 1127 - 1137. } -\author{ -} - -\note{ -} - -\seealso{ -} - \examples{ ## Create a matrix of simulation results: x1 <- as.data.frame(rnorm(n = 10, mean = 120, sd = 10)) diff -Nru r-cran-epir-0.9-32/man/epi.prev.Rd r-cran-epir-0.9-38/man/epi.prev.Rd --- r-cran-epir-0.9-32/man/epi.prev.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.prev.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -2,14 +2,16 @@ \alias{epi.prev} -\title{Estimate true prevalence +\title{ +Estimate true prevalence } + \description{ Computes the true prevalence of a disease in a population on the basis of an imperfect test. } \usage{ -epi.prev(pos, tested, se, sp, conf.level = 0.95) +epi.prev(pos, tested, se, sp, method = "wilson", conf.level = 0.95) } \arguments{ @@ -17,55 +19,73 @@ \item{tested}{the number tested.} \item{se}{test sensitivity (0 - 1).} \item{sp}{test specificity (0 - 1).} + \item{method}{a character string indicating the method to use. Options are \code{"c-p"} (Cloppper-Pearson), \code{"sterne"} (Sterne), \code{"blaker"} (Blaker) and \code{"wilson"} (Wilson).} \item{conf.level}{magnitude of the returned confidence interval. Must be a single number between 0 and 1.} } \details{ -Exact binomial confidence limits are calculated for apparent prevalence (see Collett 1999 for details). +Appropriate confidence intervals for the adjusted prevalence estimate are provided, accounting for the change in variance that arises from imperfect test sensitivity and specificity (see Reiczigel et al 2010 for details). + +The Clopper-Pearson method is known to be too conservative for two-sided intervals (Blaker 2000, Agresti and Coull 1998). Blaker's and Sterne's methods (Blaker 2000, Sterne 1954) provide smaller exact two-sided confidence interval estimates. } \value{ -A list containing the following: - \item{ap}{the point estimate of apparent prevalence, the standard error of the apparent prevalence, and the lower and upper bounds of the confidence interval around the apparent prevalence estimate.} - \item{tp}{the point estimate of the true prevalence, the standard error of the true prevalence, and the lower and upper bounds of the confidence interval around the true prevalence estimate.} +A list containing the following: + \item{ap}{the point estimate of apparent prevalence and the lower and upper bounds of the confidence interval around the apparent prevalence estimate.} + \item{tp}{the point estimate of the true prevalence and the lower and upper bounds of the confidence interval around the true prevalence estimate.} } + \references{ -Abel U (1993). DieBewertung Diagnostischer Tests. Hippokrates, Stuttgart. +Abel U (1993). DieBewertung Diagnostischer Tests. Hippokrates, Stuttgart. -Collett D (1999). Modelling Binary Data. Chapman & Hall/CRC, Boca Raton Florida, p. 24. +Agresti A, Coull BA (1998). Approximate is better than 'exact' for interval estimation of binomial proportions. American Statistician 52: 119 - 126. -Gardener IA, Greiner M (1999). Advanced Methods for Test Validation and Interpretation in Veterinary Medicince. Freie Universitat Berlin, ISBN 3-929619-22-9; 80 pp. +Blaker H (2000). Confidence curves and improved exact confidence intervals for discrete distributions. Canadian Journal of Statistics 28: 783 - 798. -Messam L, Branscum A, Collins M, Gardner I (2008) Frequentist and Bayesian approaches to prevalence estimation using examples from Johne's disease. Animal Health Research Reviews 9: 1 - 23. +Clopper CJ, Pearson ES (1934). The use of confidence of fiducial limits illustrated in the case of the binomial. Biometrika 26: 404 - 413. -Rogan W, Gladen B (1978). Estimating prevalence from results of a screening test. American Journal of Epidemiology 107: 71 - 76. -} +Gardener IA, Greiner M (1999). Advanced Methods for Test Validation and Interpretation in Veterinary Medicince. Freie Universitat Berlin, ISBN 3-929619-22-9; 80 pp. -\author{ -} +Messam L, Branscum A, Collins M, Gardner I (2008) Frequentist and Bayesian approaches to prevalence estimation using examples from Johne's disease. Animal Health Research Reviews 9: 1 - 23. -\note{ -This function uses apparent prevalence, test sensitivity and test specificity to estimate true prevalence (after Rogan and Gladen, 1978). The standard error of the Rogan Gladen true prevalence estimate is based on Abel (1993) and discussed in Messam et al. (2008). It is assumed that test sensitivity and specificity are known with certainty. +Reiczigel J, Foldi J, Ozsvari L (2010). Exact confidence limits for prevalence of disease with an imperfect diagnostic test. Epidemiology and Infection 138: 1674 - 1678. -The Rogan Gladen true prevalence estimate is unreliable for small sample sizes and when true prevalence is believed to be close to zero. The algorithm implemented here makes no correction to the Rogan Gladen estimate of true prevalence if it is less than zero or greater than one (simply to remind the user that it provides unreliable estimates of true prevalence under these conditions). In this situation one is advised to adopt a Bayesian approach to true prevalence estimation. See Messam et al. (2008) for a very readable introduction. +Rogan W, Gladen B (1978). Estimating prevalence from results of a screening test. American Journal of Epidemiology 107: 71 - 76. + +Sterne TE (1954). Some remarks on confidence or fiducial limits. Biometrika 41: 275 - 278. } -\seealso{ + +\note{This function uses apparent prevalence, test sensitivity and test specificity to estimate true prevalence (after Rogan and Gladen, 1978). Confidence intervals for the apparent and true prevalence estimates are based on code provided by Reiczigel et al. (2010). } \examples{ ## A simple random sample of 150 cows from a herd of 2560 is taken. ## Each cow is given a screening test for brucellosis which has a -## sensitivity of 96\% and a specificity of 89\%. Of the 150 cows tested +## sensitivity of 96% and a specificity of 89%. Of the 150 cows tested ## 23 were positive to the screening test. What is the estimated prevalence -## of brucellosis in this herd (and its 95\% confidence interval)? +## of brucellosis in this herd (and its 95% confidence interval)? -epi.prev(pos = 23, tested = 150, se = 0.96, sp = 0.89, conf.level = 0.95) +epi.prev(pos = 23, tested = 150, se = 0.96, sp = 0.89, method = "blaker", + conf.level = 0.95) ## The estimated true prevalence of brucellosis in this herd is 5.1 cases per -## 100 cows (95\% CI 0 -- 12 cases per 100 cows). +## 100 cows (95% CI 0 -- 13 cases per 100 cows). + +## Moujaber et al. (2008) analysed the seroepidemiology of Helicobacter pylori +## infection in Australia. They reported seroprevalence rates together with +## 95% confidence intervals by age group using the Clopper-Pearson exact +## method (Clopper and Pearson, 1934). The ELISA test they applied had 96.4% +## sensitivity and 92.7% specificity. A total of 151 subjects 1 -- 4 years +## of age were tested. Of this group 6 were positive. What is the estimated +## true prevalence of Helicobacter pylori in this age group? + +epi.prev(pos = 6, tested = 151, se = 0.964, sp = 0.927, method = "c-p", + conf.level = 0.95) + +## The estimated true prevalence of Helicobacter pylori in 1 -- 4 year olds is +## 0 cases per 100 (95% 0 -- 1.3 cases per 100). } -\keyword{univar}% at least one, from doc/KEYWORDS -\keyword{univar}% __ONLY ONE__ keyword per line +\keyword{univar} diff -Nru r-cran-epir-0.9-32/man/epi.simplesize.Rd r-cran-epir-0.9-38/man/epi.simplesize.Rd --- r-cran-epir-0.9-32/man/epi.simplesize.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.simplesize.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -24,9 +24,6 @@ \item{conf.level}{scalar, defining the level of confidence in the computed result.} } -\details{ -} - \value{ Returns an integer defining the size of the sample is required. } @@ -40,17 +37,12 @@ } -\author{ -} - \note{ If the calculated sample size is greater than 10\% of the population, an adjusted sample size is returned. \code{epsilon.r} defines the maximum relative difference between our estimate and the unknown population value. The sample estimate should not differ in absolute value from the true unknown population parameter \code{d} by more than \code{epsilon.r * d}. } -\seealso{ -} \examples{ ## EXAMPLE 1 ## A city contains 20 neighbourhood health clinics and it is desired to take a diff -Nru r-cran-epir-0.9-32/man/epi.smd.Rd r-cran-epir-0.9-38/man/epi.smd.Rd --- r-cran-epir-0.9-32/man/epi.smd.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.smd.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -26,9 +26,6 @@ \item{conf.level}{magnitude of the returned confidence interval. Must be a single number between 0 and 1.} } -\details{ -} - \value{ A list containing the following: \item{md}{standardised mean difference and its confidence interval computed for each trial.} @@ -41,9 +38,6 @@ Deeks JJ, Altman DG, Bradburn MJ (2001). Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis. In: Egger M, Davey Smith G, Altman D (eds). Systematic Review in Health Care Meta-Analysis in Context. British Medical Journal, London, pp. 290 - 291. } -\author{ -} - \note{ The standardised mean difference method is used when trials assess the same outcome, but measure it in a variety of ways. For example: a set of trials might measure depression scores in psychiatric patients but use different methods to quantify depression. In this circumstance it is necessary to standardise the results of the trials to a uniform scale before they can be combined. The standardised mean difference method expresses the size of the treatment effect in each trial relative to the variability observed in that trial. } diff -Nru r-cran-epir-0.9-32/man/epi.stratasize.Rd r-cran-epir-0.9-38/man/epi.stratasize.Rd --- r-cran-epir-0.9-32/man/epi.stratasize.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.stratasize.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -24,9 +24,6 @@ \item{conf.level}{scalar, defining the level of confidence in the computed result.} } -\details{ -} - \value{ A list containing the following: \item{strata.sample}{the estimated sample size for each strata.} @@ -50,9 +47,6 @@ Where \code{method = "proportion"} the vectors \code{strata.mean} and \code{strata.var} are ignored. } -\seealso{ -} - \examples{ ## EXAMPLE 1 ## Hospital episodes (Levy and Lemeshow 1999, page 176 -- 178) diff -Nru r-cran-epir-0.9-32/man/epi.studysize.Rd r-cran-epir-0.9-38/man/epi.studysize.Rd --- r-cran-epir-0.9-32/man/epi.studysize.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.studysize.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -65,9 +65,6 @@ Woodward M (2005). Epidemiology Study Design and Data Analysis. Chapman & Hall/CRC, New York, pp. 381 - 426. } -\author{ -} - \note{ The power of a study is its ability to demonstrate an association, given that an association actually exists. @@ -76,9 +73,6 @@ When \code{method = "proportions"} values need to be entered for \code{control}, \code{n}, and \code{power} to return a value for \code{delta}. When \code{method = "cohort.count"} values need to be entered for \code{control}, \code{n}, and \code{power} to return a value for \code{lambda} (see example 6 below). } -\seealso{ -} - \examples{ ## EXAMPLE 1 (from Woodward p 399) ## Supposed we wish to test, at the 5\% level of significance, the hypothesis diff -Nru r-cran-epir-0.9-32/man/epi.tests.Rd r-cran-epir-0.9-38/man/epi.tests.Rd --- r-cran-epir-0.9-32/man/epi.tests.Rd 2011-05-10 03:39:50.000000000 +0000 +++ r-cran-epir-0.9-38/man/epi.tests.Rd 2012-02-01 20:46:16.000000000 +0000 @@ -58,9 +58,6 @@ Greg Snow (2008) Need help in calculating confidence intervals for sensitivity, specificity, PPV & NPV. R-sig-Epi Digest 23(1): 3March 2008. } -\author{ -} - \note{ \tabular{llll}{ \tab Disease + \tab Disease - \tab Total\cr @@ -70,9 +67,6 @@ } } -\seealso{ -} - \examples{ ## Scott et al. 2008, Table 1: ## A new diagnostic test was trialled on 1586 patients. Of 744 patients