\name{dat.collins1985a} \docType{data} \alias{dat.collins1985a} \title{Studies on the Treatment of Upper Gastrointestinal Bleeding by a Histamine H2 Antagonist} \description{Results from studies examining the presence of persistent or recurrent bleedings in patients receiving either a histamine H2 antagonist or placebo.} \usage{dat.collins1985a} \format{The data frame contains the following columns: \tabular{lll}{ \bold{id} \tab \code{numeric} \tab study number \cr \bold{ref} \tab \code{numeric} \tab reference number \cr \bold{year} \tab \code{numeric} \tab year of publication \cr \bold{nti} \tab \code{numeric} \tab number of patients in treatment group \cr \bold{xti} \tab \code{numeric} \tab number of patients in treatment group with persistent or recurrent bleedings \cr \bold{nci} \tab \code{numeric} \tab number of patients in placebo group \cr \bold{xci} \tab \code{numeric} \tab number of patients in placebo group with persistent or recurrent bleedings } } \details{ The data contained in this dataset were obtained from Table I in van Houwelingen, Zwinderman, and Stijnen (1993). } \source{ Collins, R., & Langman, M. (1985). Treatment with histamine H2 antagonists in acute upper gastrointestinal hemorrhage. \emph{New England Journal of Medicine}, \bold{313}, 660--666. van Houwelingen, H. C., Zwinderman, K. H., & Stijnen, T. (1993). A bivariate approach to meta-analysis. \emph{Statistics in Medicine}, \bold{12}, 2273--2284. } \references{ Curtis, P. S., & Wang, X. (1998). A meta-analysis of elevated CO2 effects on woody plant mass, form, and physiology. \emph{Oecologia}, \bold{113}, 299--313. } \examples{ ### load data data(dat.collins1985a) ### calculate (log) odds ratio and sampling variance dat <- escalc(measure="OR", ai=xci, n1i=nci, ci=xti, n2i=nti, data=dat.collins1985a, to="all") summary(dat, digits=2, transf=exp) ### meta-analysis of log odds ratios using Peto's method res <- rma.peto(ai=xci, n1i=nci, ci=xti, n2i=nti, data=dat.collins1985a) summary(res) \dontrun{ ### meta-analysis of log odds ratios using conditional logistic regression model res <- rma.glmm(measure="OR", ai=xci, n1i=nci, ci=xti, n2i=nti, data=dat.collins1985a, model="CM.EL", method="FE") summary(res) ### plot the log likelihoods of the odds ratios llplot(measure="OR", ai=xci, n1i=nci, ci=xti, n2i=nti, data=dat.collins1985a, lwd=1, refline=NA, xlim=c(-4,4), drop00=FALSE) ### meta-analysis of log odds ratios using conditional logistic regression model res <- rma.glmm(measure="OR", ai=xci, n1i=nci, ci=xti, n2i=nti, data=dat.collins1985a, model="CM.EL", method="ML") summary(res) } } \keyword{datasets}