metafor (version 1.9-3)

dat.yusuf1985: Studies of Beta Blockers During and After Myocardial Infarction

Description

Results from studies examining the effectiveness of beta blockers for reducing mortality and reinfarction.

Usage

dat.yusuf1985

format

The data frame contains the following columns: lll{ table character table number id character trial id number trial character trial name or first author ai numeric number of deaths/reinfarctions in treatment group n1i numeric number of patients in treatment group ci numeric number of deaths/reinfarctions in control group n2i numeric number of patients in control group }

source

Yusuf, S., Peto, R., Lewis, J., Collins, R., & Sleight, P. (1985). Beta blockade during and after myocardial infarction: An overview of the randomized trials. Progress in Cardiovascular Disease, 27, 335--371.

Details

The dataset contains table 6 (total mortality from short-term trials of oral beta blockers), 9 (total mortality at one week from trials with an initial IV dose of a beta blocker), 10 (total mortality from long-term trials with treatment starting late and mortality from day 8 onwards in long-term trials that began early and continued after discharge), 11 (nonfatal reinfarction from long-term trials of beta blockers), 12a (sudden death in long-term beta blocker trials), and 12b (nonsudden death in long-term beta blocker trials) from the meta-analysis by Yusuf et al. (1985) on the effectiveness of of beta blockers for reducing mortality and reinfarction. The article also describes what is sometimes called Peto's one-step method for meta-analyzing $2 \times 2$ table data. This method is implemented in the rma.peto function.

Examples

### load data
dat <- get(data(dat.yusuf1985))

### to select a table for the analysis
tab <- "6" ### either: 6, 9, 10, 11, 12a, 12b

### to double-check total counts as reported in article
apply(dat[dat$table==tab,4:7], 2, sum, na.rm=TRUE)

### meta-analysis using Peto's one-step method
res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=(table==tab))
res
predict(res, transf=exp, digits=2)