mixmeta (version 1.2.0)

bcg: Efficacy of BCG Vaccine in the Prevention of Tuberculosis

Description

The dataset contains the data on 13 prospective clinical trials that compared the rates of tubercolosis in groups vaccinated with the Bacillus Calmette-Guerin (BCG) vaccine and non-vaccinated control populations. The outcome here is reported as both relative risk (RR) and odds ratio (OR), with associated uncertanty.

Usage

bcg

Format

A data frame with 13 observations on the following 13 variables:

• trial: sequence identifying the trial.

• author: label identifying the author(s).

• year: year of publication.

• tpos, tneg: number of positive and negative TB cases in the treated (vaccinated) group.

• cpos, cneg: number of positive and negative TB cases in the control (non-vaccinated) group.

• ablat: absolute latitude of the study location (in degrees).

• alloc: method of treatment allocation (random, alternate, or systematic assignment).

Examples

# NOT RUN {
### REPRODUCE THE RESULTS IN VAN HOUWELINGEN ET AL (2002)

# FIXED-EFFECTS META-ANALYSIS (SECTION 3.1.1)
unifix <- mixmeta(logor, logorvar, data=bcg, method="fixed")
print(summary(unifix), digits=3)

# RANDOM-EFFECTS META-ANALYSIS WITH MAXIMUM LIKELIHOOD (SECTION 3.1.2)
uniran <- mixmeta(logor, logorvar, data=bcg, method="ml")
print(summary(uniran), digits=3, report="var")

# ORIGINAL ESTIMATES AND BEST-LINEAR UNBIASED PREDICTIONS (FIGURE 3)
pred <- with(bcg, cbind(logor, logor-1.96*sqrt(logorvar),
  logor+1.96*sqrt(logorvar)))
blup <- blup(uniran, pi=TRUE)
plot(pred[,1], rev(bcg$trial)+0.2, xlim=c(-3,3), ylim=c(0,14), pch=18,
  axes=FALSE, xlab="Log odds ratio", ylab="Trial", main="Forest plot")
axis(1)
axis(2, at=bcg$trial, labels=rev(bcg$trial), lty=0, las=1)
abline(v=coef(uniran))
segments(pred[,2], rev(bcg$trial)+0.2, pred[,3], rev(bcg$trial)+0.2, lty=5)
points(blup[,1], rev(bcg$trial)-0.2, pch=19)
segments(blup[,2], rev(bcg$trial)-0.2, blup[,3], rev(bcg$trial)-0.2)

# COMPUTE THE OUTCOME SEPARATELY FOR TREATMENT AND CONTROL GROUPS
y <- with(bcg, log(cbind(tpos/tneg, cpos/cneg)))
S <- with(bcg, cbind(1/tpos+1/tneg, 1/cpos+1/cneg))

# BIVARIATE RANDOM-EFFECTS META-ANALYSIS (SECTION 4)
mvran <- mixmeta(y, S, method="ml")
print(summary(mvran), digits=3, report="var")

# META-REGRESSION (SECTION 5)
uniranlat <- update(uniran, .~. + ablat)
print(summary(uniranlat), digits=3, report="var")
drop1(uniranlat, test="Chisq")
# }