metafor (version 2.0-0)

dat.collins1985a: Studies on the Treatment of Upper Gastrointestinal Bleeding by a Histamine H2 Antagonist

Description

Results from studies examining the effectiveness of histamine H2 antagonists (cimetidine or ranitidine) in treating patients with acute upper gastrointestinal hemorrhage.

Usage

dat.collins1985a

Format

The data frame contains the following columns:

id	numeric	study number
trial	character	first author of trial
year	numeric	year of publication
ref	numeric	reference number
trt	character	C = cimetidine, R = ranitidine
ctrl	character	P = placebo, AA = antacids, UT = usual treatment
nti	numeric	number of patients in treatment group
b.xti	numeric	number of patients in treatment group with persistent or recurrent bleedings
o.xti	numeric	number of patients in treatment group in need of operation
d.xti	numeric	number of patients in treatment group that died
nci	numeric	number of patients in control group
b.xci	numeric	number of patients in control group with persistent or recurrent bleedings
o.xci	numeric	number of patients in control group in need of operation

Details

The data were obtained from Tables 1 and 2 in Collins and Langman (1985). The authors used Peto's (one-step) method for meta-analyzing the 27 trials. This approach is implemented in the rma.peto function. Using the same dataset, van Houwelingen, Zwinderman, and Stijnen (1993) describe some alternative approaches for analyzing these data, including fixed and random-effects conditional logistic models. Those are implemented in the rma.glmm function.

References

van Houwelingen, H. C., Zwinderman, K. H., & Stijnen, T. (1993). A bivariate approach to meta-analysis. Statistics in Medicine, 12, 2273--2284.

Examples

# NOT RUN {
### load data
dat <- get(data(dat.collins1985a))

### meta-analysis of log ORs using Peto's method (outcome: persistent or recurrent bleedings)
res <- rma.peto(ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat)
print(res, digits=2)

# }
# NOT RUN {
### meta-analysis of log ORs using a conditional logistic regression model (FE model)
res <- rma.glmm(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat,
                model="CM.EL", method="FE")
summary(res)
predict(res, transf=exp, digits=2)

### plot the log-likelihoods of the odds ratios
llplot(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat,
       lwd=1, refline=NA, xlim=c(-4,4), drop00=FALSE)

### meta-analysis of log odds ratios using a conditional logistic regression model (RE model)
res <- rma.glmm(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat,
                model="CM.EL", method="ML")
summary(res)
predict(res, transf=exp, digits=2)
# }
# NOT RUN {
### meta-analysis of log ORs using Peto's method (outcome: need for surgery)
res <- rma.peto(ai=o.xti, n1i=nti, ci=o.xci, n2i=nci, data=dat)
print(res, digits=2)

### meta-analysis of log ORs using Peto's method (outcome: death)
res <- rma.peto(ai=d.xti, n1i=nti, ci=d.xci, n2i=nci, data=dat)
print(res, digits=2)
# }

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