metafor (version 1.9-9)

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	id

Source

Collins, R., & Langman, M. (1985). Treatment with histamine H2 antagonists in acute upper gastrointestinal hemorrhage. New England Journal of Medicine, 313, 660--666.

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

### 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)
# ## End(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)