dat.pagliaro1992: Studies on the Effectiveness of Nonsurgical Treatments in...

In metadat: Meta-Analysis Datasets

Studies on the Effectiveness of Nonsurgical Treatments in Cirrhosis

Description

Results from 26 trials examining the effectiveness of beta-blockers and sclerotherapy for the prevention of first bleeding in patients with cirrhosis

Usage

dat.pagliaro1992

Format

The data frame contains the following columns:

study	numeric	study id
trt	character	either beta-blockers, sclerotherapy, or control
xi	numeric	number of patients with first bleeding
ni	numeric	number of patients treated

Details

The dataset includes the results from 26 randomized controlled trials examining the effectiveness of nonsurgical treatments for the prevention of first bleeding in patients with cirrhosis. Patients were either treated with beta-blockers, endoscopic sclerotherapy, or with a nonactive treatment (control). Two trials included all three treatment conditions, 7 trials compared beta-blockers against control, and 17 trials compared sclerotherapy against control. The dataset has been used in various papers to illustrate methods for conducting a network meta-analysis / mixed treatment comparison.

Concepts

medicine, odds ratios, Mantel-Haenszel method, network meta-analysis

Author(s)

Wolfgang Viechtbauer, [e-mail], https://www.metafor-project.org

Source

Pagliaro, L., D'Amico, G., Sörensen, T. I. A., Lebrec, D., Burroughs, A. K., Morabito, A., Tiné, F., Politi, F., & Traina, M. (1992). Prevention of first bleeding in cirrhosis: A meta-analysis of randomized trials of nonsurgical treatment. Annals of Internal Medicine, 117(1), 59–70. https://doi.org/10.7326/0003-4819-117-1-59

Examples

### copy data into 'dat' and examine data
dat <- dat.pagliaro1992
dat

## Not run: 

### load metafor package
library(metafor)

### restructure dataset to a contrast-based format
dat.c <- to.wide(dat, study="study", grp="trt", grpvars=3:4)
dat.c

### Mantel-Haenszel results for beta-blockers and sclerotherapy versus control, respectively
rma.mh(measure="OR", ai=xi.1, n1i=ni.1, ci=xi.2, n2i=ni.2,
       data=dat.c, subset=(trt.1=="beta-blockers"), digits=2)
rma.mh(measure="OR", ai=xi.1, n1i=ni.1, ci=xi.2, n2i=ni.2,
       data=dat.c, subset=(trt.1=="sclerotherapy"), digits=2)

### calculate log odds for each study arm
dat <- escalc(measure="PLO", xi=xi, ni=ni, data=dat)
dat

### turn treatment variable into factor and set reference level
dat$trt <- relevel(factor(dat$trt), ref="control")

### add a space before each level (this makes the output a bit more legible)
levels(dat$trt) <- paste0(" ", levels(dat$trt))

### network meta-analysis using an arm-based random-effects model with fixed study effects
### (by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons)
res <- rma.mv(yi, vi, mods = ~ factor(study) + trt - 1, random = ~ trt | study, rho=1/2, data=dat)
res

### average odds ratio comparing beta-blockers and sclerotherapy versus control, respectively
predict(res, newmods=c(rep(0,26), 1, 0), transf=exp, digits=2)
predict(res, newmods=c(rep(0,26), 0, 1), transf=exp, digits=2)

### average odds ratio comparing beta-blockers versus sclerotherapy
predict(res, newmods=c(rep(0,26), 1, -1), transf=exp, digits=2)


## End(Not run)

metadat documentation built on April 6, 2022, 5:08 p.m.