metadat (version 1.4-0)

dat.laopaiboon2015: Studies on the Effectiveness of Azithromycin for Treating Lower Respiratory Tract Infections

Description

Results from 15 studies on the effectiveness of azithromycin versus amoxycillin or amoxycillin/clavulanic acid (amoxyclav) in the treatment of acute lower respiratory tract infections.

Usage

dat.laopaiboon2015

Format

The data frame contains the following columns:

author	character	author(s)
year	numeric	publication year
ai	numeric	number of clinical failures in the group treated with azithromycin
n1i	numeric	number of patients in the group treated with azithromycin
ci	numeric	number of clinical failures in the group treated with amoxycillin or amoxyclav
n2i	numeric	number of patients in the group treated with amoxycillin or amoxyclav
age	character	whether the trial included adults or children
diag.ab	numeric	trial included patients with a diagnosis of acute bacterial bronchitis
diag.cb	numeric	trial included patients with a diagnosis of chronic bronchitis with acute exacerbation
diag.pn	numeric	trial included patients with a diagnosis of pneumonia
ctrl	character	antibiotic in control group (amoxycillin or amoxyclav)

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

medicine, risk ratios

Details

Azithromycin is an antibiotic useful for the treatment of a number of bacterial infections. Laopaiboon et al. (2015) conducted a meta-analysis of trials comparing the effectiveness of azithromycin versus amoxycillin or amoxycillin/clavulanic acid (amoxyclav) in the treatment of acute lower respiratory tract infections, including acute bacterial bronchitis, acute exacerbations of chronic bronchitis, and pneumonia. The results from 15 trials are included in this dataset.

Examples

### copy data into 'dat' and examine data
dat <- dat.laopaiboon2015
dat

if (FALSE) {
### load metafor package
library(metafor)

### analysis using the Mantel-Haenszel method
rma.mh(measure="RR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, digits=3)

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat)

### random-effects model
res <- rma(yi, vi, data=dat)
res

### average risk ratio with 95% CI
predict(res, transf=exp)
}