Studies on the Effectiveness of Azithromycin for Treating Lower Respiratory Tract Infections
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.
dat.laopaiboon2015
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) |
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.
Laopaiboon, M., Panpanich, R., & Swa Mya, K. (2015). Azithromycin for acute lower respiratory tract infections. Cochrane Database of Systematic Reviews, 3, CD001954.
### copy data into 'dat' and examine data dat <- dat.laopaiboon2015 dat ### 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)
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