metafor (version 0.5-0)

dat.bcg: Data for BCG Vaccine Studies

Description

Results from 13 clinical trials examining the effectivess of the bacillus Calmette-Guerin (BCG) vaccine for preventing tuberculosis.

Usage

dat.bcg

format

The data frame contains the following columns: lll{ trial numeric trial number author character author(s) year numeric publication year tpos numeric number of TB positive cases in the treated (vaccinated) group tneg numeric number of TB negative cases in the treated (vaccinated) group cpos numeric number of TB positive cases in the control group cneg numeric number of TB negative cases in the control group ablat numeric absolute latitude where the study was conducted alloc character method of treatment allocation (random, alternate, or systematic) }

source

Colditz, G. A., Brewer, T. F., Berkey, C. S., Wilson, M. E., Burdick, E., Fineberg, H. V., et al. (1994) Efficacy of BCG vaccine in the prevention of tuberculosis: Meta-analysis of the published literature. Journal of the American Medical Association, 271, 698--702.

Details

The 13 studies provide data in terms of 2x2 tables in the form: lcc{ TB positive TB negative vaccinated group tpos tneg control group cpos cneg } The goal of the meta-analysis was to examine the overall effectiveness of the BCG vaccine for preventing tuberculosis and to examine moderators that may potentially influence the size of the effect. The data set has been used in several publications to illustrate meta-analytic methods (see References).

References

Berkey, C. S., Hoaglin, D. C., Mosteller, F. & Colditz, G. A. (1995). A random-effects regression model for meta-analysis. Statistics in Medicine, 14, 395--411. van Houwelingen, H. C., Arends, L. R. & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. Statistics in Medicine, 21, 589--624.

Examples

### load BCG vaccine data
data(dat.bcg)

### meta-analysis of the log risk rates using a random-effects model
res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, 
           measure="RR", data=dat.bcg, method="DL")
res

### average risk rate with 95\% CI
predict(res, transf=exp)

### mixed-effects model with absolute latitude as a moderator
res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, 
           mods=ablat, measure="RR", data=dat.bcg, method="DL")
res

### predicted average risk rates for 10-60 degrees absolute latitude
predict(res, newmods=c(10, 20, 30, 40, 50, 60), transf=exp)