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dat.colditz1994

Studies on the Effectiveness of the BCG Vaccine Against Tuberculosis


Description

Results from 13 studies examining the effectiveness of the Bacillus Calmette-Guerin (BCG) vaccine against tuberculosis.

Usage

dat.colditz1994
dat.bcg

Format

The data frame contains the following columns:

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 (non-vaccinated) group
cneg numeric number of TB negative cases in the control (non-vaccinated) group
ablat numeric absolute latitude of the study location (in degrees)
alloc character method of treatment allocation (random, alternate, or systematic assignment)

Details

The 13 studies provide data in terms of 2x2 tables in the form:

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 dataset has been used in several publications to illustrate meta-analytic methods (see ‘References’).

Source

Colditz, G. A., Brewer, T. F., Berkey, C. S., Wilson, M. E., Burdick, E., Fineberg, H. V., & Mosteller, F. (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.

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.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://www.jstatsoft.org/v036/i03.

Examples

### copy data into 'dat'
dat <- dat.bcg

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat)
dat

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

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

### mixed-effects model with absolute latitude and publication year as moderators
res <- rma(yi, vi, mods = ~ ablat + year, data=dat)
res

### predicted average risk ratios for 10-60 degrees absolute latitude
### holding the publication year constant at 1970
predict(res, newmods=cbind(seq(from=10, to=60, by=10), 1970), transf=exp)

### note: the interpretation of the results is difficult because absolute
### latitude and publication year are strongly correlated (the more recent
### studies were conducted closer to the equator)
plot(dat$ablat, dat$year)
cor(dat$ablat, dat$year)

metafor

Meta-Analysis Package for R

v2.4-0
GPL (>= 2)
Authors
Wolfgang Viechtbauer [aut, cre] (<https://orcid.org/0000-0003-3463-4063>)
Initial release
2020-03-19

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