metafor (version 1.9-8)

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)

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.

Details

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

	TB positive
TB negative	vaccinated group
tpos	tneg

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’).

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. http://www.jstatsoft.org/v36/i03/.

Examples

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

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

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

### average relative risk 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 relative risks 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)