dat.bcg: Studies on the Effectiveness of the BCG Vaccine Against...

In metadat: Meta-Analysis Datasets

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. \loadmathjax

Usage

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 \mjeqn2 \times 22x2 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’).

Concepts

medicine, risk ratios, meta-regression

Author(s)

Wolfgang Viechtbauer, [e-mail], https://www.metafor-project.org

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(9), 698–702. https://doi.org/10.1001/jama.1994.03510330076038

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(4), 395–411. https://doi.org/10.1002/sim.4780140406

van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. Statistics in Medicine, 21(4), 589–624. https://doi.org/10.1002/sim.1040

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03

Examples

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

## Not run: 

### load metafor package
library(metafor)

### 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(ablat ~ year, data=dat, pch=19, xlab="Publication Year", ylab="Absolute Lattitude")
cor(dat$ablat, dat$year)


## End(Not run)

metadat documentation built on April 6, 2022, 5:08 p.m.