metadat (version 1.4-0)

dat.frank2008: Studies on the Association Between the CASP8 -652 6N del Promoter Polymorphism and Breast Cancer Risk

Description

Results from 4 case-control studies examining the association between the CASP8 -652 6N del promoter polymorphism and breast cancer risk.

Usage

dat.frank2008

Format

The data frame contains the following columns:

study	character	study identifier
bc.ins.ins	numeric	number of cases who have a homozygous insertion polymorphism
bc.ins.del	numeric	number of cases who have a heterozygous insertion/deletion polymorphism
bc.del.del	numeric	number of cases who have a homozygous deletion polymorphism
ct.ins.ins	numeric	number of controls who have a homozygous insertion polymorphism
ct.ins.del	numeric	number of controls who are heterozygous insertion/deletion polymorphism
ct.del.del	numeric	number of controls who have a homozygous deletion polymorphism

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

medicine, oncology, genetics, odds ratios

Details

The 4 studies included in this dataset are case-control studies that have examined the association between the CASP8 -652 6N del promoter polymorphism and breast cancer risk. Breast cancer cases and controls were genotyped and either had a homozygous insertion, a heterozygous insertion/deletion, or a homozygous deletion polymorphism.

Ziegler et al. (2011) used the same dataset to illustrate the use of meta-analytic methods to examine deviations from Hardy-Weinberg equilibrium across multiple studies. The relative excess heterozygosity (REH) is the proposed measure for such a meta-analysis, which can be computed by setting measure="REH".

References

Ziegler, A., Steen, K. V. & Wellek, S. (2011). Investigating Hardy-Weinberg equilibrium in case-control or cohort studies or meta-analysis. Breast Cancer Research and Treatment, 128(1), 197--201. https://doi.org/10.1007/s10549-010-1295-z

Examples

### copy data into 'dat' and examine data
dat <- dat.frank2008
dat

if (FALSE) {
### load metafor package
library(metafor)

### calculate log odds ratios comparing ins/del versus ins/ins
dat <- escalc(measure="OR", ai=bc.ins.del, bi=bc.ins.ins,
                            ci=ct.ins.del, di=ct.ins.ins, data=dat)

### fit random-effects model and get the pooled odds ratio (with 95% CI)
res <- rma(yi, vi, data=dat)
res
predict(res, transf=exp, digits=2)

### calculate log odds ratios comparing del/del versus ins/ins
dat <- escalc(measure="OR", ai=bc.del.del, bi=bc.ins.ins,
                            ci=ct.del.del, di=ct.ins.ins, data=dat)

### fit random-effects model and get the pooled odds ratio (with 95% CI)
res <- rma(yi, vi, data=dat)
res
predict(res, transf=exp, digits=2)

### calculate log odds ratios comparing ins/del+del/del versus ins/ins
dat <- escalc(measure="OR", ai=bc.ins.del+bc.del.del, bi=bc.ins.ins,
                            ci=ct.ins.del+ct.del.del, di=ct.ins.ins, data=dat)

### fit random-effects model and get the pooled odds ratio (with 95% CI)
res <- rma(yi, vi, data=dat)
res
predict(res, transf=exp, digits=2)

############################################################################

### compute the relative excess heterozygosity in the controls
dat <- escalc(measure="REH", ai=ct.ins.ins, bi=ct.ins.del, ci=ct.del.del,
              slab=study, data=dat)

### fit random-effects model and get the pooled REH value (with 90% CI)
res <- rma(yi, vi, data=dat, level=90)
res
predict(res, transf=exp, digits=2)

### draw forest plot
forest(res, atransf=exp, xlim=c(-1.4,1.4), at=log(c(0.5,5/7,1,7/5,2)))
segments(log(5/7), -2, log(5/7), res$k+1, lty="dotted")
segments(log(7/5), -2, log(7/5), res$k+1, lty="dotted")
}