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

dat.frank2008

Format

The data frame contains the following columns:

studycharacterstudy identifier
bc.ins.insnumericnumber of cases who have a homozygous insertion polymorphism
bc.ins.delnumericnumber of cases who have a heterozygous insertion/deletion polymorphism
bc.del.delnumericnumber of cases who have a homozygous deletion polymorphism
ct.ins.insnumericnumber of controls who have a homozygous insertion polymorphism
ct.ins.delnumericnumber of controls who are heterozygous insertion/deletion polymorphism
ct.del.delnumericnumber of controls who have a homozygous deletion polymorphism

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".

Source

Frank, B., Rigas, S. H., Bermejo, J. L., Wiestler, M., Wagner, K., Hemminki, K., Reed, M. W., Sutter, C., Wappenschmidt, B., Balasubramanian, S. P., Meindl, A., Kiechle, M., Bugert, P., Schmutzler, R. K., Bartram, C. R., Justenhoven, C., Ko, Y.-D., Brüning, T., Brauch, H., Hamann, U., Pharoah, P. P. D., Dunning, A. M., Pooley, K. A., Easton, D. F., Cox, A. & Burwinkel, B. (2008). The CASP8 -652 6N del promoter polymorphism and breast cancer risk: A multicenter study. Breast Cancer Research and Treatment, 111(1), 139-144. https://doi.org/10.1007/s10549-007-9752-z

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

Concepts

medicine, oncology, genetics, odds ratios

Examples

### copy data into 'dat' and examine data
dat <- dat.frank2008
dat
#>    study bc.ins.ins bc.ins.del bc.del.del ct.ins.ins ct.ins.del ct.del.del
#> 1  GFBCS        298        535        221        270        506        263
#> 2   SBCS        235        541        251        245        608        321
#> 3 GENICA        280        509        222        285        492        229
#> 4 SEARCH       1133       2115       1050       1149       2263       1062

# \dontrun{

### 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
#> 
#> Random-Effects Model (k = 4; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0058)
#> tau (square root of estimated tau^2 value):      0
#> I^2 (total heterogeneity / total variability):   0.00%
#> H^2 (total variability / sampling variability):  1.00
#> 
#> Test for Heterogeneity:
#> Q(df = 3) = 0.9329, p-val = 0.8175
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb   ci.ub    
#>  -0.0401  0.0395  -1.0155  0.3099  -0.1175  0.0373    
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub pi.lb pi.ub 
#>  0.96  0.89  1.04  0.89  1.04 
#> 

### 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
#> 
#> Random-Effects Model (k = 4; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.0090 (SE = 0.0165)
#> tau (square root of estimated tau^2 value):      0.0950
#> I^2 (total heterogeneity / total variability):   45.66%
#> H^2 (total variability / sampling variability):  1.84
#> 
#> Test for Heterogeneity:
#> Q(df = 3) = 5.4814, p-val = 0.1398
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb   ci.ub    
#>  -0.0988  0.0706  -1.4005  0.1614  -0.2372  0.0395    
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub pi.lb pi.ub 
#>  0.91  0.79  1.04  0.72  1.14 
#> 

### 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
#> 
#> Random-Effects Model (k = 4; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0052)
#> tau (square root of estimated tau^2 value):      0
#> I^2 (total heterogeneity / total variability):   0.00%
#> H^2 (total variability / sampling variability):  1.00
#> 
#> Test for Heterogeneity:
#> Q(df = 3) = 1.6413, p-val = 0.6501
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb   ci.ub    
#>  -0.0481  0.0373  -1.2921  0.1963  -0.1211  0.0249    
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub pi.lb pi.ub 
#>  0.95  0.89  1.03  0.89  1.03 
#> 

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

### 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
#> 
#> Random-Effects Model (k = 4; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0019)
#> tau (square root of estimated tau^2 value):      0.0011
#> I^2 (total heterogeneity / total variability):   0.05%
#> H^2 (total variability / sampling variability):  1.00
#> 
#> Test for Heterogeneity:
#> Q(df = 3) = 3.2001, p-val = 0.3618
#> 
#> Model Results:
#> 
#> estimate      se    zval    pval    ci.lb   ci.ub    
#>   0.0143  0.0228  0.6242  0.5325  -0.0233  0.0518    
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub pi.lb pi.ub 
#>  1.01  0.98  1.05  0.98  1.05 
#> 

### draw forest plot
forest(res, atransf=exp, header=TRUE, xlim=c(-1.5,1.5), 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")


# }