Analysis of Multivariate Failure Times in the Presence of Selection Bias with Application to Breast Cancer

Abstract

Identifying loci that modify the risk of cancer for mutation carriers is an important topic in oncogenetics. Within this research area, we are concerned with the analysis of the association between a genetic variant (single-nucleotide polymorphism rs13281615) and breast cancer among women with a pathogenic mutation in the BRCA2 gene. As this mutation is rare, data were collected retrospectively according to a case-study design through genetic screening programmes. This involves a selection bias and an intrafamilial correlation, which complicates the statistical analysis. We derive a Cramer-von Mises-type statistic to test the equality of genotype-specific survival functions when the proportional hazards model does not hold. A Clayton copula is specified to model the residual phenotype familial dependence and an innovative semiparametric bootstrap procedure is proposed to approximate the distribution of the test statistic under the null hypothesis. The test proposed is applied to data from European and North American mutation carriers and its performance is evaluated by simulations.

SNP Set Association Testing for Survival Outcomes in the Presence of Intrafamilial Correlation

“…Such dependence may be explained by other genetic, environmental, lifestyle, reproductive, and hormonal factors and has been reported elsewhere [e.g. Leclerc et al., ]. Applying the proposed kinship‐adjusted association test with yields a P ‐value equal to which indicates evidence of association between the SNPs of the TERT locus and breast cancer risk.…”