Adjustment for competing risk in kin-cohort estimation

Abstract

Kin-cohort design can be used to study the effect of a genetic mutation on the risk of multiple events, using the same study. In this design, the outcome data consist of the event history of the relatives of a sample of genotyped subjects. Existing methods for kin-cohort estimation allow estimation of the risk of one event at a time with the assumption that the censoring events are unrelated to the genetic mutation under study. These methods, however, may produce biased estimates of risk when multiple events are related to the genetic mutation, and follow-up of some of the events may be censored by the onset of other events. Using a competing risk framework to address this problem, we show that cause-specific hazard functions for carriers and noncarriers are identifiable from kin-cohort data. For estimation, we propose an extension of a composite-likelihood approach we described previously. We illustrate the use of the proposed method for estimation of the risk of ovarian cancer from BRCA1/2 mutations in the absence of breast cancer, based on data from the Washington Ashkenazi Kin-Cohort Study. We also evaluate the performance of the proposed estimation method, based on simulated data that were generated following the setup of the Washington Ashkenazi Study.