On the estimation of penetrance in the presence of competing risks with family data
Abstract
In family studies, we are interested in estimating the penetrance function of the event of interest in the presence of competing risks. Failure to account for competing risks may lead to bias in the estimation of the penetrance function. In this thesis, three statistical challenges are addressed: clustering, missing data, and competing risks. We proposed the cause-specific model with shared frailty and ascertainment correction to account for clustering and competing risks along with ascertainment of families into study. Multiple imputation is used to account for missing data. The simulation study showed good performance of our proposed model in estimating the penetrance function under high familial correlation. However the competing risks model without frailty provided a good alternative under low familial correlation. We illustrate the proposed model using Colon Cancer Family Registry data.