A New Inverse Probability of Selection Weighted Cox Model to Deal With Outcome-Dependent Sampling in Survival Analysis

Motivated by the study of genetic effect modifiers of cancer, we examined weighting approaches to correct for ascertainment bias in survival analysis. Outcome-dependent sampling is common in genetic epidemiology leading to study samples with too many events in comparison to the population and an overrepresentation of young, affected subjects. A usual approach to correct for ascertainment bias in this setting is to use an inverse probability-weighted Cox model, using weights based on external available population-based age-specific incidence rates of the type of cancer under investigation. However, the current approach is not general enough leading to invalid weights in relevant practical settings if oversampling of cases is not observed in all age groups. Based on the same principle of weighting observations by their inverse probability of selection, we propose a new, more general approach, called the generalized weighted approach. We show the advantage of the new generalized weighted cohort method using simulations and two real data sets. In both applications, the goal is to assess the association between common susceptibility loci identified in genome-wide association studies (GWAS) and cancer (colorectal and breast) using data collected through genetic testing in clinical genetics centers.