Latent Class Approach to Survival Analysis with a Compound Poisson Frailty Model with an Application to HIV Prevention Trials

• Activity Number: 171
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Rebecca Coley*+ and Elizabeth Brown
• Companies: University of Washington and Fred Hutchinson Cancer Research Center
• Keywords: Bayesian analysis ; Frailty ; Heterogeneity ; HIV prevention ; Hierarchical modeling ; Latent class analysis

Abstract

In randomized clinical trials where the outcome is time-to-event, intervention effectiveness is estimated with the Cox model. When heterogeneity is present, the assumption of proportionality does not hold and the Cox population-level estimate underestimates effectiveness for individuals at risk. This discrepancy is of particular concern in HIV prevention trials, where heterogeneity is expected and some participants have no risk of an event. Frailty models adjust for heterogeneity, but existing methods for univariate survival data assume a shared frailty distribution and provide no mechanism for using risk-related covariates to inform individuals' frailties. We propose a Bayesian hierarchical approach that models frailty with a mixture of compound Poisson distributions by classifying participants into latent risk groups using covariate data. Individuals within a class share a frailty distribution. The model also allows that some participants have no risk of an event. We apply the proposed model to data from a recently completed HIV prevention trial to estimate individual-level intervention effectiveness as well as the effect of covariates on probability and magnitude of risk.