Mixture Modeling for Joint Analysis of Survival Process, Discrete, and Continuous Covariates

• Activity Number: 412
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Fu-Wen Liang*+ and Wenyaw Chan and Carol Etzel and Bouthaina S. Dabaja and Michael D. Swartz
• Companies: MD Anderson Cancer Center and The University of Texas at Houston and MD Anderson Cancer Center and MD Anderson Cancer Center and The University of Texas at Houston
• Keywords: Joint model ; Survival analysis ; Competing risk ; Latent class ; Mixture modeling

Abstract

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time to event data. However, the commonly used mixture survival model assumes that the effects of the covariates involved in failure differ across latent classes, but the covariate distribution is homogeneous. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of survival process, discrete, and continuous covariates. The unobservable survival trajectories are identified through estimating the probability that a subject belong to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model.