Bayesian Nonparametric Survival Regression Modeling and Inference: A Tale of Handling Survival Curves That Cross

• Activity Number: 8
• Type: Invited
• Date/Time: Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
• Sponsor: WNAR
• Author(s): Gary L. Rosner*+ and Wesley O. Johnson and Maria DeIorio and Peter Müller
• Companies: The University of Texas M.D. Anderson Cancer Center and University of California, Irvine and Imperial College and The University of Texas M.D. Anderson Cancer Center
• Keywords: Dependent Dirichlet Process ; Survival analysis

Abstract

We develop a Dependent Dirichlet Process model for survival analysis data. The model extends the ANOVA DDP that was presented in De Iorio et al. (JASA, 2004) to handle continuous covariates and censored data. A major feature of the work is that there is no necessity for resulting survival curve estimates to satisfy the ubiquitous proportional hazards assumption. An illustration based on a cancer clinical trial is given where survival probabilities for times early in the study are estimated to be lower for those on a high dose treatment regimen than for those on the low dose treatment, while the reverse is true later for later times, possibly due to the toxic effect of the high dose for those who are not as healthy at the beginning of the study.