Bayesian Semiparametric Analysis of Semi-Competing Risks Data

• Activity Number: 593
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Kyu Ha Lee *+ and Sebastien Haneuse and Deborah Schrag and Francesca Dominici
• Companies: Harvard School of Public Health and Harvard School of Public Health and Dana-Farber Cancer Institute and Harvard School of Public Health
• Keywords: Bayesian survival analysis ; Reversible jump Markov chain Monte Carlo ; Shared frailty ; Truncation by death

Abstract

The Centers for Medicare and Medicaid Services currently uses 30-day readmission as a proxy outcome for quality of care for a number of adverse health conditions. The study of 30-day readmission among individuals recently diagnosed with pancreatic cancer is limited, however, by their extremely poor survival. The study of predictors of readmission among individuals recently diagnosed with pancreatic cancer, therefore, must acknowledge death as a truncation mechanism, a problem known as the `semi-competing risks' problem. In this paper, we propose a Bayesian semi-parametric regression model for semi-competing risks data. Specifically, an illness-death model is adopted to represent three transitions for pancreatic cancer patients: (1) discharge to readmission, (2) discharge to death, and (3) readmission to death. Dependence between the two event times is induced via a subject-specific shared frailty. For each of three hazard functions, the log-baseline hazard function is modeled as a mixture of piecewise constant functions, defined on separate time partitions. Model parameters are jointly formulated and estimated via a Metropolis-Hastings-Green algorithm.