Clustering Survival Outcomes using Dirichlet Process Mixture

Motivated by the national evaluation of mortality rates at kidney transplant centers in the United States, we sought to assess transplant center longterm survival outcomes by applying a methodology developed in Bayesian non-parametrics literature. We described a Dirichlet process model and a Dirichlet process mixture model with a Half-Cauchy for the estimation of the riskadjusted effects of the transplant centers. To improve the model performance and interpretability, we centered the Dirichlet process. We also proposed strategies to increase model’s classification ability. Finally we derived statistical measures and created graphical tools to rate transplant centers and identify outlying centers with exceptionally good or poor performance. The proposed method was evaluated through simulation, and then applied to assess kidney transplant centers from a national organ failure registry. Clustering Survival Outcomes using Dirichlet Process Mixture Lili Zhao , Jingchunzi Shi , Tempie H. Shearon , and Yi Li * Department of Biostatistics, University of Michigan, Ann Arbor