A Dirichlet process mixture model for survival outcome data: assessing nationwide kidney transplant centers. (26th January 2015)

• Record Type: Journal Article
• Authors: Zhao, Lili; Shi, Jingchunzi; Shearon, Tempie H.; Li, Yi
• Is Part Of: Statistics in medicine. Volume 34:Number 8(2015)
• Page Start: 1404
• Page End: 1416
• Publication Date: 2015-01-26
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6438
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim6438-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6438-para-0001">Mortality rates are probably the most important indicator for the performance of kidney transplant centers. Motivated by the national evaluation of mortality rates at kidney transplant centers in the USA, we seek to categorize the transplant centers based on the mortality outcome. We describe a Dirichlet process model and a Dirichlet process mixture model with a half‐cauchy prior for the estimation of the risk‐adjusted effects of the transplant centers, with strategies for improving the model performance, interpretability, and classification ability. We derive statistical measures and create graphical tools to rate transplant centers and identify outlying groups of 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. Copyright © 2015 John Wiley &amp; Sons, Ltd.</p> </abstract>