A new latent cure rate marker model for survival data
Sungduk Kim, Yingmei Xi, and Ming-Hui Chen
Source: Ann. Appl. Stat. Volume 3, Number 3
(2009), 1124-1146.
Abstract
To address an important risk classification issue that arises in clinical practice, we propose a new mixture model via latent cure rate markers for survival data with a cure fraction. In the proposed model, the latent cure rate markers are modeled via a multinomial logistic regression and patients who share the same cure rate are classified into the same risk group. Compared to available cure rate models, the proposed model fits better to data from a prostate cancer clinical trial. In addition, the proposed model can be used to determine the number of risk groups and to develop a predictive classification algorithm.
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Keywords: Deviance Information Criterion (DIC); Markov chain Monte Carlo; logarithm of pseudomarginal likelihood (LPML); PSA recurrence
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Permanent link to this document: http://projecteuclid.org/euclid.aoas/1254773281
Digital Object Identifier: doi:10.1214/09-AOAS238
Zentralblatt MATH identifier: 05758454
Mathematical Reviews number (MathSciNet): MR2750389
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