A new latent cure rate marker model for survival data. (English) Zbl 1196.62142

Summary: 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.


62P10 Applications of statistics to biology and medical sciences; meta analysis
62J12 Generalized linear models (logistic models)
92C50 Medical applications (general)
62H30 Classification and discrimination; cluster analysis (statistical aspects)
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