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A new threshold regression model for survival data with a cure fraction
Author(s) -
Sungduk Kim,
MingHui Chen,
Dipak K. Dey
Publication year - 2010
Publication title -
lifetime data analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.677
H-Index - 46
eISSN - 1572-9249
pISSN - 1380-7870
DOI - 10.1007/s10985-010-9166-9
Subject(s) - markov chain monte carlo , fraction (chemistry) , sampling (signal processing) , markov chain , bayesian probability , prostate cancer , gibbs sampling , statistics , population , computer science , cancer , medicine , mathematics , chemistry , environmental health , organic chemistry , filter (signal processing) , computer vision
Due to the fact that certain fraction of the population suffering a particular type of disease get cured because of advanced medical treatment and health care system, we develop a general class of models to incorporate a cure fraction by introducing the latent number N of metastatic-competent tumor cells or infected cells caused by bacteria or viral infection and the latent antibody level R of immune system. Various properties of the proposed models are carefully examined and a Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian computation for model fitting and comparison. A real data set from a prostate cancer clinical trial is analyzed in detail to demonstrate the proposed methodology.

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