A new threshold regression model for survival data with a cure fraction.
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.
Kim
Sungduk
S
Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Rockville, MD 20852, USA. kims2@mail.nih.gov
Chen
Ming-Hui
MH
Dey
Dipak K
DK
D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D018572:Disease-Free Survival; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D011471:Prostatic Neoplasms / Q000276:immunology / Q000401:mortality* / Q000628:therapy; D012044:Regression Analysis; D016019:Survival Analysis