Maximum likelihood methods for cure rate models with missing covariates.
We propose maximum likelihood methods for parameter estimation for a novel class of semiparametric survival models with a cure fraction, in which the covariates are allowed to be missing. We allow the covariates to be either categorical or continuous and specify a parametric distribution for the covariates that is written as a sequence of one-dimensional conditional distributions. We propose a novel EM algorithm for maximum likelihood estimation and derive standard errors by using Louis's formula (Louis, 1982, Journal of the Royal Statistical Society, Series B 44, 226-233). Computational techniques using the Monte Carlo EM algorithm are discussed and implemented. A real data set involving a melanoma cancer clinical trial is examined in detail to demonstrate the methodology.
Department of Mathematical Sciences, Worcester Polytechnic Institute, Massachusetts 01609, USA.
M H
MH
Chen
J G
JG
Ibrahim
D000465:Algorithms; D001699:Biometry; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D007372:Interferons / Q000627:therapeutic use; D016013:Likelihood Functions; D008545:Melanoma / Q000188:drug therapy; D015233:Models, Statistical; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D016019:Survival Analysis; D015996:Survival Rate
D007372:Interferons