Maximum likelihood methods for cure rate models with missing covariates

被引：62

作者：

Chen, MH ^{[1
]}

Ibrahim, JG

机构：

[1] Worcester Polytech Inst, Dept Math Sci, Worcester, MA 01609 USA

[2] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA

[3] Dana Farber Canc Inst, Boston, MA 02115 USA

来源：

BIOMETRICS | 2001年 / 57卷 / 01期

关键词：

cure rate model; ENT algorithm; Gibbs sampling; latent variables; missing data;

D O I：

10.1111/j.0006-341X.2001.00043.x

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：43 / 52

页数：10

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