具有缺失协变量的治愈率模型的极大似然方法。

Maximum likelihood methods for cure rate models with missing covariates.

作者信息

Chen M H, Ibrahim J G

机构信息

Department of Mathematical Sciences, Worcester Polytechnic Institute, Massachusetts 01609, USA.

出版信息

Biometrics. 2001 Mar;57(1):43-52. doi: 10.1111/j.0006-341x.2001.00043.x.

DOI:10.1111/j.0006-341x.2001.00043.x

PMID:11252617

Abstract

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.

摘要

我们提出了用于一类具有治愈比例的新型半参数生存模型参数估计的最大似然方法，其中协变量允许缺失。我们允许协变量为分类变量或连续变量，并为协变量指定一个参数分布，该分布写成一维条件分布序列。我们提出了一种用于最大似然估计的新型期望最大化（EM）算法，并使用路易斯公式（Louis，1982年，《皇家统计学会杂志》，B辑44卷，226 - 233页）推导标准误差。讨论并实现了使用蒙特卡罗EM算法的计算技术。详细研究了一个涉及黑色素瘤癌症临床试验的真实数据集，以证明该方法。