将期望最大化（EM）算法用于具有不完全分类协变量的生存数据。

Using the EM-algorithm for survival data with incomplete categorical covariates.

作者信息

Lipsitz S R, Ibrahim J G

机构信息

Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.

出版信息

Lifetime Data Anal. 1996;2(1):5-14. doi: 10.1007/BF00128467.

DOI:10.1007/BF00128467

PMID:9384645

Abstract

Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With generalized linear models, when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights proposed in Ibrahim (1990). In this article, we extend the EM by the method of weights to survival outcomes whose distributions may not fall in the class of generalized linear models. This method requires the estimation of the parameters of the distribution of the covariates. We present a clinical trials example with five covariates, four of which have some missing values.

摘要

在许多以生存时间为结局的研究中，协变量数据不完整是很常见的情况。对于广义线性模型，当缺失的协变量是分类变量时，一种获取参数估计的有用技术是Ibrahim（1990）提出的加权期望最大化（EM）方法。在本文中，我们将加权期望最大化方法扩展到分布可能不属于广义线性模型类别的生存结局。该方法需要估计协变量分布的参数。我们给出一个有五个协变量的临床试验例子，其中四个协变量有一些缺失值。