Conditional and unconditional categorical regression models with missing covariates

被引：32
|
作者：
Satten, GA [1 ]
Carroll, RJ
机构：
[1] Ctr Dis Control & Prevent, Atlanta, GA 30333 USA
[2] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA
[3] Texas A&M Univ, Dept Biostat & Epidemiol, College Stn, TX 77843 USA
[4] Univ Penn, Dept Biostat & Epidemiol, Philadelphia, PA 19104 USA
来源：
BIOMETRICS | 2000年 / 56卷 / 02期
关键词：
case-control study; endometrial cancer; likelihood; matching; missing at random; missing data; two-stage sample;
D O I：
10.1111/j.0006-341X.2000.00384.x
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
We consider methods for analyzing categorical regression models when some covariates (Z) are completely observed but other covariates (X) are missing for some subjects. When data on X are missing at random (i.e., when the probability that X is observed does not depend on the value of X itself), we present a likelihood approach for the observed data that allows the same nuisance parameters to be eliminated in a conditional analysis as when data are complete. An example of a matched case-control study is used to demonstrate our approach.
引用
收藏
页码：384 / 388
页数：5
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