An estimated-score approach for dealing with missing covariate data in matched case-control studies

被引:0
作者
Sinha, Samiran [1 ]
机构
[1] Texas A&M Univ, Dept Stat, College Stn, TX 77845 USA
来源
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2010年 / 38卷 / 04期
关键词
Conditional likelihood; efficiency; empirical distribution; influence function; log odds ratio; missing data; INDICATOR METHOD; EXPOSURE; REGRESSION; MODELS;
D O I
10.1002/cjs.10081
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Matched case-control designs are commonly used in epidemiological studies for estimating the effect of exposure variables on the risk of a disease by controlling the effect of confounding variables Due to retrospective nature of the study information on a covariate could be missing for some subjects A straightforward application of the conditional logistic likelihood for analyzing matched case-control data with the partially missing covariate may yield inefficient estimators of the parameters A robust method has been proposed to handle this problem using an estimated conditional score approach when the missingness mechanism does not depend on the disease status Within the conditional logistic likelihood framework an empirical procedure is used to estimate the odds of the disease for the subjects with missing covariate values The asymptotic distribution and the asymptotic variance of the estimator when the matching variables and the completely observed covariates are categorical The finite sample performance of the proposed estimator is assessed through a simulation study Finally the proposed method has been applied to analyze two matched case-control studies The Canadian Journal of Statistics 38 680-697 2010 (C) 2010 Statistical Society of Canada
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页码:680 / 697
页数:18
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