Estimators of Odds Ratio Regression Parameters in Matched Case-Control Studies with Covariate Measurement Error
研究了在精细匹配的病例对照研究中，当主要暴露变量存在测量误差时，比值比回归参数的几种估计方法，包括偏差校正、函数型和变换估计量，并比较了它们的渐近和小样本性质。
Abstract This article studies estimators of the odds ratio and odds ratio regression parameters in finely matched case-control studies containing a binary exposure of primary interest and subject-specific covariates that are subject to measurement error. A retrospective logistic regression model for the binary exposure variable is used. The effect of measurement errors on the conditional maximum likelihood estimator is determined. Three alternatives are considered: bias-corrected, functional, and "transformation" estimators. The asymptotic and small-sample properties of the three competitors are studied. The results are illustrated using data from a case-control study of diet and colon cancer. Key Words: Bias correctionConditional maximum likelihoodErrors in variablesLogistic regressionRetrospective study