Covariate measurement error adjustment for matched case-control studies

We propose a conditional scores procedure for obtaining bias-corrected esti mates of log odds ratios from matched case-control data in which one or mor e covariates are subject to measurement error. The approach involves condit ioning on sufficient statistics for the unobservable true covariates that a re treated as fixed unknown parameters. For the case of Gaussian nondiffere ntial measurement error, we derive a set of unbiased score equations that c an then be solved to estimate the log odds ratio parameters of interest. Th e procedure successfully removes the bias in naive estimates, and standard error estimates are obtained by resampling methods. We present an example o f the procedure applied to data from a matched case-control study of prosta te cancer and serum hormone levels, and we compare its performance to that of regression calibration procedures.