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Covariate Measurement Error Adjustment for Matched Case–Control Studies
Author(s) -
McShane Lisa M.,
Midthune Douglas N.,
Dorgan Joanne F.,
Freedman Laurence S.,
Carroll Raymond J.
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.00062.x
Subject(s) - covariate , statistics , resampling , observational error , standard error , mathematics , econometrics
Summary. We propose a conditional scores procedure for obtaining bias‐corrected estimates of log odds ratios from matched case‐control data in which one or more covariates are subject to measurement error. The approach involves conditioning on sufficient statistics for the unobservable true covariates that are treated as fixed unknown parameters. For the case of Gaussian nondifferential measurement error, we derive a set of unbiased score equations that can then be solved to estimate the log odds ratio parameters of interest. The procedure successfully removes the bias in naive estimates, and standard error estimates are obtained by resampling methods. We present an example of the procedure applied to data from a matched case–control study of prostate cancer and serum hormone levels, and we compare its performance to that of regression calibration procedures.