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Biometrika 2002 89(4):905-916; doi:10.1093/biomet/89.4.905
© 2002 by Biometrika Trust
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Semiparametric inference in matched case-control studies with missing covariate data

Paul J.Rathouz1, Glen A.Satten2 and Raymond J.Carroll3

1 Department of Health Studies, University of Chicago, 5841 South Maryland Avenue, MC2007, Chicago, Illinois 60637, U.S.Aprathouz{at}health.bsd.uchicago.edu 2 Centers for Disease Control and Prevention, Atlanta, Georgia 30333, U.S.A.gsatten{at}cdc.gov 3 Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A. carroll{at}stat.tamu.edu

We consider the problem of matched studies with a binary outcome that are analysed using conditional logistic regression, and for which data on some covariates are missing for some study participants.Methods for this problem involve either modelling the distribution of missing covariates or modelling the probability of data being missing. For this second approach, the previously proposed method did not make use of data for those persons with missing covariate data except in the model for the missingness. We propose a new class of estimators that use outcome and available covariate data for all study participants, and show that a particular member of this class always has better efficiency than the previously proposed estimator. We illustrate the efficiency gains that are possible with our approach using simulated data.

Key Words: Case-control study; Conditional inference; Estimating equation; Missing data; Projection; Robustness; Semiparametric; Two-stage study

Received November 2000. Revised November 2001


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