Likelihood methods for missing covariate data in highly stratified studies

Paul J. Rathouz

Journal of the Royal Statistical Society Series B, 2003, vol. 65, issue 3, 711-723

Abstract: Summary. The paper considers canonical link generalized linear models with stratum‐specific nuisance intercepts and missing covariate data. This family includes the conditional logistic regression model. Existing methods for this problem, each of which uses a conditioning argu‐ ment to eliminate the nuisance intercept, model either the missing covariate data or the missingness process. The paper compares these methods under a common likelihood framework. The semiparametric efficient estimator is identified, and a new estimator, which reduces dependence on the model for the missing covariate, is proposed. A simulation study compares the methods with respect to efficiency and robustness to model misspecification.

Date: 2003
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Citations: View citations in EconPapers (5)

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