Likelihood methods for missing covariate data in highly stratified studies
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2003 · Volume 65 · Issue 3
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.
Keywords
Conditional likelihood, Conditional logistic regression, Fixed effects, Matched case–control, Missing data, Nuisance parameter, Semiparametric efficiency
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