Generalized estimating equation model for binary outcomes with missing covariates.
This paper presents an approach to handling missing covariates in the generalized estimating equation (GEE) model for binary outcomes when the probability of missingness depends on the observed outcomes and covariates. The proposed method is to replace the missing quantities in the estimating function with consistent estimates. In special cases, the proposed model reduces to a weighted GEE model for the completely observed units, where the weight is the inverse of the probability of missingness. Our method can be viewed as an extension of the mean score method by Reilly and Pepe (1995, Biometrika 82, 299-314) to the GEE context. Under certain regularity conditions, the estimates of the regression coefficients obtained by the proposed method are consistent and asymptotically normally distributed. The finite sample properties of the estimates are illustrated via computer simulations. An application to the study of dementia among stroke patients is presented.
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Department of Clinical Statistics and Data Management, Wyeth-Lederle Vaccines and Pediatrics, Pearl River, New York 10965, USA.
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D000368:Aged; D001699:Biometry / Q000379:methods*; D002545:Brain Ischemia / Q000503:physiopathology / Q000523:psychology*; D003198:Computer Simulation; D003704:Dementia / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology; D004522:Educational Status; D006801:Humans; D008609:Mental Status Schedule; D015233:Models, Statistical; D011336:Probability; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors