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Augmented Inverse Probability Weighted Estimator for Cox Missing Covariate Regression
Author(s) -
Wang C. Y.,
Chen Hua Yun
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.2001.00414.x
Subject(s) - covariate , estimator , statistics , mathematics , proportional hazards model , inverse probability , joint probability distribution , conditional probability distribution , posterior probability , bayesian probability
Summary. This article investigates an augmented inverse selection probability weighted estimator for Cox regression parameter estimation when covariate variables are incomplete. This estimator extends the Horvitz and Thompson (1952, Journal of the American Statistical Association 47 , 663–685) weighted estimator. This estimator is doubly robust because it is consistent as long as either the selection probability model or the joint distribution of covariates is correctly specified. The augmentation term of the estimating equation depends on the baseline cumulative hazard and on a conditional distribution that can be implemented by using an EM‐type algorithm. This method is compared with some previously proposed estimators via simulation studies. The method is applied to a real example.