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Augmented Inverse Probability Weighted Estimator for Cox Missing Covariate Regression

C. Y. Wang and Hua Yun Chen
Biometrics
Vol. 57, No. 2 (Jun., 2001), pp. 414-419
Stable URL: http://www.jstor.org/stable/3068347
Page Count: 6
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Augmented Inverse Probability Weighted Estimator for Cox Missing Covariate Regression
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Abstract

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.

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