Journal
BIOMETRIKA
Volume 96, Issue 3, Pages 617-633Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asp027
Keywords
Augmented estimator; Biased sampling data; Embedding missing data; Left-truncation; Martingale structure; Right censoring; U-statistic
Funding
- U.S. National Institutes of Health
- U.S. National Institute on Aging
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By embedding the missing covariate data into a left-truncated and right-censored survival model, we propose a new class of weighted estimating functions for the Cox regression model with missing covariates. The resulting estimators, called the pseudo-partial likelihood estimators, are shown to be consistent and asymptotically normal. A simulation study demonstrates that, compared with the popular inverse-probability weighted estimators, the new estimators perform better when the observation probability is small and improve efficiency of estimating the missing covariate effects. Application to a practical example is reported.
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