Open AccessPseudo-partial likelihood estimators for the Cox regression model with missing covariates
Author(s) -
Xiaodong Luo,
Wei Yann Tsai,
Qiang Xu
Publication year - 2009
Publication title -
biometrika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.307
H-Index - 122
eISSN - 1464-3510
pISSN - 0006-3444
DOI - 10.1093/biomet/asp027
Subject(s) - covariate , estimator , missing data , mathematics , statistics , proportional hazards model , regression analysis , inverse probability , econometrics , m estimator , inverse probability weighting , bayesian probability , posterior probability
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.