Product-limit Estimators and Cox Regression with Missing Cause-of-failure Information
نویسندگان
چکیده
The Kaplan{Meier estimator of a survival function is used when cause of failure (censored or non-censored) is always observed. A method of survival function estimation is developed under the assumption that the failure indicators are missing completely at random (MCR). The resulting estimator is a smooth functional of the Nelson{Aalen estimators of certain cumulative transition intensities. The asymptotic properties of this estimator are derived. A simulation study shows that the proposed estimator has greater eeciency than competing MCR-based estimators. The approach is extended to the Cox model setting for the estimation of a conditional survival function given a covariate.
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