Non- and semi-parametric analysis of failure time data with missing failure indicators

TL;DR

This paper develops new semi-parametric methods for analyzing failure time data with missing failure indicators, providing estimators that are consistent, asymptotically normal, and more efficient than traditional methods.

Contribution

It introduces a class of estimating functions for Cox model parameters with missing failure statuses, including an adaptive estimator achieving minimum variance.

Findings

Adaptive estimator outperforms complete-case analysis in efficiency.

Numerical studies confirm the accuracy of asymptotic approximations.

Methods extend to nonparametric survival function estimation.

Abstract

A class of estimating functions is introduced for the regression parameter of the Cox proportional hazards model to allow unknown failure statuses on some study subjects. The consistency and asymptotic normality of the resulting estimators are established under mild conditions. An adaptive estimator which achieves the minimum variance-covariance bound of the class is constructed. Numerical studies demonstrate that the asymptotic approximations are adequate for practical use and that the efficiency gain of the adaptive estimator over the complete-case analysis can be quite substantial. Similar methods are also developed for the nonparametric estimation of the survival function of a homogeneous population and for the estimation of the cumulative baseline hazard function under the Cox model.