Survival Estimation for Missing not at Random Censoring Indicators based on Copula Models

• URL: http://arxiv.org/abs/2009.01726v2
• Date: Thu, 14 Sep 2023 11:51:06 GMT
• Title: Survival Estimation for Missing not at Random Censoring Indicators based on Copula Models
• Authors: Mikael Escobar-Bach and Olivier Goudet
• Abstract summary: We provide a new estimator for the conditional survival function with missing not at random (MNAR) censoring indicators based on a conditional copula model for the missingness mechanism. In addition to the theoretical results, we illustrate how the estimators work for small samples through a simulation study and show their practical applicability by analyzing synthetic and real data.
• Score: 1.52292571922932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the presence of right-censored data with covariates, the conditional Kaplan-Meier estimator (also known as the Beran estimator) consistently estimates the conditional survival function of the random follow-up for the event of interest. However, a necessary condition is the unambiguous knowledge of whether each individual is censored or not, which may be incomplete in practice. We therefore propose a study of the Beran estimator when the censoring indicators are generic random variables and discuss necessary conditions for the efficiency of the Beran estimator. From this, we provide a new estimator for the conditional survival function with missing not at random (MNAR) censoring indicators based on a conditional copula model for the missingness mechanism. In addition to the theoretical results, we illustrate how the estimators work for small samples through a simulation study and show their practical applicability by analyzing synthetic and real data.

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