On the Role of Volterra Integral Equations in Self-Consistent, Product-Limit, Inverse Probability of Censoring Weighted, and Redistribution-to-the-Right Estimators for the Survival Function

TL;DR

This paper explores how Volterra integral equations underpin various nonparametric survival estimators in censored data, providing new insights and simpler proofs for their interconnections and historical development.

Contribution

It reveals the fundamental role of Volterra integral equations in linking and simplifying key survival estimators, including rediscovery of classical methods and their implicit connections.

Findings

Volterra integral equations connect different survival estimators.

Product-limit estimator can be derived via integral equations.

Inverse probability weighting is linked to classical estimators.

Abstract

This paper reconsiders several results of historical and current importance to nonparametric estimation of the survival distribution for failure in the presence of right-censored observation times, demonstrating in particular how Volterra integral equations of the first kind help inter-connect the resulting estimators. The paper begins by considering Efron's self-consistency equation, introduced in a seminal 1967 Berkeley symposium paper. Novel insights provided in the current work include the observations that (i) the self-consistency equation leads directly to an anticipating Volterra integral equation of the first kind whose solution is given by a product-limit estimator for the censoring survival function; (ii) a definition used in this argument immediately establishes the familiar product-limit estimator for the failure survival function; (iii) the usual Volterra integral equation for the product-limit estimator of the failure survival function leads to an immediate and simple proof that it can be represented as an inverse probability of censoring weighted estimator (i.e., under appropriate conditions). Finally, we show that the resulting inverse probability of censoring weighted estimators, attributed to a highly influential 1992 paper of Robins and Rotnitzky, were implicitly introduced in Efron's 1967 paper in its development of the redistribution-to-the-right algorithm. All results developed herein allow for ties between failure and/or censored observations.