Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation

TL;DR

This paper develops doubly robust estimators for time-to-event analysis under covariate-induced dependent left truncation, addressing bias from dependence between truncation and event times.

Contribution

It introduces the first doubly robust estimators for left-truncated survival data, extending semiparametric theory to this setting.

Findings

Estimators are shown to be doubly robust through theoretical analysis.

Simulation studies demonstrate the estimators' favorable performance.

Application to real data illustrates practical utility.

Abstract

In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of time-to-event, conventional methods adjusting for left truncation tend to rely on the (quasi-)independence assumption that the truncation time and the event time are "independent" on the observed region. This assumption is violated when there is dependence between the truncation time and the event time possibly induced by measured covariates. Inverse probability of truncation weighting leveraging covariate information can be used in this case, but it is sensitive to misspecification of the truncation model. In this work, we apply the semiparametric theory to find the efficient influence curve of an expected (arbitrarily transformed) survival time in the presence of covariate-induced dependent left truncation. We then use it to construct estimators that are shown to enjoy double-robustness properties. Our work represents the first attempt to construct doubly robust estimators in the presence of left truncation, which does not fall under the established framework of coarsened data where doubly robust approaches are developed. We provide technical conditions for the asymptotic properties that appear to not have been carefully examined in the literature for time-to-event data, and study the estimators via extensive simulation. We apply the estimators to two data sets from practice, with different right-censoring patterns.