Estimating treatment effects from observational data under truncation by death using survival-incorporated quantiles
Qingyan Xiang, Paola Sebastiani, Thomas Perls, Stacy L. Andersen,, Svetlana Ukraintseva, Mikael Thinggaard, Judith J. Lok

TL;DR
This paper introduces a novel IPTW-based estimator for survival-incorporated quantiles to address truncation by death in observational studies, demonstrating reduced variance and practical application to cognitive effects of statins.
Contribution
It proposes the first IPTW quantile estimator using estimated propensity scores that achieves lower asymptotic variance than using true scores, with extensive simulations and real data application.
Findings
Estimated no significant cognitive difference between statin users and non-users.
Using estimated propensity scores reduces root mean square error.
Survival-incorporated quantiles effectively summarize treatment effects with death considered.
Abstract
The issue of "truncation by death" commonly arises in clinical research: subjects may die before their follow-up assessment, resulting in undefined clinical outcomes. To address this issue, we focus on survival-incorporated quantiles -- quantiles of a composite outcome combining death and clinical outcomes -- to summarize the effect of treatment. Using inverse probability of treatment weighting (IPTW), we propose an estimator for survival-incorporated quantiles from observational data, applicable to settings of both point treatment and time-varying treatments. We establish consistency and asymptotic normality of the estimator under both the true and estimated propensity scores. While the variance properties of IPTW estimators for the mean have been studied, to our knowledge, this article is the first to show that the IPTW quantile estimator using the estimated propensity score yields…
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Taxonomy
TopicsLipoproteins and Cardiovascular Health
