A Randomization-Based Method for Evaluating Time-Varying Treatment Effects

TL;DR

This paper introduces new randomization-based statistical tests that analyze how treatment effects on paired censored outcomes vary over time, providing both time-specific and overall insights, with applications to aging and social engagement data.

Contribution

It develops novel time-specific and long-term randomization-based tests for censored outcomes, incorporating sensitivity analysis for unmeasured confounding in observational studies.

Findings

New tests enable detailed time-varying treatment effect analysis.

Application to aging data illustrates the method's practical utility.

Sensitivity analysis addresses unmeasured confounding concerns.

Abstract

Tests for paired censored outcomes have been extensively studied, with some justified in the context of randomization-based inference. These tests are primarily designed to detect an overall treatment effect across the entire follow-up period, providing limited insight into when the effect manifests and how it changes over time. In this article, we introduce new randomization-based tests for paired censored outcomes that enable both time-specific and long-term analysis of a treatment effect. The tests utilize time-specific scores, quantifying each individual's impact on sample survival at a fixed time, obtained via pseudo-observations. Moreover, we develop corresponding sensitivity analysis methods to address potential unmeasured confounding in observational studies where randomization often lacks support. To illustrate how our methods can provide a fuller analysis of a time-varying treatment effect, we apply them to a matched cohort study using data from the Korean Longitudinal Study of Aging (KLoSA), focusing on the effect of social engagement on survival.