Doubly robust estimation and sensitivity analysis with outcomes truncated by death in multi-arm clinical trials

TL;DR

This paper develops new statistical methods for estimating treatment effects in multi-arm clinical trials where outcomes are truncated by death, addressing challenges of causal inference with complex survival data.

Contribution

It extends principal stratification and doubly robust estimation techniques to multi-arm trials with outcome truncation, including sensitivity analysis for causal assumptions.

Findings

Proposed simple weighting and regression estimators for survivor causal effects.

Derived efficient influence functions for doubly robust estimators.

Validated methods through extensive simulations and real data application.

Abstract

In clinical trials, the observation of participant outcomes may frequently be hindered by death, leading to ambiguity in defining a scientifically meaningful final outcome for those who die. Principal stratification methods are valuable tools for addressing the average causal effect among always-survivors, i.e., the average treatment effect among a subpopulation defined as those who would survive regardless of treatment assignment. Although robust methods for the truncation-by-death problem in two-arm clinical trials have been previously studied, its expansion to multi-arm clinical trials remains elusive. In this article, we study the identification of a class of survivor average causal effect estimands with multiple treatments under monotonicity and principal ignorability, and first propose simple weighting and regression approaches for point estimation. As a further improvement, we derive the efficient influence function to motivate doubly robust estimators for the survivor average causal effects in multi-arm clinical trials. We also propose sensitivity methods under violations of key causal assumptions. Extensive simulations are conducted to investigate the finite-sample performance of the proposed methods against the existing methods, and a real data example is used to illustrate how to operationalize the proposed estimators and the sensitivity methods in practice.