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

2024-10-09

AI Summary

This study addresses the causal identification challenge arising from truncation-by-death in multi-arm clinical trials, focusing on estimating the average treatment effect among “always-survivors.” We extend the doubly robust estimation framework to the multi-arm setting for the first time, proposing a multi-arm doubly robust estimator based on the efficient influence function and developing a sensitivity analysis method to test violations of monotonicity and principal ignorability assumptions. The approach integrates principal stratification, inverse probability weighting, and regression adjustment. Simulation studies demonstrate that the proposed estimator achieves low bias and high efficiency even in small samples. Empirical analysis on real clinical data confirms its feasibility and robustness. Our core contributions include: (i) theoretical innovation—establishing doubly robust inference for multi-arm trials under truncation-by-death; (ii) methodological advancement—constructing an efficient estimator coupled with principled sensitivity analysis; and (iii) empirical validation—demonstrating practical applicability in realistic clinical settings.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

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 in the principal strata of 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 unknown. 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. As a further improvement, we then derive the efficient influence function to motivate doubly robust estimators for the survivor average causal effects in multi-arm clinical trials. We also articulate sensitivity methods under violations of key causal assumptions. Extensive simulations are conducted to investigate the finite-sample performance of the proposed methods, and a real data example is used to illustrate how to operationalize the proposed estimators and the sensitivity methods in practice.

Problem

• Estimating causal effects when outcomes are truncated by death
• Extending doubly robust methods to multi-arm clinical trials
• Addressing survivor average causal effects under principal stratification

Innovation

• Doubly robust estimation for multi-arm trials
• Sensitivity analysis for causal assumption violations
• Principal stratification with monotonicity and ignorability