Causal Inference with Outcomes Truncated by Death and Missing Not at Random

TL;DR

This paper addresses challenges in causal inference in clinical trials with outcomes truncated by death and missing data, proposing new identification, estimation, and bounding methods under complex missingness mechanisms.

Contribution

It introduces a novel approach using a proxy variable for identifying causal effects in the presence of non-random missing outcomes due to death.

Findings

Method successfully estimates causal effects with simulated data

Provides bounds when assumptions are violated

Demonstrates applicability on HIV study data

Abstract

In clinical trials, principal stratification analysis is commonly employed to address the issue of truncation by death, where a subject dies before the outcome can be measured. However, in practice, many survivor outcomes may remain uncollected or be missing not at random, posing a challenge to standard principal stratification analyses. In this paper, we explore the identification, estimation, and bounds of the average treatment effect within a subpopulation of individuals who would potentially survive under both treatment and control conditions. We show that the causal parameter of interest can be identified by introducing a proxy variable that affects the outcome only through the principal strata, while requiring that the treatment variable does not directly affect the missingness mechanism. Subsequently, we propose an approach for estimating causal parameters and derive nonparametric bounds in cases where identification assumptions are violated. We illustrate the performance of the proposed method through simulation studies and a real dataset obtained from a Human Immunodeficiency Virus (HIV) study.