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

arXiv (Cornell University)

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.

Authors 3

1. Wei Li

   Renmin University of China

   Affiliation as printed

   Center for Applied Statistics and School of Statistics , Renmin University of China

2. Shanshan Luo Aachen Mathematical Institute

   Leiden University · Beijing Technology and Business University

   Affiliation as printed

   Mathematical Institute , Leiden University

   School of Mathematics and Statistics , Beijing Technology and Business University

3. Zhi Geng Aachen Mathematical Institute

   Leiden University · Beijing Technology and Business University

   Affiliation as printed

   Mathematical Institute , Leiden University

   School of Mathematics and Statistics , Beijing Technology and Business University