Mediation Analysis with the Mediator and Outcome Missing Not at Random

• 成果类型: Article
• 署名作者: Zuo, Shuozhi; Ghosh, Debashis; Ding, Peng; Yang, Fan
• 署名单位: Colorado School of Public Health; University of California System; University of California Berkeley; Tsinghua University; Yanqi Lake Beijing Institute of Mathematical Sciences & Applications
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2359132
• 发表日期: 2025
• 页码: 794-804
• 关键词: instrumental variables; causal; Identifiability; identification; models; noncompliance; bounds; spss; sas

摘要

Mediation analysis is widely used for investigating direct and indirect causal pathways through which an effect arises. However, many mediation analysis studies are challenged by missingness in the mediator and outcome. In general, when the mediator and outcome are missing not at random, the direct and indirect effects are not identifiable without further assumptions. We study the identifiability of the direct and indirect effects under some interpretable mechanisms that allow for missing not at random in the mediator and outcome. We evaluate the performance of statistical inference under those mechanisms through simulation studies and illustrate the proposed methods via the National Job Corps Study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.