ArticleEpidemiology (Cambridge, Mass.)2019
Estimation of Natural Indirect Effects Robust to Unmeasured Confounding and Mediator Measurement Error.
Article in Epidemiology (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Health Insurance as a Mediator of Neighborhood Deprivation and Pediatric Cancer Survival: An Analysis of State Cancer Registry Data.Cancer medicine · 2026Article
- Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.Annual review of statistics and its application · 2025Article
- INTEGRATING MENDELIAN RANDOMIZATION WITH CAUSAL MEDIATION ANALYSES FOR CHARACTERIZING DIRECT AND INDIRECT EXPOSURE-TO-OUTCOME EFFECTS.The annals of applied statistics · 2024Article
- Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Mediation analysis in the presence of continuous exposure measurement error.Statistics in medicine · 2023Article
- To Adjust or Not to Adjust? When a "Confounder" Is Only Measured After Exposure.Epidemiology (Cambridge, Mass.) · 2021Article
- Distinguishing Causation From Correlation in the Use of Correlates of Protection to Evaluate and Develop Influenza Vaccines.American journal of epidemiology · 2020Review
- Robust inference on population indirect causal effects: the generalized front door criterion.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
The use of causal mediation analysis to evaluate the pathways by which an exposure affects an outcome is widespread in the social and biomedical sciences. Recent advances in this area have established formal conditions for identification and estimation of natural direct and indirect effects. However, these conditions typically involve stringent assumptions of no unmeasured confounding and that the mediator has been measured without error. These assumptions may fail to hold in many practical settings where mediation methods are applied. The goal of this article is two-fold. First, we formally establish that the natural indirect effect can in fact be identified in the presence of unmeasured exposure-outcome confounding provided there is no additive interaction between the mediator and unmeasured confounder(s). Second, we introduce a new estimator of the natural indirect effect that is robust to both classical measurement error of the mediator and unmeasured confounding of both exposure-outcome and mediator-outcome relations under certain no interaction assumptions. We provide formal proofs and a simulation study to illustrate our results. In addition, we apply the proposed methodology to data from the Harvard President's Emergency Plan for AIDS Relief (PEPFAR) program in Nigeria.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.