ArticlePsychological methods2021
The impact of measurement error and omitting confounders on statistical inference of mediation effects and tools for sensitivity analysis.
Article in Psychological methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative bias analysis in practice: review of software for regression with unmeasured confounding.BMC medical research methodology · 2023Pooled it
- Quantitative bias analysis for mismeasured variables in health research: a review of software tools.BMC medical research methodology · 2025Review
- A novel approach to estimate moderated treatment effects and moderated mediated effects with continuous moderators.Psychological methods · 2025Article
- How do manipulation checks interfere with the inference of causal relationships?Behavior research methods · 2024Article
- Periodontal Health and Its Relationship with Psychological Stress: A Cross-Sectional Study.Journal of clinical medicine · 2024Article
- The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches.Structural equation modeling : a multidisciplinary journal · 2024Article
- The trouble with triples: Examining the impact of measurement error in mediation analysis.Genetics · 2023Article
- Causal mediation effects in single case experimental designs.Psychological methods · 2023Article
- Estimating latent baseline-by-treatment interactions in statistical mediation analysis.Structural equation modeling : a multidisciplinary journal · 2023Article
- Accommodating a Latent XM Interaction in Statistical Mediation Analysis.Multivariate behavioral researchArticle
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2 authors.
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Abstract
To make valid statistical inferences from mediation analysis, a number of assumptions need to be assessed. Among the assumptions, 2 frequently discussed ones are (a) the independent variable, mediator, and outcome variables are measured without error; and (b) no confounders of the effects in the mediation model are omitted. The impact of violating either assumption alone on statistical inference of mediation has been discussed in previous literature. In practice, violations of the 2 assumptions often co-occur. In this study, we analytically investigated the effects of measurement error and omitting confounders on statistical inference of mediation effects, including both point estimation and significance testing. Based on the analytical results, we proposed sensitivity analysis techniques for assessing the robustness of mediation inference to the violation of the 2 assumptions. To implement the techniques, we developed R functions and a user-friendly web tool. Simulated-data and real-data examples were provided for illustrations. We hope the developed tools will help researchers conduct sensitivity analyses of mediation inference more conveniently. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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