ArticleEvaluation review2013
Sensitivity plots for confounder bias in the single mediator model.
Article in Evaluation review, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed.
- Trial
- Engagement with a digital therapeutic for smoking cessation designed for persons with psychiatric illness fully mediates smoking outcomes in a pilot randomized controlled trial.Translational behavioral medicine · 2021Trial
- Understanding Who Benefits the Most from Interventions: Implications for Baseline Target Moderated Mediation Analysis with Multiple Moderators.Prevention science : the official journal of the Society for Prevention Research · 2025Article
- A novel approach to estimate moderated treatment effects and moderated mediated effects with continuous moderators.Psychological methods · 2025Article
- The association between rs6859 inmedRxiv : the preprint server for health sciences · 2024Article
- Causal moderated mediation analysis: Methods and software.Behavior research methods · 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 association between rs6859 inFrontiers in aging neuroscience · 2024Article
- Do family characteristics contribute to a socioeconomic gradient in overweight in early childhood? - Single mediation analyses of data from German preschool children.Preventive medicine reports · 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
- Causal Mediation Analysis in Single Case Experimental Designs: Introduction to the Special Issue.Evaluation & the health professions · 2022Article
- Teacher's Corner: An R Shiny App for Sensitivity Analysis for Latent Growth Curve Mediation.Structural equation modeling : a multidisciplinary journal · 2022Article
- Comparing Alternative Corrections for Bias in the Bias-Corrected Bootstrap Test of Mediation.Evaluation & the health professions · 2021Article
- The impact of measurement error and omitting confounders on statistical inference of mediation effects and tools for sensitivity analysis.Psychological methods · 2021Article
- The measurement of the mediator and its influence on statistical mediation conclusions.Psychological methods · 2021Article
- Sensitivity Analysis in Nonrandomized Longitudinal Mediation Analysis.Frontiers in psychology · 2021Article
- Description, prediction and causation: Methodological challenges of studying child and adolescent development.Developmental cognitive neuroscience · 2020Review
- Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure.Psychological methods · 2020Article
- Confounder detection in linear mediation models: Performance of kernel-based tests of independence.Behavior research methods · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundCausal inference continues to be a critical aspect of evaluation research. Recent research in causal inference for statistical mediation has focused on addressing the sequential ignorability assumption; specifically, that there is no confounding between the mediator and the outcome variable.
objectivesThis article compares and contrasts three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods.
designTwo types of data were used to fully examine the plots for sensitivity analysis. The first type was generated data from a single mediator model with a confounder influencing both the mediator and the outcome variable. The second was data from an actual intervention study. With both types of data, situations are examined where confounding has a large effect and a small effect. SUBJECTS: The nonsimulated data were from a large intervention study to decrease intentions to use steroids among high school football players. We demonstrate one situation where confounding is likely and another situation where confounding is unlikely.
conclusionsWe discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods.
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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.