ArticleJournal of statistical theory and practice2016
Assessing natural direct and indirect effects for a continuous exposure and a dichotomous outcome.
Article in Journal of statistical theory and practice, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Article
- The association of prenatal manganese exposure with problem-solving skills and its mediation by the building blocks of executive function.Neurotoxicology · 2022Article
- Inflammatory burden and persistent CT lung abnormalities in COVID-19 patients.Scientific reports · 2022Article
- Causal Mediation Analysis With Observational Data: Considerations and Illustration Examining Mechanisms Linking Neighborhood Poverty to Adolescent Substance Use.American journal of epidemiology · 2019Observational
- Placental surface area mediates the association betweenClinical epigenetics · 2018Article
- Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies.BMJ open · 2017Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Recent advances in the literature on mediation have extended from traditional linear structural equation modeling approach to causal mediation analysis using potential outcomes framework. Pearl proposed a mediation formula to calculate expected potential outcomes used in the natural direct and indirect effects definition under the key sequential ignorability assumptions. Current methods mainly focused on binary exposure variables, and in this article, this approach is further extended to settings in which continuous exposures may be of interest. Focusing on a dichotomous outcome, we give precise definitions of the natural direct and indirect effects on both the risk difference and odds ratio scales utilizing the empirical joint distribution of the exposure and baseline covariates from the whole sample analysis population. A mediation-formula based approach is proposed to estimate the corresponding causal quantities. Simulation study is conducted to assess the statistical properties of the proposed method and we illustrate our approach by applying it to the Jackson Heart Study to estimate the mediation effects of diabetes on the relation between obesity and chronic kidney disease. Sensitivity analysis is performed to assess the impact of violation of no unmeasured mediator-outcome confounder assumption.
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Registered trials
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