Statistical assessment of mediational effects for logistic mediational models

Huang, Bin; Sivaganesan, Siva; Succop, Paul; Goodman, Elizabeth

doi:10.1002/sim.1847

Cited by 89Advanced Causal Inference TechniquesStatistical Methods and Bayesian InferenceStatistical Methods in Epidemiology

Statistical assessment of mediational effects for logistic mediational models

Huang, Bin, Sivaganesan, Siva, Succop, Paul, Goodman, Elizabeth·2004·Statistics in Medicine

DOI 10.1002/sim.1847

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Abstract

The concept of mediation has broad applications in medical health studies. Although the statistical assessment of a mediational effect under the normal assumption has been well established in linear structural equation models (SEM), it has not been extended to the general case where normality is not a usual assumption. In this paper, we propose to extend the definition of mediational effects through causal inference. The new definition is consistent with that in linear SEM and does not rely on the assumption of normality. Here, we focus our attention on the logistic mediation model, where all variables involved are binary. Three approaches to the estimation of mediational effects—Delta method, bootstrap, and Bayesian modelling via Monte Carlo simulation are investigated. Simulation studies are used to examine the behaviour of the three approaches. Measured by 95 per cent confidence interval (CI) coverage rate and root mean square error (RMSE) criteria, it was found that the Bayesian method using a non‐informative prior outperformed both bootstrap and the Delta methods, particularly for small sample sizes. Case studies are presented to demonstrate the application of the proposed method to public health research using a nationally representative database. Extending the proposed method to other types of mediational model and to multiple mediators are also discussed. Copyright © 2004 John Wiley & Sons, Ltd.

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Huang, B., Sivaganesan, P. S., Paul, P., & Goodman, P. E. (2013)
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Authors: Huang, B., Sivaganesan, S., Succop, P., & Goodman, E.
Affiliations: Cincinnati Children's Hospital Medical Center (US)
University of Cincinnati (US)
Brandeis University (US)
Corresponding author: Huang, Bin
ORCID: Huang, Bin
Sivaganesan, Siva
Goodman, Elizabeth
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2004-08-12
Volume: 23
Issue: 17
Pages: 2713-2728
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.1847
OpenAlex: W2127336501
Cited by: 89
References: 28
Citations by year: 2026: 2 · 2025: 4 · 2023: 4 · 2022: 8 · 2021: 3 · 2020: 1 · 2019: 2 · 2018: 6 · 2017: 7 · 2016: 4 · 2015: 10 · 2014: 4 · 2013: 7 · 2012: 3
FWCI: 1.69
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Statistics in Medicine (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health
National Institute of Child Health and Human Development
Eunice Kennedy Shriver National Institute of Child Health and Human Development (P01- HD31921, P01HD031921)
Carolina Population Center, University of North Carolina at Chapel Hill
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: mediation analysis, causal inference, binary outcomes, non-normal data, bootstrapping, delta method, Bayesian estimation, Monte Carlo simulation, confidence interval coverage, small sample size, multiple mediators
MeSH terms: Adolescent, Bayes Theorem, Computer Simulation, Data Interpretation, Statistical, Depression, Female, Humans, Monte Carlo Method, Public Health, Social Class, Logistic Models

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Statistical assessment of mediational effects for logistic mediational models

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