Evidence map›Paper›PMID 39470692›Full record

ArticleMultivariate behavioral research

Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary.

Judith J M Rijnhart, Matthew J Valente, David P MacKinnon

Abstract read
In one paragraph

Article in Multivariate behavioral research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Judith J M RijnhartCollege of Public Health, University of South Florida.ORCID 0000-0002-1046-3741
Matthew J ValenteCollege of Public Health, University of South Florida.ORCID 0000-0001-9130-2255
David P MacKinnonDepartment of Psychology, Arizona State University.ORCID 0000-0003-0866-6010

Funding

Estimating Mediation Effects in Prevention StudiesR37DA009757 · NIDA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI MACKINNON, DAVID P · 2015 to 2024
$3.5M
Modern Longitudinal Mediation Methods for Prevention StudiesF31DA043317 · NIDA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI VALENTE, MATTHEW JOHN · 2017 to 2018
$65k
NIDA NIH HHS F31 DA043317NIDA NIH HHS R37 DA009757
6 · The paper itself

Abstract

Despite previous warnings against the use of the difference-in-coefficients method for estimating the indirect effect when the outcome in the mediation model is binary, the difference-in-coefficients method remains readily used in a variety of fields. The continued use of this method is presumably because of the lack of awareness that this method conflates the indirect effect estimate and non-collapsibility. In this paper, we aim to demonstrate the problems associated with the difference-in-coefficients method for estimating indirect effects for mediation models with binary outcomes. We provide a formula that decomposes the difference-in-coefficients estimate into (1) an estimate of non-collapsibility, and (2) an indirect effect estimate. We use a simulation study and an empirical data example to illustrate the impact of non-collapsibility on the difference-in-coefficients estimate of the indirect effect. Further, we demonstrate the application of several alternative methods for estimating the indirect effect, including the product-of-coefficients method and regression-based causal mediation analysis. The results emphasize the importance of choosing a method for estimating the indirect effect that is not affected by non-collapsibility.

Indexed as

Mediation AnalysisModels, StatisticalComputer SimulationData Interpretation, StatisticalHumansBinary outcomeindirect effectlogistic regressionmediation analysisprobit regression

Identifiers

PMID39470692
PMCPMC11991894

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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.