Evidence map›Paper›PMID 38509513›Full record

ArticleBMC medical research methodology2024

An empirical evaluation of approximate and exact regression-based causal mediation approaches for a binary outcome and a continuous or a binary mediator for case-control study designs.

Miguel Caubet, Kevin L'Espérance, Anita Koushik, Geneviève Lefebvre

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In one paragraph

Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

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3 · Its place in the literature

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1 citing paper in PubMed, 1 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Miguel CaubetDepartment of Mathematics, Université du Québec à Montréal, Montreal, Canada.
Kevin L'EspéranceDepartment of Social and Preventive Medicine, Université de Montréal, Montreal, Canada.
Anita KoushikDepartment of Social and Preventive Medicine, Université de Montréal, Montreal, Canada.
Geneviève LefebvreDepartment of Mathematics, Université du Québec à Montréal, Montreal, Canada. lefebvre.gen@uqam.ca.
Université de Montréal · CAUniversité du Québec à Montréal · CA

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2020-05473
6 · The paper itself

Abstract

backgroundIn the causal mediation analysis framework, several parametric regression-based approaches have been introduced in past years for decomposing the total effect of an exposure on a binary outcome into a direct effect and an indirect effect through a target mediator. In this context, a well-known strategy involves specifying a logistic model for the outcome and invoking the rare outcome assumption (ROA) to simplify estimation. Recently, exact estimators for natural direct and indirect effects have been introduced to circumvent the challenges prompted by the ROA. As for the approximate approaches relying on the ROA, these exact approaches cannot be used as is on case-control data where the sampling mechanism depends on the outcome.

methodsConsidering a continuous or a binary mediator, we empirically compare the approximate and exact approaches using simulated data under various case-control scenarios. An illustration of these approaches on case-control data is provided, where the natural mediation effects of long-term use of oral contraceptives on ovarian cancer, with lifetime number of ovulatory cycles as the mediator, are estimated.

resultsIn the simulations, we found few differences between the performances of the approximate and exact approaches when the outcome was rare, both marginally and conditionally on variables. However, the performance of the approximate approaches degraded as the prevalence of the outcome increased in at least one stratum of variables. Differences in behavior were also observed among the approximate approaches. In the data analysis, all studied approaches were in agreement with respect to the natural direct and indirect effects estimates.

conclusionsIn the case where a violation of the ROA applies or is expected, approximate mediation approaches should be avoided or used with caution, and exact estimators favored.

Indexed as

Mediation AnalysisModels, StatisticalCase-Control StudiesCausalityHumansLogistic ModelsBinary outcomesCase-control studyCounterfactualsMediation analysisNatural effectsOral contraceptivesOvarian cancerRare outcome assumption

Identifiers

PMID38509513
PMCPMC10953265
OpenAlexW4393008884

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Registered trials

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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.