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Mediation analysis with case–control sampling: identification and estimation in the presence of a binary mediator
Department of Statistics, Computer Science, and Applications, University of Florence, Florence, Italy.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0002-9107-6486
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE). Department of Economics, University of Perugia, Perugia, Italy.ORCID iD: 0000-0002-2503-8342
2024 (English)In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 66, no 1, article id 2300089Article in journal (Refereed) Published
Abstract [en]

With reference to a stratified case–control (CC) procedure based on a binary variable of primary interest, we derive the expression of the distortion induced by the sampling design on the parameters of the logistic model of a secondary variable. This is particularly relevant when performing mediation analysis (possibly in a causal framework) with stratified case–control (SCC) data in settings where both the outcome and the mediator are binary. Despite being designed for parametric identification, our strategy is general and can be used also in a nonparametric context. With reference to parametric estimation, we derive the maximum likelihood (ML) estimator and the M-estimator of the joint outcome–mediator parameter vector. We then conduct a simulation study focusing on the main causal mediation quantities (i.e., natural effects) and comparing M- and ML estimation to existing methods, based on weighting. As an illustrative example, we reanalyze a German CC data set in order to investigate whether the effect of reduced immunocompetency on listeriosis onset is mediated by the intake of gastric acid suppressors.

Place, publisher, year, edition, pages
Wiley-VCH Verlagsgesellschaft, 2024. Vol. 66, no 1, article id 2300089
Keywords [en]
collider node, distortion, logistic regression, odds ratio, secondary outcome
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-219820DOI: 10.1002/bimj.202300089ISI: 001141939100001Scopus ID: 2-s2.0-85182182306OAI: oai:DiVA.org:umu-219820DiVA, id: diva2:1830179
Funder
Swedish Research Council, 2019-01064Available from: 2024-01-22 Created: 2024-01-22 Last updated: 2024-03-12Bibliographically approved

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Genbäck, MinnaStanghellini, Elena

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