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Effect Measure Modification by Covariates in Mediation: Extending Regression-based Causal Mediation Analysis.
Li, Yi; Mathur, Maya B; Solomon, Daniel H; Ridker, Paul M; Glynn, Robert J; Yoshida, Kazuki.
Afiliación
  • Li Y; From the Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, Faculty of Medicine, McGill University, Montreal, QC, Canada.
  • Mathur MB; Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA.
  • Solomon DH; Quantitative Science Unit, Department of Medicine, Stanford University, Palo Alto, CA.
  • Ridker PM; Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA.
  • Glynn RJ; Department of Medicine, Harvard Medical School, Boston, MA.
  • Yoshida K; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, MA.
Epidemiology ; 34(5): 661-672, 2023 09 01.
Article en En | MEDLINE | ID: mdl-37527449
Existing methods for regression-based mediation analysis assume that the exposure-mediator effect, exposure-outcome effect, and mediator-outcome effect are constant across levels of the baseline characteristics of patients. However, investigators often have insight into how these underlying effects may be modified by baseline characteristics and are interested in how the resulting mediation effects, such as the natural direct effect (NDE), the natural indirect effect. (NIE), and the proportion mediated, are modified by these baseline characteristics. Motivated by an empirical example of anti-interleukin-1 therapy's benefit on incident anemia reduction and its mediation by an early change in an inflammatory biomarker, we extended the closed-form regression-based causal mediation analysis with effect measure modification (EMM). Using a simulated numerical example, we demonstrated that naive analysis without considering EMM can give biased estimates of NDE and NIE and visually illustrated how baseline characteristics affect the presence and magnitude of EMM of NDE and NIE. We then applied the extended method to the empirical example informed by pathophysiologic insights into potential EMM by age, diabetes, and baseline inflammation. We found that the proportion modified through the early post-treatment inflammatory biomarker was greater for younger, nondiabetic patients with lower baseline level of inflammation, suggesting differential usefulness of the early post-treatment inflammatory biomarker in monitoring patients depending on baseline characteristics. To facilitate the adoption of EMM considerations in causal mediation analysis by the wider clinical and epidemiologic research communities, we developed a free- and open-source R package, regmedint.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de Mediación / Inflamación Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Epidemiology Asunto de la revista: EPIDEMIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de Mediación / Inflamación Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Epidemiology Asunto de la revista: EPIDEMIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos