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Understanding Clinical Research21 April 2020Finding the Pathway: Mediation Analyses in Randomized Controlled TrialsA. Russell Localio, PhD, Anne R. Meibohm, PhD, and Eliseo Guallar, MD, DrPHA. Russell Localio, PhDUniversity of Pennsylvania, Philadelphia, Pennsylvania (A.R.L.)Search for more papers by this author, Anne R. Meibohm, PhDAmerican College of Physicians, Philadelphia, Pennsylvania (A.R.M.)Search for more papers by this author, and Eliseo Guallar, MD, DrPHJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (E.G.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M20-0887 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail The goal of clinical investigation often lies not only in estimating the effects of treatment or exposure but also in understanding their mechanisms. Identifying the pathways from treatment to outcome and explaining potential causes of the outcome—those are the specific domain of “mediation analysis” (1). In their report in Annals, Vallurupalli and colleagues (2) used the Canakinumab Anti-inflammatory Thrombosis Outcomes Study (CANTOS) to ask whether canakinumab reduced the incidence of anemia of chronic inflammation in patients with a history of myocardial infarction and high levels of high-sensitivity C-reactive protein (hsCRP) and whether reductions in hsCRP mediated this effect.CANTOS participants ...References1. VanderWeele TJ. Explanation in Causal Inference. Methods for Mediation and Interaction. Oxford Univ Pr; 2015. Google Scholar2. Vallurupalli M, MacFadyen JG, Glynn RJ, et al. Effects of interleukin-1β inhibition on incident anemia. Exploratory analyses from a randomized trial. Ann Intern Med. 2020;172:523-32. doi:10.7326/M19-2945 LinkGoogle Scholar3. Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013;18:137-50. [PMID: 23379553] doi:10.1037/a0031034 CrossrefMedlineGoogle Scholar4. Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods. 2010;15:309-34. [PMID: 20954780] doi:10.1037/a0020761 CrossrefMedlineGoogle Scholar5. VanderWeele TJ. Mediation analysis: a practitioner's guide. Annu Rev Public Health. 2016;37:17-32. [PMID: 26653405] doi:10.1146/annurev-publhealth-032315-021402 CrossrefMedlineGoogle Scholar6. VanderWeele TJ. Causal mediation analysis with survival data. Epidemiology. 2011;22:582-5. [PMID: 21642779] doi:10.1097/EDE.0b013e31821db37e CrossrefMedlineGoogle Scholar7. Tchetgen EJ. On causal mediation analysis with a survival outcome. Int J Biostat. 2011;7:Article 33. [PMID: 22049268] doi:10.2202/1557-4679.1351 CrossrefMedlineGoogle Scholar8. Lange T, Hansen JV. Direct and indirect effects in a survival context. Epidemiology. 2011;22:575-81. [PMID: 21552129] doi:10.1097/EDE.0b013e31821c680c CrossrefMedlineGoogle Scholar9. Wang W, Albert JM. Causal mediation analysis for the Cox proportional hazards model with a smooth baseline hazard estimator. J R Stat Soc Ser C Appl Stat. 2017;66:741-757. [PMID: 28943662] doi:10.1111/rssc.12188 CrossrefMedlineGoogle Scholar10. Vanderweele TJ, Hong G, Jones SM, et al. Mediation and spillover effects in group-randomized trials: a case study of the 4Rs educational intervention. J Am Stat Assoc. 2013;108:469-482. [PMID: 23997375] CrossrefMedlineGoogle Scholar11. Imai K, Keele L, Yamamoto T. Identification, inference and sensitivity analysis for causal mediation effects. Stat Sci. 2010;25:51-71. CrossrefGoogle Scholar12. Ding P, Vanderweele TJ. Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding. Biometrika. 2016;103:483-490. [PMID: 27279672] CrossrefMedlineGoogle Scholar13. Ding P, VanderWeele TJ. Sensitivity analysis without assumptions. Epidemiology. 2016;27:368-77. [PMID: 26841057] doi:10.1097/EDE.0000000000000457 CrossrefMedlineGoogle Scholar14. Valeri L. Computational tools. Accessed at www.hsph.harvard.edu/linda-valeri/computational-tools on 20 February 2020. Google Scholar15. SAS Institute. CAUSALMED. SAS/STAT® 15.1 User's Guide. SAS Institute; 2018. Google Scholar16. Hicks R, Tingley D. Causal mediation analysis. Stata J. 2011;11:605-619. CrossrefGoogle Scholar17. Emsley R, Liu H. PARAMED: Stata module to perform causal mediation analysis using parametric regression models. 2013. Accessed at https://EconPapers.repec.org/RePEc:boc:bocode:s457581 on 26 January 2020. Google Scholar18. Daniel RM, DeStavola BL, Cousens SN. Gformula: estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula. Stata J. 2011;11:479-517. CrossrefGoogle Scholar19. Tingley D, Yamamoto T, Hirose K, et al. mediation: R package for causal mediation analysis. J Stat Softw. 2014;59:1-38. CrossrefMedlineGoogle Scholar20. Tingley D, Yamamoto J, Hirose K, et al. mediation: R package for causal mediation analysis, version 4.4.5. 2015. Accessed at https://cran.r-project.org/web/packages/mediation/vignettes/mediation.pdf on 30 January 2020. Google Scholar21. Steen J, Loeys T, Moerkerke B, et al. Flexible mediation analysis using natural effect models. Accessed at https://cran.r-project.org/web/packages/medflex/vignettes/medflex.pdf on 9 March 2020. Google Scholar22. VanderWeele TJ, Vansteelandt S. Mediation analysis with multiple mediators. Epidemiol Methods. 2014;2:95-115. [PMID: 25580377] CrossrefMedlineGoogle Scholar23. VanderWeele TJ. A unification of mediation and interaction: a 4-way decomposition. Epidemiology. 2014;25:749-61. [PMID: 25000145] doi:10.1097/EDE.0000000000000121 CrossrefMedlineGoogle Scholar24. VanderWeele TJ, Tchetgen Tchetgen EJ. Mediation analysis with time varying exposures and mediators. J R Stat Soc Series B Stat Methodol. 2017;79:917-938. [PMID: 28824285] doi:10.1111/rssb.12194 CrossrefMedlineGoogle Scholar25. Talloen W, Moerkerke B, Loeys T, et al. Estimation of indirect effects in the presence of unmeasured confounding for the mediator-outcome relationship in a multilevel 2-1-1 mediation model. J Educ Behav Stat. 2016;41:359-391. CrossrefGoogle Scholar26. Stapleton LM, Pituch KA, Dion E. Standardized effect size measures for mediation analysis in cluster-randomized trials. J Exp Educ. 2015;83:547-582. CrossrefGoogle Scholar27. Ten Have TR, Joffe MM, Lynch KG, et al. Causal mediation analyses with rank preserving models. Biometrics. 2007;63:926-934. CrossrefMedlineGoogle Scholar28. Kenny DA. PowMedR. Accessed at www.davidakenny.net/progs/PowMedR.txt on 24 July 2016. Google Scholar29. Vittinghoff E, Neilands TB. Sample size for joint testing of indirect effects. Prev Sci. 2015;16:1128-35. [PMID: 25418811] doi:10.1007/s11121-014-0528-5 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: University of Pennsylvania, Philadelphia, Pennsylvania (A.R.L.)American College of Physicians, Philadelphia, Pennsylvania (A.R.M.)Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (E.G.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-0887.Corresponding Author: A. Russell Localio, PhD, Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 617 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104; e-mail, [email protected]upenn.edu.Current Author Addresses: Dr. Localio: Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 617 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104.Dr. Meibohm: American College of Physicians, 190 N. Independence Mall West, Philadelphia, PA 19106.Dr. Guallar: Departments of Epidemiology and Medicine, Johns Hopkins Bloomberg School of Public Health, 2024 East Monument Street, Room 2-645, Baltimore, MD 21205.Author Contributions: Conception and design: A.R. Localio.Analysis and interpretation of the data: A.R. Localio.Drafting of the article: A.R. Localio, A.R. Meibohm.Critical revision for important intellectual content: A.R. Localio, E. Guallar.Final approval of the article: A.R. Localio, A.R. Meibohm, E. Guallar.Statistical expertise: A.R. Localio, E. Guallar.Administrative, technical, or logistic support: A.R. Localio.This article was published at Annals.org on 24 March 2020. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoEffects of Interleukin-1β Inhibition on Incident Anemia Mounica Vallurupalli , Jean G. MacFadyen , Robert J. Glynn , Tom Thuren , Peter Libby , Nancy Berliner , and Paul M Ridker Metrics Cited byImproved Relationship Quality, Equitable Gender Attitudes, and Reduced Alcohol Abuse as Key Mechanisms to Reduce Intimate Partner Violence in the Bandebereho Couples’ Randomized Trial in Rwanda 21 April 2020Volume 172, Issue 8Page: 553-557KeywordsAnemiaBiostatisticsDrugsEpidemiologyHealth statisticsHigh sensitivity c reactive proteinObservational studiesPrevention, policy, and public healthRandomized trialsThrombosis ePublished: 24 March 2020 Issue Published: 21 April 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...