Tat-Thang Vo1, Cecilia Superchi2, Isabelle Boutron3, Stijn Vansteelandt4. 1. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281-S9, 9000, Ghent, Belgium; Université de Paris, CRESS, INSERM, INRA, F-75004, Paris, France. Electronic address: tatthang.vo@ugent.be. 2. Université de Paris, CRESS, INSERM, INRA, F-75004, Paris, France; Department of Statistics and Operations Research, Barcelona-Tech, UPC, c/ Jordi Girona 1-3, 08034, Barcelona, Spain. 3. Université de Paris, CRESS, INSERM, INRA, F-75004, Paris, France. 4. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281-S9, 9000, Ghent, Belgium; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Abstract
OBJECTIVES: To describe the methodological characteristics of mediation analyses (MAs) reported in recent randomized controlled trials (RCTs) and to propose recommendations on the planning, conduct, and reporting of MAs in practice. STUDY DESIGN AND SETTING: We conducted a systematic review by searching MEDLINE (January 1, 2017, to December 1, 2018) for all reports of RCTs or secondary analyses of previously published RCTs that reported a MA. Two reviewers independently screened the title, abstracts, and full texts of the identified reports and extracted the data from the 98 eligible studies. RESULTS: MAs were nearly always (96%) based on a traditional mediation approach. Most studies did not report a sample size calculation for the MA (96%) or assess potential treatment-by-mediator interactions (96%). In 53% of studies, mediators and outcomes were simultaneously measured. In 57% of studies, mediator-mediator and mediator-outcome confounders were adjusted for in the analysis, although adjustment was often limited to few potential confounders. About 30% of studies discussed the assumptions underlying the MA. CONCLUSION: The conduct and reporting of MAs remained quite heterogeneous in practice. Future MAs could benefit from a consensus-based planning, conduct, and reporting guideline for MA.
OBJECTIVES: To describe the methodological characteristics of mediation analyses (MAs) reported in recent randomized controlled trials (RCTs) and to propose recommendations on the planning, conduct, and reporting of MAs in practice. STUDY DESIGN AND SETTING: We conducted a systematic review by searching MEDLINE (January 1, 2017, to December 1, 2018) for all reports of RCTs or secondary analyses of previously published RCTs that reported a MA. Two reviewers independently screened the title, abstracts, and full texts of the identified reports and extracted the data from the 98 eligible studies. RESULTS: MAs were nearly always (96%) based on a traditional mediation approach. Most studies did not report a sample size calculation for the MA (96%) or assess potential treatment-by-mediator interactions (96%). In 53% of studies, mediators and outcomes were simultaneously measured. In 57% of studies, mediator-mediator and mediator-outcome confounders were adjusted for in the analysis, although adjustment was often limited to few potential confounders. About 30% of studies discussed the assumptions underlying the MA. CONCLUSION: The conduct and reporting of MAs remained quite heterogeneous in practice. Future MAs could benefit from a consensus-based planning, conduct, and reporting guideline for MA.
Authors: Hopin Lee; Aidan G Cashin; Sarah E Lamb; Sally Hopewell; Stijn Vansteelandt; Tyler J VanderWeele; David P MacKinnon; Gemma Mansell; Gary S Collins; Robert M Golub; James H McAuley; A Russell Localio; Ludo van Amelsvoort; Eliseo Guallar; Judith Rijnhart; Kimberley Goldsmith; Amanda J Fairchild; Cara C Lewis; Steven J Kamper; Christopher M Williams; Nicholas Henschke Journal: JAMA Date: 2021-09-21 Impact factor: 56.272
Authors: Elizabeth A Stuart; Ian Schmid; Trang Nguyen; Elizabeth Sarker; Adam Pittman; Kelly Benke; Kara Rudolph; Elena Badillo-Goicoechea; Jeannie-Marie Leoutsakos Journal: Epidemiol Rev Date: 2022-01-14 Impact factor: 4.280
Authors: Ruti G Levtov; Kate Doyle; Jeffrey B Bingenheimer; Shaon Lahiri; Shamsi Kazimbaya; Emmanuel Karamage; Felix Sayinzoga; Merab Mutoni; Claude Hodari Rubayita; Gary Barker Journal: Prev Sci Date: 2022-10-11
Authors: Judith J M Rijnhart; Matthew J Valente; David P MacKinnon; Jos W R Twisk; Martijn W Heymans Journal: Struct Equ Modeling Date: 2020-09-18 Impact factor: 6.125
Authors: Aidan G Cashin; James H McAuley; Sarah E Lamb; Sally Hopewell; Steven J Kamper; Christopher M Williams; Nicholas Henschke; Hopin Lee Journal: BMC Med Res Methodol Date: 2020-02-03 Impact factor: 4.615
Authors: Judith J M Rijnhart; Sophia J Lamp; Matthew J Valente; David P MacKinnon; Jos W R Twisk; Martijn W Heymans Journal: BMC Med Res Methodol Date: 2021-10-25 Impact factor: 4.615