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Stijn Vansteelandt

Researcher at Ghent University

Publications -  284
Citations -  10516

Stijn Vansteelandt is an academic researcher from Ghent University. The author has contributed to research in topics: Causal inference & Estimator. The author has an hindex of 46, co-authored 258 publications receiving 8311 citations. Previous affiliations of Stijn Vansteelandt include Ghent University Hospital & University of Pavia.

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A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational Studies: The AGReMA Statement.

Abstract: Importance Mediation analyses of randomized trials and observational studies can generate evidence about the mechanisms by which interventions and exposures may influence health outcomes. Publications of mediation analyses are increasing, but the quality of their reporting is suboptimal. Objective To develop international, consensus-based guidance for the reporting of mediation analyses of randomized trials and observational studies (A Guideline for Reporting Mediation Analyses; AGReMA). Design, setting, and participants The AGReMA statement was developed using the Enhancing Quality and Transparency of Health Research (EQUATOR) methodological framework for developing reporting guidelines. The guideline development process included (1) an overview of systematic reviews to assess the need for a reporting guideline; (2) review of systematic reviews of relevant evidence on reporting mediation analyses; (3) conducting a Delphi survey with panel members that included methodologists, statisticians, clinical trialists, epidemiologists, psychologists, applied clinical researchers, clinicians, implementation scientists, evidence synthesis experts, representatives from the EQUATOR Network, and journal editors (n = 19; June-November 2019); (4) having a consensus meeting (n = 15; April 28-29, 2020); and (5) conducting a 4-week external review and pilot test that included methodologists and potential users of AGReMA (n = 21; November 2020). Results A previously reported overview of 54 systematic reviews of mediation studies demonstrated the need for a reporting guideline. Thirty-three potential reporting items were identified from 3 systematic reviews of mediation studies. Over 3 rounds, the Delphi panelists ranked the importance of these items, provided 60 qualitative comments for item refinement and prioritization, and suggested new items for consideration. All items were reviewed during a 2-day consensus meeting and participants agreed on a 25-item AGReMA statement for studies in which mediation analyses are the primary focus and a 9-item short-form AGReMA statement for studies in which mediation analyses are a secondary focus. These checklists were externally reviewed and pilot tested by 21 expert methodologists and potential users, which led to minor adjustments and consolidation of the checklists. Conclusions and relevance The AGReMA statement provides recommendations for reporting primary and secondary mediation analyses of randomized trials and observational studies. Improved reporting of studies that use mediation analyses could facilitate peer review and help produce publications that are complete, accurate, transparent, and reproducible.
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Distribution of human papillomavirus in a family planning population in nairobi, kenya.

TL;DR: The pattern of HPV distribution in this population was different from that in other regions in the world, which has important consequences for HPV vaccine development.
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Flexible Mediation Analysis With Multiple Mediators.

TL;DR: This article proposes a procedure for obtaining fine-grained decompositions that may still be recovered from observed data in such complex settings and introduces natural effects models along with estimation methods that allow for flexible and parsimonious modeling.
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Regression models for disease prevalence with diagnostic tests on pools of serum samples.

TL;DR: Joint pool and sample size calculations using information from individual contributors to the pool are performed and it is shown that a good design can severely reduce cost and yet increase precision.
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Invited Commentary: G-Computation–Lost in Translation?

TL;DR: A compromise approach is proposed, doubly robust standardization, that combines the benefits of both of these causal inference techniques and is not more difficult to implement.