Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators

Margarita Moreno-Betancur*, Paul A Moran, Denise Becker, George Patton, John B Carlin

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)
20 Downloads (Pure)

Abstract

Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. First, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Second, the complexity of such questions is invariably such that there are no well-defined mediator interventions (i.e. actual treatments, programs, etc.) for which data exist on the relevant populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data, that is, on mediator measures such as depression symptoms for which the actual interventions that one might implement to alter them are not well defined. We propose a novel framework that addresses these challenges by defining mediation effects that map to a target trial of hypothetical interventions targeting multiple mediators for which we simulate the effects. Specifically, we specify a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical interventions that would shift the mediators’ distributions (separately under various interdependence assumptions, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, simulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions, which reflects the lower level of evidence that is expected with ill-defined interventions. Application to the self-harm example in the Victorian Adolescent Health Cohort Study illustrates the value of our proposal for informing the design and evaluation of actual interventions in the future.
Original languageEnglish
Pages (from-to)1395-1412
Number of pages18
JournalStatistical Methods in Medical Research
Volume30
Issue number6
Early online date20 Mar 2021
DOIs
Publication statusPublished - 30 Jun 2021

Bibliographical note

Funding Information:
We thank the reviewers for invaluable feedback and Carolyn Coffey for sharing her knowledge of the Victorian Adolescent Health Cohort Study. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a Discovery Early Career Researcher Award fellowship to MMB from the Australian Research Council [DE190101326]. This work was also supported by The University of Melbourne (MMB) and the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol, England (PM). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. The Murdoch Children?s Research Institute is supported by the Victorian Government?s Operational Infrastructure Support Program.

Publisher Copyright:
© The Author(s) 2021.

Keywords

  • mediation
  • ill-defined interventions
  • interventional effects
  • natural effects
  • target trial
  • multiple mediators
  • randomised controlled trial
  • causal inference

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