The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index.
Mediation and moderation of treatment effects in randomised controlled trials of complex interventions
Abstract
Complex intervention trials should be able to answer both pragmatic and explanatory questions in order to test the theories motivating the intervention and help understand the underlying nature of the clinical problem being tested. Key to this is the estimation of direct effects of treatment and indirect effects acting through intermediate variables which are measured post-randomisation. Using psychological treatment trials as an example of complex interventions, we review statistical methods which crucially evaluate both direct and indirect effects in the presence of hidden confounding between mediator and outcome. We review the historical literature on mediation and moderation of treatment effects. We introduce two methods from within the existing causal inference literature, principal stratification and structural mean models, and demonstrate how these can be applied in a mediation context before discussing approaches and assumptions necessary for attaining identifiability of key parameters of the basic causal model. Assuming that there is modification by baseline covariates of the effect of treatment (i.e. randomisation) on the mediator (i.e. covariate by treatment interactions), but no direct effect on the outcome of these treatment by covariate interactions leads to the use of instrumental variable methods. We describe how moderation can occur through post-randomisation variables, and extend the principal stratification approach to multiple group methods with explanatory models nested within the principal strata. We illustrate the new methodology with motivating examples of randomised trials from the mental health literature.
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“In order to lend δ̄(x) (see Equation 5) a causal interpretation, we must assume sequential ignorability (Emsley et al. 2010, Imai et al. 2010c), sometimes termed exchangeability or no unmeasured confounding.”
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Metadata
- Authors
- Emsley, R., Dunn, G., & White, I. R.
- Affiliations
- University of Manchester (GB)
- MRC Biostatistics Unit (GB)
- Corresponding author
- Dunn, Graham
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2009-07-16
- Volume
- 19
- Issue
- 3
- Pages
- 237-270
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2092884989
- Cited by
- 356
- References
- 115
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 10 · 2023: 13 · 2022: 12 · 2021: 28 · 2020: 17 · 2019: 30 · 2018: 23 · 2017: 34 · 2016: 38 · 2015: 33 · 2014: 25 · 2013: 34 · 2012: 20
- FWCI
- 11.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistical Methods in Medical Research (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Funders
- Medical Research Council
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- mediation analysis, moderation analysis, complex interventions, unmeasured confounding, principal stratification, instrumental variables, mental health interventions, causal inference
- MeSH terms
- Suicide Prevention, Depression, Humans, Mental Health, Psychotherapy, Schizophrenia, Models, Statistical, Randomized Controlled Trials as Topic, Treatment Outcome, Outcome Assessment, Health Care, Risk Reduction Behavior, Biostatistics
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