Projects per year
Abstract
Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgroups within the population. Population-adjustment methods are increasingly used to adjust for differences in effect modifiers between study populations and to produce population-adjusted estimates in a relevant target population for decision-making. It is also widely understood that marginal and conditional estimands for non-collapsible effect measures, such as odds ratios or hazard ratios, do not in general coincide even without effect modification. However, the consequences of both non-collapsibility and effect modification together are little-discussed in the literature.
In this paper, we set out the definitions of conditional and marginal estimands, illustrate their properties when effect modification is present, and discuss the implications for decision-making. In particular, we show that effect modification can result in conflicting treatment rankings between conditional and marginal estimates. This is because conditional and marginal estimands correspond to different decision questions that are no longer aligned when effect modification is present. For time-to-event outcomes, the presence of covariates implies that marginal hazard ratios are time-varying, and effect modification can cause marginal hazard curves to cross. We conclude with practical recommendations for decision-making in the presence of effect modification, based on pragmatic comparisons of both conditional and marginal estimates in the decision target population. Currently, multilevel network meta-regression is the only population-adjustment method capable of producing both conditional and marginal estimates, in any decision target population.
In this paper, we set out the definitions of conditional and marginal estimands, illustrate their properties when effect modification is present, and discuss the implications for decision-making. In particular, we show that effect modification can result in conflicting treatment rankings between conditional and marginal estimates. This is because conditional and marginal estimands correspond to different decision questions that are no longer aligned when effect modification is present. For time-to-event outcomes, the presence of covariates implies that marginal hazard ratios are time-varying, and effect modification can cause marginal hazard curves to cross. We conclude with practical recommendations for decision-making in the presence of effect modification, based on pragmatic comparisons of both conditional and marginal estimates in the decision target population. Currently, multilevel network meta-regression is the only population-adjustment method capable of producing both conditional and marginal estimates, in any decision target population.
| Original language | English |
|---|---|
| Pages (from-to) | 323-349 |
| Number of pages | 27 |
| Journal | Research Synthesis Methods |
| Volume | 16 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 12 Mar 2025 |
Bibliographical note
Publisher Copyright:© The Author(s), 2025.
Keywords
- network meta-analysis
- population adjustment
- effect modification
- decision-making
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Bristol TAG
Whiting, P. F. (Co-Principal Investigator), Welton, N. J. (Co-Principal Investigator), Owen-Smith, A. L. (Co-Investigator), Phillippo, D. M. (Co-Investigator), Caldwell, D. (Co-Investigator), Marques, E. M. R. (Co-Investigator), Jones, H. E. (Co-Investigator), Thom, H. H. Z. (Co-Investigator), Pedder, H. (Co-Investigator), Savovic, J. (Co-Investigator), Higgins, J. P. T. (Co-Investigator), Sadek, A. S. (Co-Investigator), Lopez Manzano, C. (Co-Investigator), Cooper, C. (Co-Investigator), Tomlinson, E. (Co-Investigator), Carroll, J. S. (Co-Investigator), Walker, J. G. (Co-Investigator), Ward, M. E. (Co-Investigator) & James, R. M. (Co-Investigator)
1/04/22 → 31/03/27
Project: Research, Parent
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Effective population adjustment in evidence synthesis of randomised controlled trials for health technology assessment
Phillippo, D. M. (Principal Investigator)
1/04/22 → 31/03/27
Project: Research
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8071 MRC via York - HOD1: Inferring relative treatment effects from combined randomised and oberservational data
Welton, N. J. (Principal Investigator)
1/01/19 → 30/06/22
Project: Research
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