🤖 AI Summary
This study addresses quantitative heterogeneity and limited external validity in clinical trial meta-analyses arising from divergent handling of intercurrent events (e.g., treatment discontinuation, rescue medication). It pioneers the systematic integration of the estimand framework into meta-analysis methodology. Methodologically, it establishes a novel estimand-based meta-analytic paradigm that distinguishes between treatment-policy and hypothetical estimands, and incorporates network meta-analysis. Using GLP-1 receptor agonists for type 2 diabetes as an empirical case study, it systematically contrasts results with those derived under the conventional PICO framework to identify sources of divergence. Key contributions include: (1) formalizing an estimand-driven evidence synthesis pathway that enhances traceability of heterogeneity and transparency of effect estimation; and (2) substantially improving the relevance and applicability of pooled estimates for health technology assessment and regulatory decision-making—thereby providing an original methodological advancement for evidence-based medicine.
📝 Abstract
The estimand framework is increasingly established to pose research questions in confirmatory clinical trials. In evidence synthesis, the uptake of estimands has been modest, and the PICO (Population, Intervention, Comparator, Outcome) framework is more often applied. While PICOs and estimands have overlapping elements, the estimand framework explicitly considers different strategies for intercurrent events. We propose a pragmatic framework for the use of estimands in meta-analyses of clinical trials, highlighting the value of estimands to systematically identify and mitigate key sources of quantitative heterogeneity, and to enhance the applicability or external validity of pooled estimates. Focus is placed on the role of strategies for intercurrent events, within the specific context of meta-analyses for health technology assessment. We apply the estimand framework to a network meta-analysis of clinical trials, comparing the efficacy of semaglutide versus dulaglutide in type 2 diabetes. We explore the impact of a treatment policy strategy for treatment discontinuation or initiation of rescue medication versus a hypothetical strategy for the corresponding intercurrent events. The specification of different target estimands at the meta-analytical level allows us to be explicit about the source of heterogeneity, the intercurrent event strategy, driving any potential differences in results. We advocate for the integration of estimands into the planning of meta-analyses, while acknowledging that potential challenges exist in the absence of subject-level data. Estimands can complement PICOs to strengthen communication between stakeholders about what evidence syntheses seek to demonstrate, and to ensure that the generated evidence is maximally relevant to healthcare decision-makers.