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.

翻译：估计目标框架日益被用于在确证性临床试验中提出研究问题。在证据综合领域，估计目标的采用仍较为有限，而PICO（人群、干预、对照、结局）框架的应用更为普遍。尽管PICO与估计目标存在重叠要素，但估计目标框架明确考虑了针对并发事件的不同策略。我们提出一个实用框架，用于在临床试验荟萃分析中应用估计目标，强调估计目标在系统性识别和缓解关键定量异质性来源、以及增强合并估计的适用性或外部效度方面的价值。重点聚焦于并发事件策略在卫生技术评估荟萃分析特定情境中的作用。我们将估计目标框架应用于临床试验的网络荟萃分析，比较司美格鲁肽与度拉糖肽治疗2型糖尿病的疗效。我们探究了针对治疗中止或启用急救药物的治疗策略与针对相应并发事件的假设策略之间的影响差异。在荟萃分析层面指定不同的目标估计目标，使我们能够明确异质性来源、并发事件策略以及驱动结果潜在差异的因素。我们主张将估计目标整合到荟萃分析的设计中，同时承认在缺乏个体层面数据的情况下可能存在潜在挑战。估计目标可补充PICO框架，以加强利益相关方之间关于证据综合旨在证明什么的沟通，并确保证据对医疗决策者具有最大相关性。