The target trial framework enables causal inference from longitudinal observational data by emulating randomized trials initiated at multiple time points. Precision is often improved by pooling information across trials, with standard models typically assuming - among other things - a time-constant treatment effect. However, this obscures interpretation when the true treatment effect varies, which we argue to be likely as a result of relying on noncollapsible estimands. To address these challenges, this paper introduces a model-free strategy for target trial analysis, centered around the choice of the estimand, rather than model specification. This ensures that treatment effects remain clearly interpretable for well-defined populations even under model misspecification. We propose estimands suitable for different study designs, and develop accompanying G-computation and inverse probability weighted estimators. Applications on simulations and real data on antimicrobial de-escalation in an intensive care unit setting demonstrate the greater clarity and reliability of the proposed methodology over traditional techniques.
Applying the estimands framework to non-inferiority trials: guidance on choice of hypothetical estimands for non-adherence and comparison of estimation methods
Estimands and estimation in trials with time trends
Universitat Politècnica de Catalunya - BarcelonaTech（加泰罗尼亚理工大学）；Novartis Pharma AG（诺华制药）；Ghent University（根特大学）；Vrije Universiteit Brussel（布鲁塞尔自由大学）；Medical University of Vienna（维也纳医科大学）
What is estimated in cluster randomized crossover trials with informative sizes? -- A survey of estimands and common estimators
University of Pennsylvania（宾夕法尼亚大学）；Monash University（莫纳什大学）；MRC Clinical Trials Unit at UCL（伦敦大学学院医学研究委员会临床试验单元）；Wellington Hospital（惠灵顿医院）；Medical Research Institute of New Zealand（新西兰医学研究所）；Australian and New Zealand Intensive Care Research Centre, Monash University（莫纳什大学澳新重症监护研究中心）；University of Melbourne（墨尔本大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Yale School of Public Health（耶鲁公共卫生学院）
Hypothetical estimands in clinical trials: a unification of causal inference and missing data methods
ICH E9附录引入了术语并发事件，是指随机化后发生的事件，这些事件可能妨碍观察到关注的结局或影响其解释。它提出了五种处理并发事件的策略，以形成一个被估量，但没有提出估计的统计方法。在本文中，我们专注于假设的策略，其中的处理效应的假设情况下，其中并发事件是防止定义。对于它的估计，我们考虑了因果推断和缺失数据方法。我们建立了某些“因果推断估计量”是相同的某些“缺失数据估计量”。这些链接可能会帮助那些熟悉一套方法而不是另一套方法的人。此外，使用潜在结果符号使我们能够更清楚地说明缺失数据方法依赖于哪些假设来估计假设被估量。这有助于表明估计假设的被估量是否合理，以及在分析中应该使用什么数据。我们发现，假设的被估量可以估计并发事件发生后，这是通常不使用的数据利用。我们还提出了蒙特卡罗模拟，说明在不同的设置中的方法的实施和性能。
From Estimands to Robust Inference of Treatment Effects in Master Protocol Trials
University of Washington（华盛顿大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Eli Lilly and Company（礼来公司）；Food and Drug Administration（食品药品监督管理局）；Seattle Children’s Research Institute（西雅图儿童研究所）
Best for which estimand? A known-truth benchmark of longitudinal-matching and target-trial-emulation methods for time-varying treatments
School of Population and Public Health, University of British Columbia（不列颠哥伦比亚大学公共卫生与人口健康学院）；Centre for Advancing Health Outcomes, St. Paul’s Hospital（圣保罗医院健康结果促进中心）
Comparing causal estimands from sequential nested versus single point target trials: A simulation study
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Target RWE（目标真实世界证据）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Bloomberg School of Public Health, Johns Hopkins University（约翰霍普金斯大学彭博公共卫生学院）；Duke University（杜克大学）
Combining the target trial and estimand frameworks to define the causal estimand: an application using real-world data to contextualize a single-arm trial
Causal mediation in cluster-randomized trials with multiple mediators: spillover-aware decomposition, identification, and semiparametric efficient inference
Assessing Vaccine Effectiveness in Observational Studies via Nested Trial Emulation
Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校吉林斯全球公共卫生学院生物统计系）；Nuffield Department of Population Health, University of Oxford（牛津大学努菲尔德人口健康系）
Sorbonne Université（索邦大学）；Inserm（法国国家健康与医学研究院）；Sorbonne Public Health Institute（索邦公共卫生研究所）；Equipe PEPITES（PEPITES团队）；Assistance Publique - Hôpitaux de Paris（巴黎公立协助医院）；Hôpital Pitié Salpêtrière（皮蒂埃-萨尔佩特里耶尔医院）；Département de Santé Publique（公共卫生系）；Centre de Pharmacoépidémiologie (Cephepi)（药物流行病学中心）；APHP（巴黎公立协助医院）；Hôpital Saint-Antoine（圣安东医院）；Inria（法国国家计算机科学与控制研究院）；CNRS（法国国家科学研究中心）；Paris Brain Institute（巴黎脑研究所）
A Framework for Parametric Time-Varying Treatment Effects in Multiple Intervention Stepped Wedge Design Clinical Trials in the Presence of Non-Uniform Cluster-Period Correlation Structures
The Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）；UTHealth Houston School of Public Health, The University of Texas Health Science Center at Houston（休斯顿德克萨斯大学健康科学中心休斯顿公共卫生学院）
Reframing Population-Adjusted Indirect Comparisons as a Transportability Problem: An Estimand-Based Perspective and Implications for Health Technology Assessment
Counterfactual Uncertainty Quantification of Factual Estimand of Efficacy from Before-and-After Treatment Repeated Measures Randomized Controlled Trials
Department of Mathematics, The University of Manchester；Janssen Research；Pfizer Worldwide Research \& Development；Department of Statistics, The Ohio State University
