Time-to-event estimands are central to many oncology clinical trials. The estimand framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest and requires predefining the handling of intercurrent events that occur after treatment initiation and either preclude the observation of an event of interest or impact the interpretation of the treatment effect. We discuss a practical problem in clinical trial design and execution, i.e. in some clinical contexts it is not feasible to systematically follow patients to an event of interest. Loss to follow-up in the presence of intercurrent events can affect the meaning and interpretation of the study results. We provide recommendations for trial design, stressing the need for close alignment of the clinical question of interest and study design, impact on data collection and other practical implications. When patients cannot be systematically followed, compromise may be necessary to select the best available estimand that can be feasibly estimated under the circumstances. We discuss the use of sensitivity and supplementary analyses to examine assumptions of interest.
The Role of Occlusion: Potential Extension of the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis for Time-to-Event Oncology Studies
ICH E9（R1）被估量指南1术语未完全解决复杂肿瘤学背景下至事件时间被估量的概念需求。我们之前描述了如何将至事件时间终点的删失和删失机制嵌入ICH E9（R1）估计指南术语中。这是制药行业工作组关于肿瘤学删失机制估计量的第二篇论文，讨论了肿瘤学临床背景下的特殊问题，这些问题可能需要与其他一些治疗领域不同的方法以及ICH E9（R1）指南的扩展。删失的概念是在闭塞事件的更广泛背景下讨论的，闭塞代表任何进一步随访的损失和/或从分析中删除进一步收集的数据。闭塞构成了比被估量指南的并发事件和终末事件术语更广泛的概念，适用于描述和处理退出评估或不同被估量要求冲突的情况。我们的特点，提供更多的细节，实际影响，并在每个被估量的策略处理闭塞事件的应用程序的例子。
Estimands and Their Implications for Evidence Synthesis for Oncology: A Simulation Study of Treatment Switching in Meta-Analysis
Core Clinical Sciences Inc.；Centre for Advancing Health Outcomes, University of British Columbia（不列颠哥伦比亚大学健康结果促进中心）；Novo Nordisk Pharma（诺和诺德制药）；Novo Nordisk A/S（诺和诺德公司）；KeeneONStatistics；Department of Health Research Methodology, Evidence, and Impact, McMaster University（麦克马斯特大学健康研究方法、证据与影响系）
Quantifying Uncertainty in Future Event Counts at Interim Analyses of Time-to-Event Trials, with an Application to Pediatric Oncology
Bicocca Bioinformatics, Biostatistics and Bioimaging Centre B4School of Medicine and Surgery, University of Milano–Bicocca（米兰-比可卡大学医学与外科学院）；University of Warwick（华威大学）；Fondazione IRCCS San Gerardo dei Tintori（圣杰拉尔多蒂托里IRCCS基金会）
Personalized Oncology: Feasibility of Evaluating Treatment Effects for Individual Patients
Institute of Public Health, Charité – Universitätsmedizin Berlin（公共卫生研究所，柏林夏里特医科大学）；Digital Health – Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany（德国波茨坦哈索·普拉特纳数字工程学院数字健康-机器学习研究组）；Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA（美国纽约西奈山伊坎医学院西奈山数字健康哈索·普拉特纳研究所）
Centrality in Collaboration: A Novel Algorithm for Social Partitioning Gradients in Community Detection for Multiple Oncology Clinical Trial Enrollments
University of Toronto（多伦多大学）；University Health Network（大学健康网络）
Evaluating treatment effects on longitudinal outcomes with attrition due to death: Methods for a two-dimentional estimand with a case study in Quality of Life
Ghent University（根特大学）；Leiden University Medical Center（莱顿大学医学中心）；Pfizer Inc（辉瑞公司）；Oslo University Hospital（奥斯陆大学医院）；European Organisation for Research and Treatment of Cancer (EORTC) Headquarters（欧洲癌症研究与治疗组织（EORTC）总部）；University of Freiburg（弗赖堡大学）
Learning from Literature: Integrating LLMs and Bayesian Hierarchical Modeling for Oncology Trial Design
Yale Cancer Center, Yale School of Medicine（耶鲁癌症中心，耶鲁医学院）；Pfizer Inc（辉瑞公司）；Public Health Sciences Division, Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心公共卫生科学部）
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）
Factorial clinical trials in the presence and absence of plausible statistical interactions between treatment factors. A historical review of the methodological literature
University of Leeds（利兹大学）；HITAP（卫生技术评估研究所）；University of Nottingham（诺丁汉大学）；King’s College London（伦敦国王学院）
Win Time In Favor of Treatment (WINFT) for Hierarchical Endpoints
North Carolina State University（北卡罗来纳州立大学）；Duke Clinical Research Institute, Duke University（杜克大学杜克临床研究所）；Department of Biostatistics and Bioinformatics, Duke University（杜大学生统计与生物信息系）；Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison（威斯康星大学麦迪逊分校医学院公共卫生学院生物统计与医学信息系）
A Bayesian hierarchical mixture cure modelling framework to utilize multiple survival datasets for long-term survivorship estimates: A case study from previously untreated metastatic melanoma
UCL（伦敦大学学院）；Bristol Myers Squibb（百时美施贵宝）；The Royal Marsden Hospital（皇家马斯登医院）
