Fleming Thomas R, Carroll Kevin J, Wittes Janet T, Emerson Scott S, Rothmann Mark D, Collins Sylva, Levin Gregory
Department of Biostatistics, University of Washington, Seattle, Washington, USA.
KJC Statistics, LTD, Woodford, UK.
Stat Med. 2025 May;44(10-12):e70104. doi: 10.1002/sim.70104.
In randomized clinical trials, stopping study medication, use of rescue treatment, and other intercurrent events can complicate interpretation of results. The ICH E9(R1) Addendum on estimands stimulated important cross-disciplinary discussions on trial objectives. Unfortunately, with influence of the Addendum, many trials have proposed analyzing primary endpoints using "while on treatment", "hypothetical", or "principal stratum" strategies that handle intercurrent events in ways that use post-randomization outcomes to exclude information from randomized participants and don't preserve integrity of randomization, or that don't reliably capture the intervention's meaningful net effects. These approaches have inherent limitations in ability to draw scientifically rigorous inference on clinically relevant causal effects important for decisions about adopting interventions. We describe advantages of trials with standard-of-care control arms targeting estimands using "treatment policy" approaches for intercurrent events, while potentially incorporating other meaningful intercurrent events, such as death, into the primary endpoint applying a composite strategy. Well-designed and -conducted trials targeting such estimands achieve scientifically rigorous causal inference through analyzes that protect the integrity of randomization. Such estimands also provide meaningful information relevant to real-world settings because they (1) are unconditional in nature i.e., they don't condition on post-treatment circumstances that might not be many participants' experiences; and (2) properly evaluate the experimental intervention within a regimen that includes possible ancillary care that would be clinically appropriate in real-world settings. We hope to add clarity about appropriate strategies for intercurrent events and how to improve design, conduct, and analysis of clinical trials to address questions of greatest clinical importance reliably.
在随机临床试验中,停止研究用药、使用挽救治疗以及其他并发事件会使结果的解释变得复杂。国际人用药品注册技术协调会(ICH)关于估计量的E9(R1)增编引发了关于试验目标的重要跨学科讨论。不幸的是,在该增编的影响下,许多试验提议使用“治疗期间”“假设性”或“主要层”策略来分析主要终点,这些策略处理并发事件的方式是利用随机化后的结果排除随机分组参与者的信息,且不保持随机化的完整性,或者不能可靠地捕捉干预措施有意义的净效应。这些方法在对采用干预措施的决策至关重要的临床相关因果效应进行科学严谨推断的能力方面存在固有局限性。我们描述了采用针对并发事件的“治疗策略”方法、以标准治疗对照臂为目标估计量的试验的优势,同时可能将其他有意义的并发事件(如死亡)纳入主要终点并应用复合策略。针对此类估计量精心设计和实施的试验通过保护随机化完整性的分析实现科学严谨的因果推断。此类估计量还提供与现实世界情况相关的有意义信息,因为它们(1)本质上是无条件的,即它们不依赖于可能并非许多参与者经历的治疗后情况;(2)在包含可能在现实世界临床环境中合适的辅助治疗的治疗方案内正确评估实验性干预措施。我们希望能更清晰地阐明处理并发事件的适当策略,以及如何改进临床试验的设计、实施和分析,以可靠地解决最重要的临床问题。
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