Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK.
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Trials. 2023 Apr 25;24(1):294. doi: 10.1186/s13063-023-07265-5.
BACKGROUND: Surgical interventions are complex. Key elements of this complexity are the surgeon and their learning curve. They pose methodological challenges in the design, analysis and interpretation of surgical RCTs. We identify, summarise, and critically examine current guidance about how to incorporate learning curves in the design and analysis of RCTs in surgery. EXAMINING CURRENT GUIDANCE: Current guidance presumes that randomisation must be between levels of just one treatment component, and that the evaluation of comparative effectiveness will be made via the average treatment effect (ATE). It considers how learning effects affect the ATE, and suggests solutions which seek to define the target population such that the ATE is a meaningful quantity to guide practice. We argue that these are solutions to a flawed formulation of the problem, and are inadequate for policymaking in this setting. REFORMULATING THE PROBLEM: The premise that surgical RCTs are limited to single-component comparisons, evaluated via the ATE, has skewed the methodological discussion. Forcing a multi-component intervention, such as surgery, into the framework of the conventional RCT design ignores its factorial nature. We briefly discuss the multiphase optimisation strategy (MOST), which for a Stage 3 trial would endorse a factorial design. This would provide a wealth of information to inform nuanced policy but would likely be infeasible in this setting. We discuss in more depth the benefits of targeting the ATE conditional on operating surgeon experience (CATE). The value of estimating the CATE for exploring learning effects has been previously recognised, but with discussion limited to analysis methods only. The robustness and precision of such analyses can be ensured via the trial design, and we argue that trial designs targeting CATE represent a clear gap in current guidance. CONCLUSION: Trial designs that facilitate robust, precise estimation of the CATE would allow for more nuanced policymaking, leading to patient benefit. No such designs are currently forthcoming. Further research in trial design to facilitate the estimation of the CATE is needed.
背景：手术干预较为复杂。其复杂性的关键要素包括外科医生及其学习曲线。在设计、分析和解释外科 RCT 时，这些要素给方法学带来了挑战。我们确定、总结并批判性地审查了当前关于如何在外科 RCT 设计和分析中纳入学习曲线的指导意见。
审视当前指导：当前的指导意见假定随机化必须在一个治疗因素的不同水平之间进行，并且通过平均治疗效果（ATE）评估比较效果。它考虑了学习效果如何影响 ATE，并提出了一些解决方案，旨在通过定义目标人群，使 ATE 成为指导实践的有意义的数量。我们认为，这些是对问题的错误表述的解决方案，在这种情况下不足以制定政策。
重新表述问题：外科 RCT 仅限于单因素比较，通过 ATE 进行评估的前提假设，使方法学讨论产生了偏差。将多因素干预（如手术）强制纳入传统 RCT 设计框架，忽略了其组合性质。我们简要讨论了多阶段优化策略（MOST），对于 3 期试验，该策略将支持组合设计。这将提供丰富的信息，以提供细致的政策建议，但在这种情况下可能不可行。我们更深入地讨论了根据手术医生经验（CATE）目标 ATE 的好处。以前已经认识到估计 CATE 以探索学习效果的价值，但仅限于分析方法的讨论。通过试验设计可以确保此类分析的稳健性和精确性，我们认为，针对 CATE 的试验设计代表了当前指导意见中的一个明显差距。
结论：有助于稳健、精确估计 CATE 的试验设计将允许更细致的决策制定，从而使患者受益。目前没有这样的设计。需要进一步研究试验设计，以促进 CATE 的估计。
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