From the Department of Neurology (J.W.J.v.U., L.H.v.d.B., R.P.A.v.E.), UMC Utrecht Brain Center, and Biostatistics & Research Support (S.N., M.J.C.E., R.P.A.v.E.), Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands; Sorbonne Université (J.G.-B., C.M.-P., T.S.), INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, Site Pitié-Salpêtrière, Département R3S; APHP (G.B.), Groupe Hospitalier Paris 6, Hôpital Pitié-Salpêtrière, Département de Neurologie, Centre Référent SLA, France; Department of Neurology (M.E.C.), Massachusetts General Hospital, Boston; and Sheffield Institute for Translational Neuroscience (C.J.M.), University of Sheffield, United Kingdom.
Neurology. 2023 Jun 6;100(23):e2398-e2408. doi: 10.1212/WNL.0000000000207306. Epub 2023 Apr 21.
Late-phase clinical trials for neurodegenerative diseases have a low probability of success. In this study, we introduce an algorithm that optimizes the planning of interim analyses for clinical trials in amyotrophic lateral sclerosis (ALS) to better use the time and resources available and minimize the exposure of patients to ineffective or harmful drugs.
A simulation-based algorithm was developed to determine the optimal interim analysis scheme by integrating prior knowledge about the success rate of ALS clinical trials with drug-specific information obtained in early-phase studies. Interim analysis schemes were optimized by varying the number and timing of interim analyses, together with their decision rules about when to stop a trial. The algorithm was applied retrospectively to 3 clinical trials that investigated the efficacy of diaphragm pacing or ceftriaxone on survival in patients with ALS. Outcomes were additionally compared with conventional interim designs.
We evaluated 183-1,351 unique interim analysis schemes for each trial. Application of the optimal designs correctly established lack of efficacy, would have concluded all studies 1.2-19.4 months earlier (reduction of 4.6%-57.7% in trial duration), and could have reduced the number of randomized patients by 1.7%-58.1%. By means of simulation, we illustrate the efficiency for other treatment scenarios. The optimized interim analysis schemes outperformed conventional interim designs in most scenarios.
Our algorithm uses prior knowledge to determine the uncertainty of the expected treatment effect in ALS clinical trials and optimizes the planning of interim analyses. Improving futility monitoring in ALS could minimize the exposure of patients to ineffective or harmful treatments and result in significant ethical and efficiency gains.
神经退行性疾病的后期临床试验成功率较低。本研究引入一种算法，旨在优化肌萎缩侧索硬化症（ALS）临床试验的中期分析计划，以更好地利用现有时间和资源，并最大限度地减少患者接触无效或有害药物的风险。
开发了一种基于模拟的算法，通过将 ALS 临床试验成功率的先验知识与早期研究中获得的药物特异性信息相结合，确定最佳中期分析方案。通过改变中期分析的数量和时间，以及它们关于何时停止试验的决策规则，优化中期分析方案。该算法被应用于回顾性分析 3 项研究膈肌起搏或头孢曲松对 ALS 患者生存影响的临床试验。并将结果与传统中期设计进行了比较。
我们评估了每个试验的 183-1351 个独特的中期分析方案。应用最佳设计可以正确确定疗效不佳，并将所有研究提前结束 1.2-19.4 个月（试验持续时间缩短 4.6%-57.7%），且可减少 1.7%-58.1%的随机患者数量。通过模拟，我们展示了其他治疗场景的效率。在大多数情况下，优化的中期分析方案优于传统的中期设计。
我们的算法利用先验知识确定 ALS 临床试验中预期治疗效果的不确定性，并优化中期分析计划。改善无效性监测可以最大限度地减少患者接触无效或有害治疗的风险，并带来显著的伦理和效率收益。
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