Development and Evaluation of a Simulation-Based Algorithm to Optimize the Planning of Interim Analyses for Clinical Trials in ALS
van Unnik, Jordi W J; Nikolakopoulos, Stavros; Eijkemans, Marinus J C; Gonzalez-Bermejo, Jésus; Bruneteau, Gaelle; Morelot-Panzini, Capucine; van den Berg, Leonard H; Cudkowicz, Merit E; McDermott, Christopher J; Similowski, Thomas; van Eijk, Ruben P A
(2023) Neurology, volume 100, issue 23, pp. e2398 - e2408
(Article)
Abstract
BACKGROUND AND OBJECTIVES: 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
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patients to ineffective or harmful drugs. METHODS: 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. RESULTS: 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. DISCUSSION: 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.
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Keywords: Amyotrophic Lateral Sclerosis/drug therapy, Computer Simulation, Humans, Medical Futility, Research Design, Retrospective Studies, Uncertainty, Journal Article
ISSN: 0028-3878
Publisher: Lippincott Williams & Wilkins
Note: Funding Information: The Article Processing Charge was funded by Wolters Kluwer/UKB VSNU Agreement. Funding Information: This study was funded by Stichting ALS Nederland (TRICALS-Reactive II). The DiPALS study was funded by the National Institute for Health Research Health Technology Assessment program (09/55/33) and by the Motor Neurone Disease Association of England, Wales, and Northern Ireland. The RespiStimALS study was funded by the Hospital Program for Clinical Research, French Ministry of Health (grant no. P110133), and by a “Contrat de Recherche Clinique” of the Direction de la Recherche Clinique et du Développement (DRCD), Assistance Publique-Hôpitaux de Paris, Paris, France (grant no. CRC15017-R02), which also served as the study sponsor. It was also funded by the French patients' association for ALS research (“Association pour la Rercherche sur la Sclérose Latérale Amyotrophique,” ARSLA) and by the Thierry de Latran foundation for Amyotrophic Lateral Sclerosis. The Ceftriaxone-ALS study was funded by the National Institute of Neurological Disorders and Stroke (grant 5 U01-NS-049640). Publisher Copyright: © American Academy of Neurology.
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