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Published on: October 6, 2015
Development and Evaluation of a Simulation-Based Algorithm to Optimize the Planning of Interim Analyses for Clinical
Jordi W J van Unnik1, Stavros Nikolakopoulos1, Marinus J C Eijkemans1
1From 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.
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 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.
