Monte Carlo simulations of randomized clinical trials in epilepsy. Issue 8 (24th May 2017)

• Record Type: Journal Article
• Authors: Goldenholz, Daniel M.; Tharayil, Joseph; Moss, Robert; Myers, Evan; Theodore, William H.
• Is Part Of: Annals of clinical and translational neurology. Volume 4:Issue 8(2017)
• Page Start: 544
• Page End: 552
• Publication Date: 2017-05-24
• Subjects: Nervous system -- Diseases -- Periodicals; Neurology -- Periodicals; 616.8005
• DOI: 10.1002/acn3.426
• Languages: English
• ISSNs: 2328-9503

Abstract

Abstract: Background: The placebo response in epilepsy randomized clinical trials (RCTs) has recently been shown to largely reflect underlying natural variability in seizure frequency. Based on this observation, we sought to explore the parameter space of RCT design to optimize trial efficiency and cost. Methods: We used one of the world's largest patient reported seizure diary databases, SeizureTracker.com to derive virtual patients for simulated RCTs. We ran 1000 randomly generated simulated trials using bootstrapping (sampling with replacement) for each unique combination of trial parameters, sweeping a large set of parameters in durations of the baseline and test periods, number of patients, eligibility criteria, drug effect size, and patient dropout. We studied the resulting trial efficiency and cost. Results: A total of 6, 732, 000 trials were simulated, drawing from 5097 patients in the database. We found that the strongest regression predictors of placebo response were durations of baseline and test periods. Drug effect size had a major impact on trial efficiency and cost. Dropout did not have a major impact on trial efficiency or cost. Eligibility requirements impacted trial efficiency to a limited extent. Cost was minimized while maintaining statistical integrity with very short RCT durations. Discussion: This study suggests that RCT parameters can be improved over current practice to reduce costs while maintaining statistical power. In addition, use of a large‐scale population dataset in a massively parallel computing analysis allows exploration of the wider parameter space of RCT design prior to running a trial, which could help accelerate drug discovery and approval.