Does accounting for seizure frequency variability increase clinical trial power?

ObjectiveSeizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, ZV.MethodsTwo models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, ZV. Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the ZV method on three datasets (SeizureTracker: n=3016, Human Epilepsy Project: n=107, and NeuroVista: n=15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N=100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. "Power" was determined as the percentage of trials successfully distinguishing placebo from drug (p<0.05).ResultsPrediction accuracy across datasets was, ZV: 91-100%, RR50: 42-80%. Simulated RCT ZV analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm.SignificanceZV may increase the statistical power of an RCT relative to the traditional RR50

Does accounting for seizure frequency variability increase clinical trial power?

Objective: Seizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, Z(V). Methods: Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, Z(V). Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the Z(V) method on three datasets (SeizureTracker: n = 3016, Human Epilepsy Project: n = 107, and NeuroVista: n = 15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N = 100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. "Power" was determined as the percentage of trials successfully distinguishing placebo from drug (p < 0.05). Results: Prediction accuracy across datasets was, Z(V): 91-100%, RR50: 42-80%. Simulated RCT Z(V) analysis achieved > 90% power at N = 100 per arm while RR50 required N = 200 per arm. Significance: Z(V), may increase the statistical power of an RCT relative to the traditional RR50