UCSF

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.