Does accounting for seizure frequency variability increase clinical trial power?

Epilepsy Res. 2017 Nov;137:145-151. doi: 10.1016/j.eplepsyres.2017.07.013. Epub 2017 Jul 25.


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, ZV.

Methods: Two 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).

Results: Prediction 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.

Significance: ZV may increase the statistical power of an RCT relative to the traditional RR50.

Keywords: Clinical trials; Epilepsy; Natural variability; Placebo effect; Prediction; Seizure frequency.

Publication types

  • Research Support, N.I.H., Intramural

MeSH terms

  • Anticonvulsants / therapeutic use*
  • Computer Simulation
  • Data Interpretation, Statistical*
  • Humans
  • Models, Statistical*
  • Randomized Controlled Trials as Topic / methods*
  • Reproducibility of Results
  • Seizures / drug therapy*
  • Seizures / physiopathology*
  • Treatment Outcome


  • Anticonvulsants