TY - JOUR

T1 - Does accounting for seizure frequency variability increase clinical trial power?

AU - Goldenholz, Daniel M.

AU - Goldenholz, Shira R.

AU - Moss, Robert

AU - French, Jacqueline

AU - Lowenstein, Daniel

AU - Kuzniecky, Ruben

AU - Haut, Sheryl

AU - Cristofaro, Sabrina

AU - Detyniecki, Kamil

AU - Hixson, John

AU - Karoly, Philippa

AU - Cook, Mark

AU - Strashny, Alex

AU - Theodore, William H.

AU - Pieper, Carl

N1 - Funding Information:
This research was funded in part by the National Institutes of Neurological Disorders and Stroke, Intramural Research Division.
Publisher Copyright:
© 2017

PY - 2017/11

Y1 - 2017/11

N2 - 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.

AB - 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.

KW - Clinical trials

KW - Epilepsy

KW - Natural variability

KW - Placebo effect

KW - Prediction

KW - Seizure frequency

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U2 - 10.1016/j.eplepsyres.2017.07.013

DO - 10.1016/j.eplepsyres.2017.07.013

M3 - Article

C2 - 28781216

AN - SCOPUS:85028085955

SN - 0920-1211

VL - 137

SP - 145

EP - 151

JO - Epilepsy Research

JF - Epilepsy Research

ER -