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

Does accounting for seizure frequency variability increase clinical trial power?

Author information

1
Clinical Epilepsy Section, NINDS, NIH, United States; Division of Epilepsy, Beth Israel Deaconess Medical Center. Electronic address: daniel.goldenholz@bidmc.harvard.edu.
2
Division of Epilepsy, Beth Israel Deaconess Medical Center. Electronic address: shira.r.g@gmail.com.
3
SeizureTracker LLC, United States. Electronic address: rob@seizuretracker.com.
4
Department of Neurology,New York University, United States. Electronic address: jacqueline.french@nyumc.org.
5
Department of Neurology, UCSF, United States. Electronic address: Lowenstein@ucsf.edu.
6
Department of Neurology,New York University, United States. Electronic address: Ruben.Kuzniecky@nyumc.org.
7
Department of Neurology, Montefiore Medical Center/Albert Einstein College of Medicine, United States. Electronic address: SHAUT@montefiore.org.
8
Department of Neurology,New York University, United States. Electronic address: Sabrina.Cristofaro@nyumc.org.
9
Department of Neurology, Yale University, United States. Electronic address: kamil.detyniecki@yale.edu.
10
Department of Neurology, UCSF, United States. Electronic address: John.Hixson@ucsf.edu.
11
University of Melbourne, Australia. Electronic address: p.karoly@student.unimelb.edu.au.
12
University of Melbourne, Australia. Electronic address: markcook@unimelb.edu.au.
13
Department of Neurology, Centers for Disease Control, United States. Electronic address: kpr9@cdc.gov.
14
Clinical Epilepsy Section, NINDS, NIH, United States. Electronic address: TheodorW@ninds.nih.gov.
15
Duke University Medical Center, Dept. of Biostatistics and Bioinformatics, United States. Electronic address: carl.pieper@duke.edu.

Abstract

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

PMID:
28781216
PMCID:
PMC5650933
DOI:
10.1016/j.eplepsyres.2017.07.013
[Indexed for MEDLINE]
Free PMC Article

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