### 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, 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.

Original language | English (US) |
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Journal | Epilepsy Research |

DOIs | |

State | Accepted/In press - 2017 |

### Fingerprint

### Keywords

- Clinical trials
- Epilepsy
- Natural variability
- Placebo effect
- Prediction
- Seizure frequency

### ASJC Scopus subject areas

- Neurology
- Clinical Neurology

### Cite this

*Epilepsy Research*. https://doi.org/10.1016/j.eplepsyres.2017.07.013

**Does accounting for seizure frequency variability increase clinical trial power?** / Goldenholz, Daniel M.; Goldenholz, Shira R.; Moss, Robert; French, Jacqueline; Lowenstein, Daniel; Kuzniecky, Ruben; Haut, Sheryl R.; Cristofaro, Sabrina; Detyniecki, Kamil; Hixson, John; Karoly, Philippa; Cook, Mark; Strashny, Alex; Theodore, William H.; Pieper, Carl.

Research output: Contribution to journal › Article

*Epilepsy Research*. https://doi.org/10.1016/j.eplepsyres.2017.07.013

}

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 R.

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

PY - 2017

Y1 - 2017

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

UR - http://www.scopus.com/inward/record.url?scp=85028085955&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028085955&partnerID=8YFLogxK

U2 - 10.1016/j.eplepsyres.2017.07.013

DO - 10.1016/j.eplepsyres.2017.07.013

M3 - Article

C2 - 28781216

AN - SCOPUS:85028085955

JO - Epilepsy Research

JF - Epilepsy Research

SN - 0920-1211

ER -