Daniel M Goldenholz1, Shira R Goldenholz2, Robert Moss3, Jacqueline French4, Daniel Lowenstein5, Ruben Kuzniecky6, Sheryl Haut7, Sabrina Cristofaro8, Kamil Detyniecki9, John Hixson10, Philippa Karoly11, Mark Cook12, Alex Strashny13, William H Theodore14, Carl Pieper15. 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. Published by Elsevier B.V.
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. Published by Elsevier B.V.
Authors: J A French; B W Abou-Khalil; R F Leroy; E M T Yacubian; P Shin; S Hall; H Mansbach; V Nohria Journal: Neurology Date: 2011-03-30 Impact factor: 9.910
Authors: Daniel M Goldenholz; Shira R Goldenholz; Juan Romero; Rob Moss; Haoqi Sun; Brandon Westover Journal: Ann Neurol Date: 2020-07-09 Impact factor: 10.422
Authors: Daniel M Goldenholz; Robert Moss; David A Jost; Nathan E Crone; Gregory Krauss; Rosalind Picard; Chiara Caborni; Jose E Cavazos; John Hixson; Tobias Loddenkemper; Tracy Dixon Salazar; Laura Lubbers; Lauren C Harte-Hargrove; Vicky Whittemore; Jonas Duun-Henriksen; Eric Dolan; Nitish Kasturia; Mark Oberemk; Mark J Cook; Mark Lehmkuhle; Michael R Sperling; Patricia O Shafer Journal: Epilepsia Date: 2018-03-31 Impact factor: 5.864
Authors: Daniel M Goldenholz; Shira R Goldenholz; Robert Moss; Jacqueline French; Daniel Lowenstein; Ruben Kuzniecky; Sheryl Haut; Sabrina Cristofaro; Kamil Detyniecki; John Hixson; Philippa Karoly; Mark Cook; Alex Strashny; William H Theodore Journal: Ann Clin Transl Neurol Date: 2018-01-09 Impact factor: 4.511
Authors: Victor Ferastraoaru; Daniel M Goldenholz; Sharon Chiang; Robert Moss; William H Theodore; Sheryl R Haut Journal: Epilepsia Open Date: 2018-07-04