Related Experiment Video
Updated: Mar 12, 2026

A Model for Epilepsy of Infectious Etiology using Theiler's Murine Encephalomyelitis Virus
Published on: June 23, 2022
Treatment Trials for Neonatal Seizures: The Effect of Design on Sample Size
Nathan J Stevenson1,2, Geraldine B Boylan1, Lena Hellström-Westas3
1Irish Centre for Fetal and Neonatal Translational Research and Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
Insights
Optimizing randomized control trial (RCT) design is crucial for studying anti-epileptic drugs (AEDs) in neonatal seizures. Careful selection of outcome measures and control groups significantly influences the required sample size for effective clinical trials.
Area of Science:
- Neonatal Neurology
- Clinical Trial Design
- Pharmacology
Background:
- Neonatal seizures are common and treated with anti-epileptic drugs (AEDs).
- Current AEDs have suboptimal efficacy, necessitating new drug development.
- Randomized control trials (RCTs) are planned to evaluate novel AEDs.
Purpose of the Study:
- To determine how different randomized control trial (RCT) designs impact the required sample size for neonatal seizure treatment studies.
- To analyze the influence of outcome measures, control groups, and treatment delays on sample size calculations.
- To assess the false positive rate in uncontrolled study designs.
Main Methods:
- Simulated RCTs using seizure time courses from 41 neonates with hypoxic ischemic encephalopathy.
- Varied five outcome measures, three AED protocols, eight treatment delays (Td), and four efficacy levels.
- Performed power calculations and analyzed sample sizes for different simulated RCT designs.
Main Results:
- The choice of outcome measure had the largest impact on sample size (median 30.7-fold difference).
- Positive controls increased sample size by a median of 3.2-fold; treatment delays increased it by 2.1-fold.
- RCTs in hypothermic neonates required 2.6-fold larger sample sizes than in normothermic neonates.
Conclusions:
- RCT design profoundly influences the required sample size for neonatal seizure drug trials.
- Utilizing control groups, appropriate outcome measures, and controlling for treatment delays minimizes sample size.
- Optimized RCT design ensures trial validity and efficiency in evaluating new anti-epileptic drugs.
Abstract:
Neonatal seizures are common in the neonatal intensive care unit. Clinicians treat these seizures with several anti-epileptic drugs (AEDs) to reduce seizures in a neonate. Current AEDs exhibit sub-optimal efficacy and several randomized control trials (RCT) of novel AEDs are planned. The aim of this study was to measure the influence of trial design on the required sample size of a RCT. We used seizure time courses from 41 term neonates with hypoxic ischaemic encephalopathy to build seizure treatment trial simulations. We used five outcome measures, three AED protocols, eight treatment delays from seizure onset (Td) and four levels of trial AED efficacy to simulate different RCTs. We performed power calculations for each RCT design and analysed the resultant sample size. We also assessed the rate of false positives, or placebo effect, in typical uncontrolled studies. We found that the false positive rate ranged from 5 to 85% of patients depending on RCT design. For controlled trials, the choice of outcome measure had the largest effect on sample size with median differences of 30.7 fold (IQR: 13.7-40.0) across a range of AED protocols, Td and trial AED efficacy (p<0.001). RCTs that compared the trial AED with positive controls required sample sizes with a median fold increase of 3.2 (IQR: 1.9-11.9; p<0.001). Delays in AED administration from seizure onset also increased the required sample size 2.1 fold (IQR: 1.7-2.9; p<0.001). Subgroup analysis showed that RCTs in neonates treated with hypothermia required a median fold increase in sample size of 2.6 (IQR: 2.4-3.0) compared to trials in normothermic neonates (p<0.001). These results show that RCT design has a profound influence on the required sample size. Trials that use a control group, appropriate outcome measure, and control for differences in Td between groups in analysis will be valid and minimise sample size.
More Related Videos
10:46Author Spotlight: Obtaining High-Quality CSF and Blood Samples for Epilepsy Biomarker Discovery
Published on: September 1, 2023
04:53A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
Published on: September 20, 2019
Related Concept Videos
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs
Study Design in Statistics
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...