Biostatistical Design in Device Clinical Trials

Abstract

Medical device clinical trials present unique biostatistical challenges absent from pharmaceutical trials: operator learning curves confound early performance data, device iteration during development creates mid-trial design changes, patient-device interaction variability exceeds drug pharmacokinetic variability, and objective performance criteria (OPC) based on historical controls replace randomised comparisons for many device categories. Traditional fixed-sample parallel-group designs adapted from drug trials often produce underpowered or inefficient device evaluations because they fail to account for these device-specific complexities. This study presents the Biostatistical Design Assessment Framework for Device Trials (BDAFDT), evaluating five trial design strategies -- fixed-sample single-arm OPC, fixed-sample randomised controlled trial, Bayesian adaptive design with informative priors, group sequential design with interim analyses, and platform trial with master protocol -- across four design efficiency tasks: sample size optimisation, learning curve accommodation, design change flexibility, and evidence strength for regulatory submission. Our Trial Design Efficiency Score (TDES) integrates statistical power, sample efficiency, adaptive capability, regulatory acceptance, and operational feasibility. Bayesian adaptive design achieved the highest TDES (0.926) through informative priors incorporating predicate device data that reduced required sample sizes by 34% while maintaining 90% power, and platform trials achieved the highest design flexibility (0.955) through master protocols enabling seamless addition of new device arms without restarting the trial infrastructure.