Pharm Stat. 2026 Mar-Apr;25(2):e70086. doi: 10.1002/pst.70086.
ABSTRACT
Well-controlled clinical trials employ careful processes to reduce bias, often blinding investigators and sponsors to prevent knowledge of study outcomes and potential operational bias. Quality assurance of outcomes is also ensured through designation of unblinded data managers and statisticians, so that complex adaptive designs with multiple interim analyses can be executed. Our approach addresses potential ad-hoc requests by the Data and Safety Monitoring Board (DSMB) for monitoring safety, efficacy, and ethical oversight. A novel approach utilizing current trial data is proposed to predict trial outcomes for blinded decision-makers without unblinding those that should stay blinded. Bayesian predictive power, a trial prediction method, is employed and illustrated on simulated data. This study presents an approach for presenting updated Bayesian predictive power in complex adaptive designs, exemplified by the Hyperbaric Oxygen Brain Injury Treatment (HOBIT) trial. Simulation examples motivated from the trial demonstrate the utility of Bayesian predictive power in predicting trial outcomes and sample size distribution, aiding in resource allocation and decision-making with different reports for blinded and unblinded teams. Bayesian predictive power calculations offer valuable insights into future trial behavior for both blinded and unblinded groups, aiding in guidance during trial conduction. The approach outlined in this short communication can be applied to various trial designs.
PMID:41849677 | DOI:10.1002/pst.70086
