ArticlemedRxiv : the preprint server for health sciences2024
Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials.
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
importanceImproving the efficiency of interim assessments in phase III trials should reduce trial costs, hasten the approval of efficacious therapies, and mitigate patient exposure to disadvantageous randomizations.
objectiveWe hypothesized that
designCross-sectional analysis.
setting230 randomized phase III oncology trials enrolling 184,752 participants.
participantsIndividual patient-level data were manually reconstructed from primary endpoint Kaplan-Meier curves.
interventionsTrial accruals were simulated 100 times per trial and leveraged published patient outcomes such that only the accrual dynamics, and not the patient outcomes, were randomly varied. MAIN OUTCOMES AND MEASURES: Early stopping was triggered per simulation if interim analysis demonstrated ≥ 85% probability of minimum clinically important difference/3 for efficacy or futility. Trial-level early closure was defined by stopping frequencies ≥ 0.75.
resultsA total of 12,451 simulations (54%) met early stopping criteria. Trial-level early stopping frequency was highly predictive of the published outcome (OR, 7.24; posterior probability of association, >99.99%; AUC, 0.91; CONCLUSIONS AND RELEVANCE: Bayesian interim analyses may improve randomized trial efficiency by reducing enrollment requirements without compromising trial interpretation. Increased utilization of Bayesian interim analysis has the potential to reduce costs of late-phase trials, reduce patient exposures to ineffective therapies, and accelerate approvals of effective therapies. KEY POINTS:
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Registered trials
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