ArticleBritish journal of cancer2025
Bayesian interim analysis and efficiency of phase III randomized trials.
Article in British journal of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Overall survival in oncology phase III trials: an individual patient level meta-analysis (OPTIMA).The oncologist · 2026Pooled it
- The added value of Bayesian interim analysis in randomized phase II and phase III clinical trials in hemato-oncology.Haematologica · 2026Article
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14 authors.
Funding
Abstract
backgroundImproving efficiency of phase III trials is paramount for reducing costs, hastening approvals, and mitigating exposure to disadvantageous randomizations. Compared to standard frequentist interim analysis, Bayesian early stopping rules may improve efficiency by the flexibility of differential priors for efficacy and futility coupled with evaluation of clinically meaningful effect sizes.
methodsIndividual patient-level data from 184,752 participants across 230 randomized two-arm parallel oncology phase III trials were manually reconstructed from primary endpoint Kaplan-Meier curves. Accrual dynamics, but not patient outcomes, were randomly varied. Bayesian Cohen's κ assessed agreement between the original analysis and the Bayesian interim analysis.
resultsTrial-level early closure was recommended based on the Bayesian interim analysis for 82 trials (36%), including 62 trials which had performed frequentist interim analysis and 33 which were already closed early by the frequentist interim analysis. Bayesian early stopping rules were 96% sensitive for detecting trials with a primary endpoint difference, and there was a high level of agreement in overall trial interpretation (κ, 0.95). Moreover, Bayesian interim analysis was associated with reduced enrollment.
conclusionsBayesian interim analyses seem to improve trial efficiency by reducing enrollment requirements without compromising interpretation.
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