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Interpreting Treatment Effects Using Posterior Probabilities: A Bayesian Reanalysis of 230 Phase III Oncology Trials
Alexander D Sherry1,2, Pavlos Msaouel3,4, Gabrielle S Kupferman1
1Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
JCO Clinical Cancer Informatics
|May 1, 2026
Summary
Posterior probability analysis of phase III oncology trials reveals discrepancies with traditional P-value thresholds. Bayesian models offer unique interpretative value for clinical relevance, improving trial interpretation.
Area of Science:
- Oncology
- Biostatistics
- Clinical Trials
Background:
- Traditional oncology trials use P-value thresholds for superiority, which are often misinterpreted.
- Posterior probability offers a direct estimation of hypothesis probability, enhancing interpretation.
Purpose of the Study:
- To reanalyze phase III oncology trials using posterior probability.
- To benchmark posterior probability against standard statistical significance interpretation.
Main Methods:
- Reconstructed outcomes from 194,129 patients across 230 phase III oncology trials.
- Calculated posterior probabilities for treatment effect using various priors and clinically relevant effect sizes, including minimum clinically important difference (MCID).
Main Results:
- Trials deemed superior by P-values showed >90% probability for marginal benefits (HR < 1).
- Fewer positive trials met the ASCO (HR < 0.8) or ESMO (HR < 0.64) MCID thresholds with >90% probability.
- Some trials not deemed superior still had >90% probability for marginal benefits.
Conclusions:
- Bayesian models and posterior probability offer unique interpretative value in oncology trials.
- Posterior probability can bridge the gap between refuting the null hypothesis and identifying clinically relevant effects.
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