Industrialization of Bayesian decision-making for proof-of-commercial-concept study designs.

Authors

Fan Wu, Pascal Minini, Gang Han, Bingzhi Zhang, Meehyung Cho, Xun Chen, Eugene Andre Houseman

Published in

Journal of biopharmaceutical statistics. Pages 1-16. Jul 21, 2026. Epub Jul 21, 2026.

Abstract

HERALD (Holistic Evolving ReAssessment-Leveraged Decision-making) is a Bayesian decision-making framework anchored in the prediction of phase 3 efficacy success. At the proof-of-commercial-concept (POCC) study design stage, HERALD links available phase 3 design assumptions and success criteria with potential POCC treatment effects and yields decision boundaries that guarantee sufficient probability of success in phase 3. Since its commencement, HERALD has been widely adopted at Sanofi and received endorsement from the governance and key stakeholders across therapeutic areas. In this manuscript, we introduce how HERALD at the POCC design stage is industrialized. Through development of statistical software, standardization of governance presentation, consolidation of implementation examples, and periodical engagement programs of role-tailored trainings, we streamline the decision-making criteria discussion and empower both statisticians and nonstatisticians to communicate design options more efficiently within a cross-functional team.

PMID:
42478184
Bibliographic data and abstract were imported from PubMed on 21 Jul 2026.

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