Adjustment for Inconsistency in Adaptive Phase 2/3 Designs With Dose Optimization

Adaptive Phase 2/3 designs hold great promise in contemporary oncology drug development, especially when limited data from Phase 1 dose-finding is insufficient for identifying an optimal dose. However, inconsistent results between Phase 2 and Phase 3 may raise regulatory and practical concerns. The imperfection in dose selection further complicates the issue. In this paper, we explicitly incorporate the concerns about inconsistency into the statistical analysis under three hypothesis testing strategies (conservative, aggressive, and neutral) by specifying an inconsistency cutoff and accounting for the probability of "picking-the-winner." This investigation illustrates how to balance regulatory caution, sponsor interests, and practical considerations in adaptive Phase 2/3 designs with dose optimization, which paves the way for further research in a less explored area.

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Publication Details

Journal
Pharmaceutical Statistics
Published
2025-09-26
DOI
https://doi.org/10.1002/pst.70031
Primary Topic
Optimal Experimental Design Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Adjustment for Inconsistency in Adaptive Phase 2/3 Designs With Dose Optimization

Mo Huang, Xuekui Zhang, Cong Chen
Pharmaceutical Statistics
Optimal Experimental Design Methods
article

Adjustment for Inconsistency in Adaptive Phase 2/3 Designs With Dose Optimization

Mo Huang, Xuekui Zhang, Cong Chen
article en

Abstract

Adaptive Phase 2/3 designs hold great promise in contemporary oncology drug development, especially when limited data from Phase 1 dose-finding is insufficient for identifying an optimal dose. However, inconsistent results between Phase 2 and Phase 3 may raise regulatory and practical concerns. The imperfection in dose selection further complicates the issue. In this paper, we explicitly incorporate the concerns about inconsistency into the statistical analysis under three hypothesis testing strategies (conservative, aggressive, and neutral) by specifying an inconsistency cutoff and accounting for the probability of "picking-the-winner." This investigation illustrates how to balance regulatory caution, sponsor interests, and practical considerations in adaptive Phase 2/3 designs with dose optimization, which paves the way for further research in a less explored area.

Pharmaceutical StatisticsVol. 24(6)
Pfizer (United States) (US), University of Victoria (CA), Decision Sciences (United States) (US)
Michael Smith Health Research BC, Canada Research Chairs
Openalex Percentile: Top 69%
Optimal Experimental Design Methods
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