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Adaptive enrichment with subpopulation selection at interim: methodologies, applications and design considerations.
Sue-Jane Wang1, H M James Hung
1Office of Biostatistics, OTS/CDER, FDA, Silver Spring, MD 20993, USA.
Adaptive enrichment strategies in drug development personalize medicine by adapting trial populations and statistical methods. This approach, illustrated by a case example, faces implementation challenges but aids regulatory approval.
Area of Science:
- Clinical Trial Design
- Pharmacoeconomics
- Biostatistics
Background:
- Personalized medicine drives interest in adaptive enrichment for drug development.
- Various adaptive enrichment methodologies exist for exploratory and confirmatory trials.
- Adaptive enrichment aims to optimize drug development through flexible trial designs.
Purpose of the Study:
- To provide an overview of adaptive enrichment methodologies in statistical literature.
- To illustrate adaptive enrichment with a regulatory approval case example.
- To discuss challenges and design considerations for confirmatory adaptive enrichment trials.
Main Methods:
- Overview of statistical methodologies for adaptive enrichment.
- Case study analysis of a drug approval involving population and statistical information adaptation.
- Assessment of treatment effect consistency using Wang et al. (2013) approach.
Main Results:
- A case example demonstrates successful drug approval using adaptive enrichment.
- Key adaptation elements included population and statistical information adjustments.
- Challenges in implementation involve logistic aspects and adherence to pre-specified rules.
Conclusions:
- Confirmatory adaptive enrichment trials present implementation challenges.
- Consistent treatment effect estimation is crucial for regulatory reporting.
- Design considerations for adaptive enrichment include various null hypothesis frameworks.
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