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Enrichment design with patient population augmentation.
Bo Yang1, Yijie Zhou1, Lanju Zhang1
1Data and Statistical Science, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, United States.
Contemporary Clinical Trials
|March 10, 2015
Summary
This study introduces a novel clinical trial design that enriches for biomarker-positive patients while also enrolling biomarker-negative patients to assess overall treatment effects. This approach enhances trial success probability and efficiently estimates treatment benefits across diverse populations.
Area of Science:
- Clinical trial design
- Biostatistics
- Pharmacology
Background:
- Clinical trial enrichment strategies aim to improve success rates by focusing on responsive subpopulations.
- Existing enrichment designs face challenges in classifying subpopulations and estimating treatment effects in non-targeted groups.
- The U.S. Food and Drug Administration (FDA) guidance highlights uncertainties in enrichment design.
Purpose of the Study:
- To propose a novel clinical trial design strategy that augments biomarker-positive subpopulations with biomarker-negative patients.
- To develop a weighted statistic for unbiased estimation of overall treatment effects in enriched trials.
- To ensure high probability of trial success in biomarker-positive subpopulations while efficiently assessing overall treatment benefit.
Main Methods:
- A weighted statistic is derived using screening information to correct for subpopulation disproportionality.
- Enrollment of biomarker-negative patients occurs after sufficient power is achieved in the biomarker-positive subpopulation.
- A two-stage testing procedure is employed: first on biomarker-positive, then on the overall population using the weighted statistic.
Main Results:
- The proposed weighted statistic provides an unbiased estimate of the overall treatment effect, comparable to all-comer trials.
- The design maintains a primary focus on the biomarker-positive subpopulation for initial testing.
- This approach balances the need for high success probability in the enriched group with efficient assessment of broader treatment efficacy.
Conclusions:
- The novel design offers a high probability of success in biomarker-positive subpopulations.
- It efficiently assesses overall treatment effects, even with uncertain benefits in biomarker-negative patients.
- This strategy differs from traditional enrichment, stratified, and adaptive designs by prioritizing biomarker-positive efficacy while enabling overall treatment effect evaluation.
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