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Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control
Claire L Smith1, Zachary Thomas2, Nathan Enas2
1Eli Lilly and Company, Surrey, UK.
Pharmaceutical Statistics
|January 7, 2020
Summary
Leveraging historical data in phase 2 clinical trials using Bayesian augmented control (BAC) designs can improve drug development efficiency. These methods reduce sample size and study duration, offering significant benefits for novel therapy development.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Oncology Drug Development
Background:
- Historical data integration in clinical trials can enhance efficiency.
- Challenges include potential bias and power loss if historical and current data are inconsistent.
- Prognostic factor adjustment is crucial when utilizing historical information.
Purpose of the Study:
- To illustrate the application of Bayesian augmented control (BAC) designs in oncology phase 2 trials.
- To demonstrate how historical data can improve trial efficiency and reduce sample size.
- To explore methods for adjusting for prognostic factors and assessing data consistency.
Main Methods:
- Application of Bayesian augmented control (BAC) designs in two oncology case studies (glioblastoma and pancreatic cancer).
- Utilizing an informative prior for control arm hazard rate in one study.
- Employing a hierarchical model for data borrowing, allowing the extent of borrowing to be data-driven.
- Incorporating Bayesian analyses with adjustments for prognostic factors.
Main Results:
- Sample size savings of 15% to 20% were achieved in the glioblastoma study using a BAC design.
- The hierarchical model enabled data-driven borrowing, adapting to the consistency between observed and historical data.
- Bayesian analyses effectively adjusted for prognostic factors, enhancing the reliability of results.
Conclusions:
- Incorporating historical data through Bayesian trial design offers substantial sample size and duration savings.
- This approach facilitates a more scientific and efficient development of novel therapies by optimizing control arm recruitment.
- Sensitivity analyses are essential for robust interpretation, and patient-level data transparency aids in adjusting for prognostic imbalances.
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