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Minimizing control group allocation in randomized trials using dynamic borrowing of external control data - An
Louis Dron1,2, Shirin Golchi3,2, Grace Hsu3,2
1Department of Health Research Methodology, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Background:
Enrollment of participants to control arms in clinical trials can be challenging. This is particularly an issue in oncology trials where the standard-of-care is shifting rapidly and several promising experimental treatments are undergoing phase III testing. Novel methods for utilizing external control data may mitigate these challenges, but applied examples are sparse. Here, we therefore illustrate how Bayesian dynamic borrowing of external individual patient level control data from similar clinical trials can often reduce randomization to the control intervention without substantially trading-off precision. We further explore which types of scenarios yield viable trade-offs, and which do not.
Patients And Methods:
We obtained individual patient data on patients being treated with second-line therapy for non-small cell lung cancer from Project Data Sphere with minimal in/exclusion criteria restrictions, and applied Bayesian hierarchical models with uninformative priors to generate illustrative synthetic control groups.
Results:
Four phase III clinical trials were identified and utilized in our analysis. Even when studies which are knowingly incongruent with one another are selected to generate a synthetic control, the nature of this methodology minimizes improper borrowing from historical data. The use of a small concurrent control group within a trial greatly reduces penalized selection, and our results demonstrate the ability to reduce allocation to the control group by up to 80% with a minimal increase in uncertainty when closely matched historical data is available.
Conclusion:
Dynamic borrowing using Bayesian hierarchical models with uninformative priors represents a novel approach to utilizing external controls for comparative estimates using single arm evidence.
Insights
Bayesian dynamic borrowing can reduce control arm randomization in clinical trials by up to 80% using external patient data. This method offers a novel way to leverage historical data for comparative estimates, especially in oncology.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Oncology Research
Background:
- Enrollment in clinical trial control arms is challenging, particularly in oncology due to rapidly evolving standards of care.
- External control data utilization methods are needed but applied examples are scarce.
Purpose of the Study:
- To illustrate Bayesian dynamic borrowing of external individual patient-level control data.
- To explore scenarios for viable trade-offs in using external data.
Main Methods:
- Utilized individual patient data from Project Data Sphere for second-line non-small cell lung cancer therapy.
- Applied Bayesian hierarchical models with uninformative priors to generate synthetic control groups.
Main Results:
- Analyzed four phase III clinical trials.
- Demonstrated reduction in control group allocation by up to 80% with minimal uncertainty increase.
- Showcased minimized improper borrowing even with incongruent studies.
Conclusions:
- Bayesian dynamic borrowing offers a novel approach for utilizing external controls.
- This method facilitates comparative estimates using single-arm evidence.
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