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Adaptive Bayesian design for phase I dose-finding trials using a joint model of response and toxicity
1Department of Biostatistics, University of Pittsburgh, Pittsburgh 15261, Pennsylvania, USA. sumswang@yahoo.com
Journal of Biopharmaceutical Statistics
|January 16, 2010
Summary
This study introduces an adaptive Bayesian method for phase I clinical trials, using both patient response and toxicity to guide dose escalation. This approach enhances decision-making for targeted therapies by incorporating biological and clinical responses.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Pharmacology
Background:
- Phase I cancer trials traditionally face challenges with rare clinical responses.
- Molecularly targeted therapies may increase the frequency of clinical and biological responses.
- Existing dose-finding methods may not fully leverage response data.
Purpose of the Study:
- To develop a novel adaptive Bayesian dose-finding method for phase I clinical trials.
- To integrate both patient response and toxicity data for more informed dose escalation.
- To enhance the precision and efficiency of dose selection in early-phase oncology studies.
Main Methods:
- An adaptive Bayesian framework was developed, modeling response and toxicity thresholds.
- The model assumes a bivariate log-normal distribution or mixture for individual thresholds.
- Dose assignment maximizes patient-oriented expected utility, utilizing prior and accumulated data.
Main Results:
- Simulations using parameters from early Gleevec trials demonstrated the proposed design's value.
- The method effectively utilizes both response and toxicity information for dose escalation.
- Evidence suggests improved decision-making compared to traditional approaches.
Conclusions:
- The proposed adaptive Bayesian design offers a valuable tool for phase I clinical trials.
- Integrating response and toxicity data enhances dose-finding for targeted therapies.
- This method supports more aggressive and informative dose escalation strategies.
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