Effective sample size for computing prior hyperparameters in Bayesian phase I–II dose-finding

• Published in: Clinical trials (London, England) Vol. 11; no. 6; pp. 657 - 666
• Main Authors: Thall, Peter F, Herrick, Richard C, Nguyen, Hoang Q, Venier, John J, Norris, J Clift
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.12.2014; Sage Publications Ltd
• Subjects: Bayes Theorem; Bayesian analysis; Clinical trials; Clinical Trials, Phase I as Topic - methods; Clinical Trials, Phase II as Topic - methods; Dose-Response Relationship, Drug; Drug Dosage Calculations; Drug therapy; Humans; Models, Statistical; Parameter estimation; Sample Size; clinical trial; Adaptive design; Bayesian design; phase I/II trial; dose-finding
• ISSN: 1740-7745; 1740-7753
• DOI: 10.1177/1740774514547397

Background: The efficacy–toxicity trade-off based design is a practical Bayesian phase I–II dose-finding methodology. Because the design’s performance is very sensitive to prior hyperparameters and the shape of the target trade-off contour, specifying these two design elements properly is essential. Purpose: The goals are to provide a method that uses elicited mean outcome probabilities to derive a prior that is neither overly informative nor overly disperse, and practical guidelines for specifying the target trade-off contour. Methods: A general algorithm is presented that determines prior hyperparameters using least squares penalized by effective sample size. Guidelines for specifying the trade-off contour are provided. These methods are illustrated by a clinical trial in advanced prostate cancer. A new version of the efficacy–toxicity program is provided for implementation. Results: Together, the algorithm and guidelines provide substantive improvements in the design’s operating characteristics. Limitations: The method requires a substantial number of elicited values and design parameters, and computer simulations are required to obtain an acceptable design. Conclusion: The two key improvements greatly enhance the efficacy–toxicity design’s practical usefulness and are straightforward to implement using the updated computer program. The algorithm for determining prior hyperparameters to ensure a specified level of informativeness is general, and may be applied to models other than that underlying the efficacy–toxicity method.