title: Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding.
abstract: 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.
  
  
  OBJECTIVE
  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.
  
  
  CONCLUSIONS
  The method requires a substantial number of elicited values and design parameters, and computer simulations are required to obtain an acceptable design.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA rex@mdanderson.org.
authors.forename: Peter F
authors.initials: PF
authors.lastname: Thall
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Richard C
authors.initials: RC
authors.lastname: Herrick
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Hoang Q
authors.initials: HQ
authors.lastname: Nguyen
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: John J
authors.initials: JJ
authors.lastname: Venier
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: J Clift
authors.initials: JC
authors.lastname: Norris
mesh_terms: D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000379:methods*; D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D004305:Dose-Response Relationship, Drug; D054796:Drug Dosage Calculations; D006801:Humans; D015233:Models, Statistical; D018401:Sample Size
keywords: Adaptive design; Bayesian design; clinical trial; dose-finding; phase I/II trial