title: A Bayesian approach for unplanned sample sizes in phase II cancer clinical trials.
abstract: BACKGROUND
  Phase II cancer clinical trials commonly employ two-stage designs that incorporate a single interim analysis for lack of efficacy and are designed to achieve specified frequentist properties. The requirement to examine the outcome at a prespecified sample size (SS) can be problematic, because the attained SS often differs from the planned SS.
  
  
  OBJECTIVE
  We propose to address unplanned SSs achieved at either stage by means of a Bayesian approach that approximately preserves the original design's properties.
  
  
  METHODS
  Our approach translates the rejection rule of the original frequentist design into equivalent statements about the posterior distribution of the response rate and applies this Bayesian criterion to the analysis with any realized SS.
  
  
  RESULTS
  The results demonstrate that our approach approximately maintains operating characteristics of the original frequentist design including type I and type II error rates, probability of early termination, and expected SS under the null hypothesis.
  
  
  CONCLUSIONS
  Designs attained under this approach may not satisfy target limits for type I error rate and power.
  
  
  CONCLUSIONS
  Our method offers a coherent analysis plan when the attained SS at either stage deviates from that specified in the original design. The price of its flexibility, in terms of erosion of the desired frequentist properties, is modest.
authors.affiliation: Division of Oncology, The Children's Hospital of Philadelphia, PA 19104, USA. liy3@email.chop.edu
authors.forename: Yimei
authors.initials: Y
authors.lastname: Li
authors.forename: Rosemarie
authors.initials: R
authors.lastname: Mick
authors.forename: Daniel F
authors.initials: DF
authors.lastname: Heitjan
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000628:therapy*; D012107:Research Design; D018401:Sample Size