title: Bayesian hierarchical modeling of patient subpopulations: efficient designs of Phase II oncology clinical trials.
abstract: BACKGROUND
  In oncology, the treatment paradigm is shifting toward personalized medicine, where the goal is to match patients to the treatments most likely to deliver benefit. Treatment effects in various subpopulations may provide some information about treatment effects in other subpopulations.
  
  
  OBJECTIVE
  We compare different approaches to Phase II trial design where a new treatment is being investigated in several groups of patients. We compare considering each group in an independent trial to a single trial with hierarchical modeling of the patient groups.
  
  
  METHODS
  We assume four patient groups with different background response rates and simulate operating characteristics of three trial designs, Simon's Optimal Two-Stage design, a Bayesian adaptive design with frequent interim analyses, and a Bayesian adaptive design with frequent interim analyses and hierarchical modeling across patient groups.
  
  
  RESULTS
  Simon's designs are based on 10% Type I and Type II error rates. The independent Bayesian designs are tuned to have similar error rates, but may have a slightly smaller mean sample size due to more frequent interim analyses. Under the null, the mean sample size is 2-4 patients smaller. A hierarchical model across patient groups can provide additional power and a further reduction in mean sample size. Under the null, the addition of the hierarchical model decreases the mean sample size an additional 4-7 patients in each group. Under the alternative hypothesis, power is increased to at least 98% in all groups.
  
  
  CONCLUSIONS
  Hierarchical borrowing can make finding a single group in which the treatment is promising, if there is only one, more difficult. In a scenario where the treatment is uninteresting in all but one group, power for that one group is reduced to 65%. When the drug appears promising in some groups and not in others, there is potential for borrowing to inflate the Type I error rate.
  
  
  CONCLUSIONS
  The Bayesian hierarchical design is more likely to correctly conclude efficacy or futility than the other two designs in many scenarios. The Bayesian hierarchical design is a strong design for addressing possibly differential effects in different groups.
authors: aBerry Consultants, LLC, Austin, TX, USA.
authors: Scott M
authors: SM
authors: Berry
authors: Kristine R
authors: KR
authors: Broglio
authors: Susan
authors: S
authors: Groshen
authors: Donald A
authors: DA
authors: Berry
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008495:Medical Oncology; D012372:ROC Curve; D018401:Sample Size
references: Clin Trials. 2012 Feb;9(1):37-47
references: 21878446
references: Stat Med. 2002 Oct 15;21(19):2909-16
references: 12325107
references: N Engl J Med. 2010 Jul 22;363(4):301-4
references: 20551152
references: Control Clin Trials. 1989 Mar;10(1):1-10
references: 2702835
references: Stat Med. 1995 Oct 30;14(20):2173-90
references: 8552895
references: Biometrics. 2003 Jun;59(2):221-8
references: 12926706
references: Clin Trials. 2010 Oct;7(5):516-24
references: 20338899
references: Stat Med. 1990 Aug;9(8):919-29
references: 2218194
references: Biometrics. 2004 Jun;60(2):418-26
references: 15180667
references: Med Care. 2010 Jun;48(6 Suppl):S137-44
references: 20473185
references: Clin Trials. 2011 Apr;8(2):129-43
references: 21282293
references: Biometrics. 1991 Sep;47(3):871-81
references: 1742443
references: Stat Med. 2003 Mar 15;22(5):763-80
references: 12587104
references: J Clin Oncol. 2009 Jul 1;27(19):3148-53
references: 19451433
references: Control Clin Trials. 1990 Feb;11(1):37-42
references: 2157579
references: Nat Rev Drug Discov. 2006 Jan;5(1):27-36
references: 16485344