title: Using phase II data for the analysis of phase III studies: An application in rare diseases.
abstract: BACKGROUND
  Clinical research and drug development in orphan diseases are challenging, since large-scale randomized studies are difficult to conduct. Formally synthesizing the evidence is therefore of great value, yet this is rarely done in the drug-approval process. Phase III designs that make better use of phase II data can facilitate drug development in orphan diseases.
  
  
  METHODS
  A Bayesian meta-analytic approach is used to inform the phase III study with phase II data. It is particularly attractive, since uncertainty of between-trial heterogeneity can be dealt with probabilistically, which is critical if the number of studies is small. Furthermore, it allows quantifying and discounting the phase II data through the predictive distribution relevant for phase III. A phase III design is proposed which uses the phase II data and considers approval based on a phase III interim analysis. The design is illustrated with a non-inferiority case study from a Food and Drug Administration approval in herpetic keratitis (an orphan disease). Design operating characteristics are compared to those of a traditional design, which ignores the phase II data.
  
  
  RESULTS
  An analysis of the phase II data reveals good but insufficient evidence for non-inferiority, highlighting the need for a phase III study. For the phase III study supported by phase II data, the interim analysis is based on half of the patients. For this design, the meta-analytic interim results are conclusive and would justify approval. In contrast, based on the phase III data only, interim results are inconclusive and require further evidence.
  
  
  CONCLUSIONS
  To accelerate drug development for orphan diseases, innovative study designs and appropriate methodology are needed. Taking advantage of randomized phase II data when analyzing phase III studies looks promising because the evidence from phase II supports informed decision-making. The implementation of the Bayesian design is straightforward with public software such as R.
authors.affiliation: 1 Novartis Pharma AG, Basel, Switzerland.
authors.forename: Simon
authors.initials: S
authors.lastname: Wandel
authors.affiliation: 1 Novartis Pharma AG, Basel, Switzerland.
authors.forename: Beat
authors.initials: B
authors.lastname: Neuenschwander
authors.affiliation: 2 Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Röver
authors.affiliation: 2 Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Friede
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic; D017326:Clinical Trials, Phase III as Topic; D003625:Data Collection / Q000379:methods*; D017277:Drug Approval / Q000458:organization & administration*; D006801:Humans; D016849:Keratitis, Herpetic / Q000188:drug therapy; D035583:Rare Diseases / Q000188:drug therapy*; D012107:Research Design
keywords: Bayesian statistics; Drug development in rare diseases; meta-analysis; phase III studies