title: Generating health technology assessment evidence for rare diseases.
abstract: OBJECTIVE
  Rare diseases are often heterogeneous in their progression and response to treatment, with only a small population for study. This provides challenges for evidence generation to support HTA, so novel research methods are required.
  
  
  METHODS
  Discussion with an expert panel was augmented with references and case studies to explore robust approaches for HTA evidence generation for rare disease treatments.
  
  
  RESULTS
  Traditional RCTs can be modified using sequential, three-stage or adaptive designs to gain more power from a small patient population or to focus trial design. However, such designs need to maintain important design aspects such as randomization and blinding and be analyzed to take account of the multiple analyses performed. N-of-1 trials use within-patient randomization to test repeat periods of treatment and control until a response is clear. Such trials could be particularly valuable for rare diseases and when prospectively planned across several patients and analyzed using Bayesian techniques, a population effect can be estimated that might be of value to HTA. When the optimal outcome is unclear in a rare disease, disease specific patient reported outcomes can elucidate impacts on patients' functioning and wellbeing. Likewise, qualitative research can be used to elicit patients' perspectives, with just a small number of patients.
  
  
  CONCLUSIONS
  International consensus is needed on ways to improve evidence collection and assessment of technologies for rare diseases, which recognize the value of novel study designs and analyses in a setting where the outcomes and effects of importance are yet to be agreed.
authors.affiliation: Evidence Based Health Policy Consultant,Scotlandk.facey@btinternet.com.
authors.forename: Karen
authors.initials: K
authors.lastname: Facey
authors.affiliation: Global Medical Affairs,EVD. Genzyme a Sanofi Company and Autonomous University of Barcelona.
authors.forename: Alicia
authors.initials: A
authors.lastname: Granados
authors.affiliation: Clinical Epidemiology & Biostatistics,McMaster University.
authors.forename: Gordon
authors.initials: G
authors.lastname: Guyatt
authors.affiliation: Genetic Alliance UK.
authors.forename: Alastair
authors.initials: A
authors.lastname: Kent
authors.affiliation: Associate Professor of Health Services Research,Mayo Clinic.
authors.forename: Nilay
authors.initials: N
authors.lastname: Shah
authors.affiliation: Radboud University Medical Centre.
authors.forename: Gert Jan
authors.initials: GJ
authors.lastname: van der Wilt
authors.affiliation: Canadian Organization for Rare Disorders.
authors.forename: Durhane
authors.initials: D
authors.lastname: Wong-Rieger
mesh_terms: D006801:Humans; D016032:Randomized Controlled Trials as Topic; D035583:Rare Diseases; D012107:Research Design; D013673:Technology Assessment, Biomedical / Q000379:methods*
keywords: Technology assessment