title: Bayesian meta-analysis on medical devices: application to implantable cardioverter defibrillators.
abstract: OBJECTIVE
  The aim of this study is to describe and illustrate a method to obtain early estimates of the effectiveness of a new version of a medical device.
  
  
  METHODS
  In the absence of empirical data, expert opinion may be elicited on the expected difference between the conventional and modified devices. Bayesian Mixed Treatment Comparison (MTC) meta-analysis can then be used to combine this expert opinion with existing trial data on earlier versions of the device. We illustrate this approach for a new four-pole implantable cardioverter defibrillator (ICD) compared with conventional ICDs, Class III anti-arrhythmic drugs, and conventional drug therapy for the prevention of sudden cardiac death in high risk patients. Existing RCTs were identified from a published systematic review, and we elicited opinion on the difference between four-pole and conventional ICDs from experts recruited at a cardiology conference.
  
  
  RESULTS
  Twelve randomized controlled trials were identified. Seven experts provided valid probability distributions for the new ICDs compared with current devices. The MTC model resulted in estimated relative risks of mortality of 0.74 (0.60-0.89) (predictive relative risk [RR] = 0.77 [0.41-1.26]) and 0.83 (0.70-0.97) (predictive RR = 0.84 [0.55-1.22]) with the new ICD therapy compared to Class III anti-arrhythmic drug therapy and conventional drug therapy, respectively. These results showed negligible differences from the preliminary results for the existing ICDs.
  
  
  CONCLUSIONS
  The proposed method incorporating expert opinion to adjust for a modification made to an existing device may play a useful role in assisting decision makers to make early informed judgments on the effectiveness of frequently modified healthcare technologies.
authors.affiliation: Health Economics Research Group, Brunel University, Uxbridge, United Kingdom. cmp11jy@sheffield.ac.uk
authors.forename: Ji-Hee
authors.initials: JH
authors.lastname: Youn
authors.forename: Joanne
authors.initials: J
authors.lastname: Lord
authors.forename: Karla
authors.initials: K
authors.lastname: Hemming
authors.forename: Alan
authors.initials: A
authors.lastname: Girling
authors.forename: Martin
authors.initials: M
authors.lastname: Buxton
mesh_terms: D006801:Humans; D000889:Anti-Arrhythmia Agents / Q000191:economics / Q000627:therapeutic use; D001145:Arrhythmias, Cardiac / Q000401:mortality / Q000628:therapy; D001499:Bayes Theorem; D016757:Death, Sudden, Cardiac / Q000453:epidemiology; D003657:Decision Making; D017147:Defibrillators, Implantable / Q000191:economics / Q000706:statistics & numerical data; D004864:Equipment and Supplies / Q000191:economics / Q000706:statistics & numerical data; D015233:Models, Statistical; D011336:Probability; D012306:Risk; D018570:Risk Assessment
chemical_list: D000889:Anti-Arrhythmia Agents