title: The use of continuous data versus binary data in MTC models: a case study in rheumatoid arthritis.
abstract: BACKGROUND
  Estimates of relative efficacy between alternative treatments are crucial for decision making in health care. When sufficient head to head evidence is not available Bayesian mixed treatment comparison models provide a powerful methodology to obtain such estimates. While models can be fit to a broad range of efficacy measures, this paper illustrates the advantages of using continuous outcome measures compared to binary outcome measures.
  
  
  METHODS
  Using a case study in rheumatoid arthritis a Bayesian mixed treatment comparison model is fit to estimate the relative efficacy of five anti-TNF agents currently licensed in Europe. The model is fit for the continuous HAQ improvement outcome measure and a binary version thereof as well as for the binary ACR response measure and the underlying continuous effect. Results are compared regarding their power to detect differences between treatments.
  
  
  RESULTS
  Sixteen randomized controlled trials were included for the analysis. For both analyses, based on the HAQ improvement as well as based on the ACR response, differences between treatments detected by the binary outcome measures are subsets of the differences detected by the underlying continuous effects.
  
  
  CONCLUSIONS
  The information lost when transforming continuous data into a binary response measure translates into a loss of power to detect differences between treatments in mixed treatment comparison models. Binary outcome measures are therefore less sensitive to change than continuous measures. Furthermore the choice of cut-off point to construct the binary measure also impacts the relative efficacy estimates.
authors.affiliation: Department of Statistics, Trinity College Dublin, Dublin, Ireland. schmitzs@tcd.ie
authors.forename: Susanne
authors.initials: S
authors.lastname: Schmitz
authors.forename: Roisin
authors.initials: R
authors.lastname: Adams
authors.forename: Cathal
authors.initials: C
authors.lastname: Walsh
mesh_terms: D018501:Antirheumatic Agents / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000188:drug therapy* / Q000503:physiopathology; D001499:Bayes Theorem; D003131:Combined Modality Therapy; D003657:Decision Making; D005060:Europe; D019317:Evidence-Based Medicine; D006801:Humans; D008727:Methotrexate; D015233:Models, Statistical; D019982:Organizational Case Studies; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010919:Placebos; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012720:Severity of Illness Index; D012955:Societies, Medical; D011795:Surveys and Questionnaires
chemical_list: D018501:Antirheumatic Agents; D010919:Placebos; D008727:Methotrexate