title: Improving the power of clinical trials of rheumatoid arthritis by using data on continuous scales when analysing response rates: an application of the augmented binary method.
abstract: In clinical trials of RA, it is common to assess effectiveness using end points based upon dichotomized continuous measures of disease activity, which classify patients as responders or non-responders. Although dichotomization generally loses statistical power, there are good clinical reasons to use these end points; for example, to allow for patients receiving rescue therapy to be assigned as non-responders. We adopt a statistical technique called the augmented binary method to make better use of the information provided by these continuous measures and account for how close patients were to being responders.
  
  
  
  We adapted the augmented binary method for use in RA clinical trials. We used a previously published randomized controlled trial (Oral SyK Inhibition in Rheumatoid Arthritis-1) to assess its performance in comparison to a standard method treating patients purely as responders or non-responders. The power and error rate were investigated by sampling from this study.
  
  
  
  The augmented binary method reached similar conclusions to standard analysis methods but was able to estimate the difference in response rates to a higher degree of precision. Results suggested that CI widths for ACR responder end points could be reduced by at least 15%, which could equate to reducing the sample size of a study by 29% to achieve the same statistical power. For other end points, the gain was even higher. Type I error rates were not inflated.
  
  
  
  The augmented binary method shows considerable promise for RA trials, making more efficient use of patient data whilst still reporting outcomes in terms of recognized response end points.
authors.affiliation: MRC Biostatistics Unit, Institute of Public Health, Cambridge james.wason@mrc-bsu.cam.ac.uk.
authors.forename: James M S
authors.initials: JM
authors.lastname: Wason
authors.affiliation: Global Medicines Development, AstraZeneca Pharmaceuticals, Cambridge, UK.
authors.forename: Martin
authors.initials: M
authors.lastname: Jenkins
mesh_terms: D000631:Aminopyridines; D018501:Antirheumatic Agents / Q000627:therapeutic use; D001172:Arthritis, Rheumatoid / Q000188:drug therapy*; D003625:Data Collection; D023381:Endpoint Determination; D006801:Humans; D009025:Morpholines; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data; D010078:Oxazines / Q000627:therapeutic use; D011725:Pyridines / Q000627:therapeutic use; D011743:Pyrimidines; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D016896:Treatment Outcome
chemical_list: D000631:Aminopyridines; D018501:Antirheumatic Agents; D009025:Morpholines; D010078:Oxazines; D011725:Pyridines; D011743:Pyrimidines; C523665:fostamatinib
keywords: biostatistics; methodology; randomized controlled trials; rheumatoid arthritis.