title: Calculation of numbers-needed-to-treat in parallel group trials assessing ordinal outcomes: case examples from acute stroke and stroke prevention.
abstract: BACKGROUND
  Number-needed-to-treat describes the magnitude of the effect of an intervention, underpins health economic analyses, and is typically calculated for binary events. Ordered categorical outcomes provide more clinical information and their analysis using ordinal approaches is usually more efficient statistically. However, to date, techniques to calculate number-needed-to-treat based on ordinal outcomes for parallel group trials have had important limitations. Aims Numbers-needed-to-treat may be calculated for ordinal data from parallel group trials by using an unmatched comparison of all subjects or by generating matched pairs of patients nested within the study.
  
  
  METHODS
  The above approaches were assessed and compared with numbers-needed-to-treat calculated for binary outcomes using individual patient data from acute and prevention stroke trials testing the effect of interventions of varying utility and efficacy.
  
  
  RESULTS
  Numbers-needed-to-treat were generally lower numerically for ordinal vs. binary, and matched vs. unmatched analyses, and the lowest in highly efficacious interventions: hemicraniectomy, ordinal matched 2.4 vs. ordinal unmatched 2.5 vs. binary matched 12 vs. binary unmatched 9 (one trial, 12 month outcome); alteplase, 4.5 vs. 6.6 vs. 8.4 vs. 8.4 (one trial with two parts, three-months); aspirin, 42 vs. 58 vs. 76 vs. 80 (one trial, six-months); and stroke units, 3.6-5.3 vs. 6.2 vs. 4.7-5.9 vs. 6.3-7.0 (two trials, three- to 60 months). Similar trends were seen for aspirin/dipyridamole vs. aspirin in secondary prevention, 22 vs. 20 vs. 31 vs. 31 (one trial, 24 months).
  
  
  CONCLUSIONS
  Number-needed-to-treat may be calculated for ordinal outcome data derived from parallel group stroke trials; such numbers-needed-to-treat are lower than those calculated for binary outcomes. Their use complements the use of ordinal statistical approaches in the analysis of ordered categorical data.
authors.affiliation: Division of Stroke Medicine, University of Nottingham, Hospital campus, Nottingham NG5 1PB, UK. philip.bath@nottingham.ac.uk
authors.forename: Philip
authors.initials: P
authors.lastname: Bath
authors.forename: Cheryl
authors.initials: C
authors.lastname: Hogg
authors.forename: Michael
authors.initials: M
authors.lastname: Tracy
authors.forename: Stuart
authors.initials: S
authors.lastname: Pocock
mesh_terms: D000368:Aged; D000465:Algorithms; D001241:Aspirin / Q000627:therapeutic use; D000068342:Aspirin, Dipyridamole Drug Combination; D002986:Clinical Trials as Topic; D016001:Confidence Intervals; D004176:Dipyridamole / Q000627:therapeutic use; D004338:Drug Combinations; D005260:Female; D005343:Fibrinolytic Agents / Q000627:therapeutic use; D006801:Humans; D008297:Male; D010975:Platelet Aggregation Inhibitors / Q000627:therapeutic use; D012107:Research Design; D018401:Sample Size; D020521:Stroke / Q000188:drug therapy* / Q000453:epidemiology / Q000517:prevention & control*; D010959:Tissue Plasminogen Activator / Q000627:therapeutic use; D016896:Treatment Outcome
chemical_list: D000068342:Aspirin, Dipyridamole Drug Combination; D004338:Drug Combinations; D005343:Fibrinolytic Agents; D010975:Platelet Aggregation Inhibitors; D004176:Dipyridamole; D010959:Tissue Plasminogen Activator; D001241:Aspirin