title: Should stroke trials adjust functional outcome for baseline prognostic factors?
abstract: OBJECTIVE
  Many stroke trials have provided neutral results. Suboptimal statistical analyses may be failing to detect effective interventions. Adjusting outcomes for baseline prognostic factors in the analysis may improve the efficiency of analysis of outcomes.
  
  
  METHODS
  Data from 23 stroke trials (25 674 patients) assessing functional outcome were included. The prognostic variables considered were age, sex, and baseline severity. Unadjusted and adjusted ordinal logistic regression models were compared using simulated data from each trial (10 000 simulations per trial). Three levels of treatment effect were assessed with ORs of 0.95, 0.74, and 0.57. The reduction in sample size gained from using the adjusted models, as compared with an unadjusted model, was then calculated as a reflection of the increase in statistical power.
  
  
  RESULTS
  Adjusting outcome for baseline factors led to a reduction in sample size, which was similar across all 3 treatment effects (median percentage reduction, interquartile range): OR=0.95: 35.3% (21.0 to 42.1); OR=0.74: 38.4% (29.4 to 42.7); and OR=0.57: 38.4% (27.4 to 42.2). As the treatment effect increased, the proportion of simulations in which the treatment effect for the adjusted model was greater than for the unadjusted model also increased.
  
  
  CONCLUSIONS
  Adjusting for prognostic factors in stroke trials can reduce sample size by at least 20% to 30% (the lower interquartile range) for a given power. Conversely, trialists may want to power for an unadjusted analysis and then increase statistical power by adjusting for prognostic factors.
authors.affiliation: Stroke Trials Unit, University of Nottingham, UK.
authors.forename: Laura J
authors.initials: LJ
authors.lastname: Gray
authors.forename: Philip M W
authors.initials: PM
authors.lastname: Bath
authors.forename: Timothy
authors.initials: T
authors.lastname: Collier
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D018401:Sample Size; D012737:Sex Factors; D020521:Stroke / Q000453:epidemiology*; D016896:Treatment Outcome