Improving trial power through use of prognosis-adjusted end points.
OBJECTIVE
The stroke patient population is heterogeneous, leading to wide variation in outcome caused by differences in age, initial severity, and presence of concomitant disease. Setting an identical recovery target for all patients in intervention trials may conceal individually important therapeutic treatment effects. Instead, a variable end point that takes severity or likely prognosis into account may be more informative.


METHODS
We used data from the Glycine Antagonist in Neuroprotection (GAIN) International trial to assess statistical power of various primary end points for intervention trials. We selected prognosis-adjusted cut points based on Barthel Index (BI) or Rankin Scale (RS) using a prognostic model, or assigned a fixed end point within subgroups of patients defined by their Oxford category or National Institutes of Health Stroke Scale (NIHSS) score. We simulated a treatment effect and estimated statistical power with standard formulae.


RESULTS
Assignment of end points using a prognostic model for individual patients increased statistical power, when compared with assigning end points using only the Oxford classification. For the BI, power was increased from 60% to 88% (equivalent to a 49% reduction in sample size if power remains unchanged). With the RS end points, power was increased from 84% to 92% (or a 24% reduction in sample size). Versus a fixed end point for all patients, model-based methods increased power by 22 percentage points for BI> or =95 and 14 percentage points for RS< or =1 (effective sample size reductions 43% and 34%).


CONCLUSIONS
Prognosis-adjusted end points can increase statistical power compared with fixed end points. Assessment is based on realistic goals for individual patients and yet trial results remain generalizable.
Young
Fiona B
FB
Division of Cardiovascular and Medical Sciences, Gardiner Institute, Western Infirmary, Boyd Orr Building, University of Glasgow, Glasgow, G11 6NT, UK. Fby1w@clinmed.gla.ac.uk
Lees
Kennedy R
KR
Weir
Christopher J
CJ
D000367:Age Factors; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D015897:Comorbidity; D003198:Computer Simulation; D005998:Glycine / Q000037:antagonists & inhibitors; D006801:Humans; D015233:Models, Statistical; D018696:Neuroprotective Agents / Q000627:therapeutic use; D011379:Prognosis; D018401:Sample Size; D012720:Severity of Illness Index; D020521:Stroke / Q000188:drug therapy; D016896:Treatment Outcome
D018696:Neuroprotective Agents; D005998:Glycine