title: Increased statistical power with combined independent randomization tests used with multiple-baseline design.
abstract: OBJECTIVE
  Physicians often assess the effectiveness of treatments on a small number of patients. Multiple-baseline designs (MBDs), based on the Wampold-Worsham (WW) method of randomization and applied to four subjects, have relatively low power. Our objective was to propose another approach with greater power that does not suffer from the time requirements of the WW method applied to a greater number of subjects.
  
  
  METHODS
  The power of a design that involves the combination of two four-subject MBDs was estimated using computer simulation and compared with the four- and eight-subject designs. The effect of a delayed linear response to treatment on the power of the test was also investigated.
  
  
  RESULTS
  Power was found to be adequate (>80%) for a standardized mean difference (SMD) greater than 0.8. The effect size associated with 80% power from combined tests was smaller than that of the single four-subject MBD (SMD=1.3) and comparable with the eight-subject MBD (SMD=0.6). A delayed linear response to the treatment resulted in important reductions in power (20-35%).
  
  
  CONCLUSIONS
  By combining two four-subject MBD tests, an investigator can detect better effect sizes (SMD=0.8) and be able to complete a comparatively timelier and feasible study.
authors: Division of Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada.
authors: Pascal N
authors: PN
authors: Tyrrell
authors: Paul N
authors: PN
authors: Corey
authors: Brian M
authors: BM
authors: Feldman
authors: Earl D
authors: ED
authors: Silverman
mesh_terms: D003627:Data Interpretation, Statistical; D005240:Feasibility Studies; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D018401:Sample Size; D013997:Time Factors