title: Prospective alpha allocation in the Clarification of Optimal Anticoagulation through Genetics (COAG) trial.
abstract: BACKGROUND
  The Clarification of Optimal Anticoagulation through Genetics (COAG) trial is a large, multicenter, double-blinded, randomized trial to determine whether use of a genotype-guided dosing algorithm (using clinical and genetic information) to initiate warfarin treatment will improve anticoagulation status when compared to a dosing algorithm using only clinical information.
  
  
  OBJECTIVE
  This article describes prospective alpha allocation and balanced alpha allocation for the design of the COAG trial.
  
  
  METHODS
  The trial involves two possibly heterogeneous populations, which can be distinguished by the difference in warfarin dose as predicted by the two algorithms. A statistical approach is detailed, which allows an overall comparison as well as a comparison of the primary endpoint in the subgroup for which sufficiently different doses are predicted by the two algorithms. Methods of allocating alpha for these analyses are given - a prospective alpha allocation and allocating alpha so that the two analyses have equal power, which we call a 'balanced alpha allocation.'
  
  
  RESULTS
  We show how to include an analysis of the primary endpoint in a subgroup as a co-primary analysis. Power can be improved by incorporating the correlation between the overall and subgroup analyses in a prospective alpha allocation approach. Balanced alpha allocation for the full cohort and subgroup tests to achieve the same desired power for both of the primary analyses is discussed in detail.
  
  
  CONCLUSIONS
  In the COAG trial, it is impractical to stratify the randomization on subgroup membership because genetic information may not be available at the time of randomization. If imbalances in the treatment arms in the subgroup are found, they will need to be addressed.
  
  
  CONCLUSIONS
  The design of the COAG trial assures that the subgroup in which the largest treatment difference is expected is elevated to a co-primary analysis. Incorporating the correlation between the full cohort and the subgroup analyses provides an improvement in power for the subgroup comparison, and further improvement may be achieved via a balanced alpha allocation approach when the parameters involved in the sample size calculation are reasonably well estimated.
authors.affiliation: Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA. jooj@nhlbi.nih.gov
authors.forename: Jungnam
authors.initials: J
authors.lastname: Joo
authors.forename: Nancy L
authors.initials: NL
authors.lastname: Geller
authors.forename: Benjamin
authors.initials: B
authors.lastname: French
authors.forename: Stephen E
authors.initials: SE
authors.lastname: Kimmel
authors.forename: Yves
authors.initials: Y
authors.lastname: Rosenberg
authors.forename: Jonas H
authors.initials: JH
authors.lastname: Ellenberg
mesh_terms: D000465:Algorithms; D000925:Anticoagulants / Q000008:administration & dosage; D054796:Drug Dosage Calculations; D005838:Genotype; D006801:Humans; D018579:Patient Selection; D010597:Pharmacogenetics; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D013223:Statistics as Topic / Q000379:methods*; D014859:Warfarin / Q000008:administration & dosage
chemical_list: D000925:Anticoagulants; D014859:Warfarin