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Clin Trials. 2010 Oct;7(5):597-604. doi: 10.1177/1740774510381285. Epub 2010 Aug 6.

Prospective alpha allocation in the Clarification of Optimal Anticoagulation through Genetics (COAG) trial.

Author information

1
Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA. jooj@nhlbi.nih.gov

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.

PURPOSE:

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.

LIMITATIONS:

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.

PMID:
20693186
PMCID:
PMC3111931
DOI:
10.1177/1740774510381285
[Indexed for MEDLINE]
Free PMC Article

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