Comparison of stratification and adaptive methods for treatment allocation in an acute stroke clinical trial

Abstract

Achieving balance on prognostic factors between treatment groups in a clinical trial is important to ensure that any observed treatment effect may be attributed to the treatment itself. Improving the balance on prognostic factors also potentially increases the statistical power attained in a trial. Substantial imbalances may occur by chance if simple randomization is used. Allocation of the treatment according to stratified random blocks based on clinical features is the conventional approach to obtain treatment groups that are as similar as possible. An alternative approach, known as minimization (or more generally as adaptive stratification), has also been proposed. We assessed the feasibility of adaptive stratification in the context of a clinical trial of insulin to control plasma glucose level following acute stroke. We determined suitable settings for the parameters in the adaptive stratification procedure by simulation studies. Specifically, we assessed: the optimal probability for allocating a patient to the preferred (leading to least imbalance on prognostic factors) treatment group; the number of variables that could be incorporated in the adaptive stratification algorithm; the weighting that should be given to each variable; and whether interactions between variables should be included. We then compared the statistical power, across a range of simulated treatment effects, between trials where treatments were allocated by stratified random blocks and by adaptive stratification. Finally, we considered the importance of the method of analysis in realizing the gain in power which may potentially be achieved by allocating treatments using stratified random blocks or adaptive stratification.