Randomization procedures for multicomponent behavioral intervention factorial trials in the multiphase optimization strategy framework: challenges and recommendations.

The multiphase optimization strategy (MOST) is an increasingly popular framework to prepare, optimize, and evaluate multicomponent behavioral health interventions. Within this framework, it is common to use a factorial trial to assemble an optimized multicomponent intervention by simultaneously testing several intervention components. With the possibility of a large number of conditions (unique combinations of components) and a goal to balance conditions on both sample size (for statistical efficiency) and baseline covariates (for internal validity), such trials face additional randomization challenges compared to the standard two-arm trial. The purpose of the current paper is to compare and contrast potential randomization methods for factorial trials in the context of MOST and to provide guidance for the reporting of those methods. We describe the principles, advantages, and disadvantages of several randomization methods in the context of factorial trials. We then provide examples to examine current practice in the MOST-related literature and provide recommendations for reporting of randomization. We identify two key randomization decisions for MOST-related factorial trials: (i) whether to randomize to components or conditions and (ii) whether to use restricted randomization techniques, such as stratification, permuted blocks, and minimization. We also provide a checklist to assist researchers in ensuring complete reporting of randomization methods used. As more investigators use factorial trials within the MOST framework for assembling optimized multicomponent behavioral interventions, appropriate implementation and rigorous reporting of randomization procedures will be essential for ensuring the efficiency and validity of the results. © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.