Micro-Randomized Trial with Flexible Design and Sample Size Considerations

Abstract

Technological advancements have made it possible to deliver mobile health interventions to individuals. A novel framework that has emerged from such advancements is the just-in-time adaptive intervention (JITAI), which aims to suggest the right support to the individuals when their needs arise. The micro-randomized trial (MRT) design has been proposed recently to test the proximal effects of these JITAIs. In an MRT, participants are repeatedly randomized to one of the intervention categories of various components, at a scale of hundreds or thousands of decision-points over the study. However, the extant MRT framework only considers the components with a fixed number of categories. We propose a novel extension of the MRT design, by allowing flexible addition of more categories to the components during the study, in a spirit analogous to adaptive platform trials. Accordingly, we develop a methodology to allow for simultaneous comparisons of varying numbers of categories over a given study period. The proposed methodology is motivated by collaboration on the DIAMANTE study, which learns to adaptively deliver multiple complex kinds of text messages to encourage physical activity among the patients with Diabetes and Depression. We apply a methodology similar to the generalized estimating equation approach on the longitudinal data arising from the proposed MRT, to develop novel test statistics for assessing the proximal effects and deriving the associated sample size calculators. We conduct simulation studies to evaluate the sample size calculators, based on both power and precision. We have developed an R shiny application of the sample size calculators.