Evaluating time-varying treatment effects in hybrid SMART-MRT designs

TL;DR

This paper introduces methods to evaluate time-varying treatment effects in hybrid SMART-MRT designs, combining adaptive interventions with causal inference, demonstrated through a study on reducing college binge drinking.

Contribution

It formalizes causal estimands for hybrid SMART-MRTs and develops analytical methods to assess synergistic effects on various outcomes.

Findings

Defined causal estimands for hybrid SMART-MRTs.

Developed data analytic methods for treatment effect assessment.

Applied methods to real-world binge drinking intervention data.

Abstract

Recently a new experimental approach, the hybrid experimental design (HED), was introduced to enable investigators to answer scientific questions about building behavioral interventions in which human-delivered and digital components are integrated and adapted on multiple timescales: slow (e.g., every few weeks) and fast (e.g., every few hours), respectively. An increasingly common HED involves the integration of the sequential, multiple assignment, randomized trial (SMART) with the micro-randomized trial (MRT), allowing investigators to answer scientific questions about potential synergistic effects of digital and human-delivered interventions. Approaches to formalize these questions in terms of causal estimands and associated data analytic methods are limited. In this paper, we formally define and assess these synergistic effects in hybrid SMART-MRTs on both proximal and distal outcomes. Practical utility is shown through the analysis of M-Bridge, a hybrid SMART-MRT aimed at reducing binge drinking among first-year college students.