Dynamic treatment regimes, also called adaptive interventions, guide sequential treatment decision-making in a variety of fields, including healthcare and education. Dynamic treatment regimes accommodate differences between individuals and changes in individuals over time. Sequential randomized trials are a specific type of trial design useful for developing high-quality dynamic treatment regimes. Sequential randomized trials utilize re-randomization of individuals over time in order to discover how to sequence, time, and personalize treatments. Two of the most commonly used sequential randomized trial designs are sequential multiple assignment randomized trials and micro-randomized trials.

In this thesis, we contribute to both the design and analysis of sequential randomized trials. We describe design considerations for sequential randomized trials in online education. We present the design and analysis for a sequential randomized trial developed to reduce dropout in a massively open online course. We also develop statistical methodology and sample size formulae for sequential multiple assignment randomized trial designs which include cluster-level randomization. The techniques are inspired by a trial aiming to develop high-quality dynamic treatment regimes for mental health clinics. Lastly, we illustrate the design, describe the analysis, and present results of a large micro-randomized trial aiming to develop mobile health interventions for improving medical interns' mental health.