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Cluster randomized trials (CRTs) are used to study the effectiveness of complex or community-level interventions across a diverse range of contexts. To provide meaningful population-level intervention estimates, marginal models are typical used to analyze CRTs and other studies for which clustering of outcomes arise, such as individually randomized group treatment (IRGT) trials. Using four motivating studies, this dissertation addresses unanswered statistical questions in the design and analysis of CRTs and IRGT trials within the marginal modeling framework. Specifically, we develop sample size methods for CRTs with four-level structure and for longitudinal IRGT trials, develop methods for the analysis of small CRTs with time-to-event outcomes, and provide a cautionary tale about the analysis of CRTs with variable cluster sizes. The proposed methods are assessed through simulation studies and applied to real data examples.
Mentor: Liz Turner
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