Date of Award

January 2024

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Veronika Shabanova

Second Advisor

Lee Kennedy-Shaffer

Abstract

Stepped wedge cluster randomized trials (SWCRTs) have become increasingly popular across various disciplines, particularly in public health and medical research, as they allow evaluation of interventions rolled out sequentially across multiple clusters. However, time-to-event outcomes in SWCRTs pose significant methodological challenges. Traditional closed-form solutions for power and sample size estimation often fail to capture the complexities introduced by the implementation of staggered treatment and the discrete nature of outcome measurements. In this paper, we propose a simulation-based RShiny platform aimed at addressing these limitations by offering a more accurate and flexible approach to determining power and sample size in SWCRTs with interval-censored time-to-event outcomes. Leveraging Monte Carlo simulations, our platform accommodates both cluster-level random effects and treatment-level random effects, as well as varying interval lengths, to provide more accurate and reliable statistical power estimates in scenarios where approximation methods may not suffice. We also illustrate the practical application of this platform using the ``Sankofa 2" trial, an active multi clinic HIV intervention study in Ghana, underscoring the importance of accounting for real-world complexities in the design and analysis of SWCRTs.

Comments

This is an Open Access Thesis.

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