Bayesian Hierarchical Modeling based on Multi-Source Exchangeability
2017-07
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Bayesian Hierarchical Modeling based on Multi-Source Exchangeability
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2017-07
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Progress in medical practice traditionally takes place over a sequence of clinical studies which are designed to establish clinical efficacy, identify the safety profile, and seek regulatory approval for a novel treatment strategy. These trials in humans can be expensive and present numerous challenges in their implementation. While some challenges may be addressed by the development of innovative trial designs, it may also be advantageous to incorporate supplemental sources of information, which are typically ignored in traditional approaches to analysis. In this dissertation, we introduce Multi-Source Exchangeability Models (MEMs), a general Bayesian hierarchical approach that integrates supplemental data arising from multiple, possibly non-exchangeable, sources into the analysis of a primary source. We first describe the proposed framework and prove some desirable asymptotic properties that show the consistency of posterior estimation. Simulation results illustrate that MEMs incorporate more supplemental information in the presence of homogeneous supplemental sources and exhibit reduced bias in the presence of heterogeneous supplemental sources relative to competing Bayesian hierarchical modeling strategies. Next, we illustrate how MEMs can be used to design a more efficient sequential platform design for Ebola virus disease by sharing information across trial segments. When compared to the standard platform design, we demonstrate that MEMs with adaptive randomization improved power by as much as 51% with limited type-I error inflation. We conclude by extending our work with model averaging to the estimation of multiple mixture distributions in the presence of a hypothesized biological relationship between groups to identify non-compliance in a regulatory tobacco clinical trial. The results of this dissertation illustrate that MEMs yield favorable characteristics across a variety of scenarios and motivates further research to extend the MEM framework to other settings, as well.
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University of Minnesota Ph.D. dissertation. July 2017. Major: Biostatistics. Advisor: Joseph Koopmeiners. 1 computer file (PDF); xiii, 135 pages.
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Kaizer, Alexander. (2017). Bayesian Hierarchical Modeling based on Multi-Source Exchangeability. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/190550.
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