Computational approaches for addressing complexity in biomedicine The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.autBrown, Justin ReedthsDinu, ValentindgcJohnson, WilliamdgcPetitti, DianapblArizona State UniversityengPartial requirement for: Ph.D., Arizona State University, 2012Includes bibliographical references (p. 163-169)Field of study: Biomedial informaticsby Justin Reed Brownhttps://hdl.handle.net/2286/R.I.1492000Doctoral DissertationAcademic thesesvii, 169 p113458507431630349197150897adminIn CopyrightAll Rights Reserved2012TextStatisticsbiostatisticsBioinformaticsBioinformatics--Mathematics.Bioinformatics