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Title page for ETD etd-07172018-120720

Type of Document Master's Thesis
Author Neal, Jacquelyn Elizabeth
URN etd-07172018-120720
Title Statistical Methods for Modeling Disease Progression
Degree Master of Science
Department Biostatistics
Advisory Committee
Advisor Name Title
Dandan Liu Committee Chair
Qingxia Chen Committee Member
  • transition rates
  • Disease progression
  • multi-state modeling
  • longitudinal modeling
  • partly conditional models
Date of Defense 2018-06-28
Availability restricted
There are multiple existing methods to modeling disease progression, and the relationship between risk factors and progression is an important consideration when choosing which method to use. This thesis is motivated by the pathology of Alzheimer’s disease (AD), and modeling methods are examined in the context of AD using data from the National Alzheimer’s Coordinating Center (NACC). Models reliant on the Markov assumption are a common method used when modeling progressive disease with multiple states. However, the Markov assumption may not be a realistic assumption for the disease pathology. Other

methods exist that do not rely on the Markov assumption, but their implementation is rare in AD literature. Existing methods from the literature have been applied to NACC

data, specifically cross-sectional methods, Markov multi-state models, and partly conditional models. Cross-sectional methods and multi-state methods have been used in AD literature, but the third method, partly conditional models, has not yet been used in this disease area. The ability and ease of these models to incorporate competing risk information and time-varying covariates will be compared. These differing methods for modeling transitions

between disease stages will be compared, and strengths and limitations of each method will be discussed.

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