Statistical Methods for Modeling Disease Progression

Abstract

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.