Application of Pattern Recognition Methods to Identify Dietary Patterns in Longitudinal Studies: A Novel approach in Nutritional Epidemiology

With the increasing prevalence of longitudinal nutritional data applications in medical science, there is a need for complex statistical models for the identification of dietary patterns in the longitudinal set. Advances are constantly being made in our understanding of the interpretability and application of statistical methodologies for longitudinal data. However, little guidance on these matters is available in most nutritional contexts. One of the most important features of longitudinal data is that the observations repeatedly collected over time are correlated to each other. This time-varying association among observations, which cannot be obtained solely by focusing on cross-sectional data analysis methods, is the main analytic challenge in longitudinal studies. This challenge is particularly relevant in the nutritional field, where researchers strive to identify useful and understandable dietary patterns from large-scale nutritional data. In nutritional epidemiology, dietary patterns are derived using pattern recognition (PR) methods. Generally, there are two types of PR methods; supervised and unsupervised. Although many nutritional studies applied cross-sectional PR methods to the identification of dietary patterns, however, the assumption of these methods might not be suitable for the identification of patterns in longitudinal data. Currently, extensions to both supervised and unsupervised cross-sectional PR methods for revealing patterns in longitudinal data exist in the literature. However, none of these methods have been applied to the identification of dietary patterns where nutritional data collected repeatedly over time. Recently, longitudinal principal component analysis (LPCA) and unbiased random effects expectation maximization algorithm (RE-EM) tree methods, as a substitute to principal component analysis (PCA) and regression tree analysis (RT), for revealing pattens in longitudinal studies are developed. This thesis introduces the first application of LPCA and unbiased RE-EM tree, as unsupervised and supervised PR methods, respectively, for the analysis of longitudinal nutritional data. To illustrate these methods, an analysis of dietary patterns in a representative sub-sample of the Saskatchewan Bone Mineral Study (BMAS) is presented. Results showed that the models presented in this thesis seem feasible and useful for the identification of dietary patterns and their trajectories where nutritional data is collected longitudinally. In this sense, this thesis assists the nutritional epidemiologists and researchers in understanding the importance, role, and meaning of the consideration of time-varying associations in diet. It also introduces new dietary pattern analysis methods in longitudinal nutritional studies using LPCA and unbiased RE-EM tree