The defense is titled Dynamic Prediction for Survival Outcomes and Network AssistedLocalized Functional Principal Component Analysis. In the first part of the thesis, we develop a “Jointly Estimated Landmarking (JEL)” ap-proach for dynamic survival prediction using longitudinal covariates. JEL specifically models the effects of recent biomarker values (individual intercept) and change in biomarker values (individual slope) on conditional survival risk. The survival model is kept flexible with a transformation function G, including the Cox proportional hazards model and the propor-tional odds model as special cases. Time-varying models are also developed to assess the time-varying effect of longitudinal predictors. In the second part of the thesis, we first develop a network-assisted Localized Functional Principal Component Analysis (LFPCA) approach. Then network-assisted LFPCA is applied to an MEG dataset and a fMRI dataset to illustrate its power of dimension reduction and interpretable feature extraction. Finally, the principal components extracted from a longitudinal fMRI dataset are used for dynamic prediction for survival outcomes.
Committee Chair and Advisor: Dr. Kehui Chen