Survival analysis deals with the study of the time until an event of interest occurs. The CoxProportional Hazards model (Cox model) is commonly used to model the relationship betweena survival...Show moreSurvival analysis deals with the study of the time until an event of interest occurs. The CoxProportional Hazards model (Cox model) is commonly used to model the relationship betweena survival outcome and a set of cross-sectional covariates, but it cannot handle longitudinal co-variates, i.e. covariates that are repeatedly measured over time. Traditional ways to deal withlongitudinal covariates include joint modelling, landmarking and the time-dependent Cox model,but to date their applicability has mostly been restricted to problems with a small number oflongitudinal covariates.Recently, the increasing availability of repeated measurements in biomedical studies has mo-tivated the development of statistical methods specifically designed to predict survival from alarge (potentially high-dimensional) number of longitudinal covariates. Due to the fact that suchmethods are still quite new, little is known about how these methods may perform in practice.The aim of this thesis is to compare the performance of various statistical methods to predictsurvival on a real dataset where many longitudinal covariates are available as predictors. Fourmethods were chosen for comparison, including two novel methods employing different techniquesto harness the longitudinal information, Penalized Regression Calibration (PRC) and Multivari-ate Functional Principal Component Cox (MFPCCox) model, and penalized Cox models usinglandmarking (last observation carried forward method) and baseline measurements respectively.These methods were applied to the data from the Alzheimer’s Disease Neuroimaging Initiative(ADNI) study in the context of dynamic prediction of time to develop dementia. The ADNIstudy monitored the development of dementia in cohort of elderly individuals, and collected anextensive, heterogeneous set of markers over multiple years of follow-ups. Predictions were com-puted using a total of 26 covariates, of which 21 were longitudinal. The predictive performanceof the models was evaluated considering three performance measures (time-dependent AUC, Cindex, and Brier score).The results showed that the best performing method depended on the choice of performancemeasure, landmark time, and prediction time. Landmarking was the best performing methodwhen looking at the time-dependent AUC and C index, whereas PRC was the best performingmethod in terms of Brier score. Landmarking, PRC, and MFPCCox outperformed the baselinemodel that ignored the follow-up information, suggesting that the longitudinal information inthe ADNI data can be used to improve predictions for dementia. Overall, our results seem toindicate that for the ADNI data a simple approach such as landmarking may be enough to deliveraccurate predictions, when compared to more sophisticated approaches (PRC and MFPCCox)that model the trajectories of longitudinal covariates.Show less
