… updated longitudinal information in survival prediction while … between the longitudinal trajectory
and the survival process. … -to-event outcomes using longitudinal biomarkers. A simulation …

… data, we are interested in a multivariate longitudinal … we propose a joint longitudinal and
survival model that has a … joint survival and longitudinal model in which the longitudinal markers …

… longitudinal changes in cytokines and survival, we estimated a slope for each cytokine on
every patient by fitting a least-squares regression line to longitudinal … slopes and survival using …

… outcomes of the investigated study. This joint modeling approach thus allowed us to make
a quantitative inference from early longitudinal biomarker data to predict survival outcome in …

… survival predictions. In this dissertation, we consider dynamic survival models and prediction
… that incorporate the effects of longitudinally collected covariates on time-to-event outcomes. …

… longitudinal changes in biomarker concentrations over time and examined these patterns with
regard to patient outcomes … Studies that only explored the relationship between biomarker …

… multiple biomarkers with a shared latent reduced rank longitudinal … biomarkers is more
flexible than some existing methods since the latent trajectory shared by the multiple biomarkers …

… As there are almost no data on longitudinal changes, we aimed to evaluate those in the
present prospective observational study during chemotherapy and the course of disease. …

… In particular, we consider 500 longitudinal biomarkers in the simulation studies and 1000
longitudinal biomarkers in the real data application. In pHDJM we adopt linear mixed-effects …

… are associated with outcomes (mortality and intubation) into a predictive model. We
hypothesized that longitudinal biomarkers are more suited for predicting outcomes within the next …