… projection techniques, dynamic modeling [23] … regression analysis, as Cox models do not
rely on any specific assumption on the parametric shape of the hazard function. In this analysis, …

… Such prediction models must be updated in order to remain useful. In this study, we investigate
dynamic model updating of clinical survival prediction models. In contrast to discrete or …

… facilitates the implementation of survival models for credit risk data. In particular, if we … obtain
the standard logistic regression model. Risk factors used in credit risk models fall into three …

… to model survival data are based on the Cox model, and we too will here consider models
that are extensions of the Cox regression model. … techniques for high dimensional data, is that …

… dynamic controls R package survivalROC • ROC function survivalROC() accepts censored
survival data … t C/D , where s is the “baseline” time of the subsetted data set (ie, T ≥ s), while t (…

… regression with different types of survival data. The review covers various survival scenarios,
including randomly censored data, data … for investigating the dynamic pattern of covariate …

… Illustration of survival data with longitudinal measurements where subjects are aligned
based on the synchronization event. Colored dots indicate the times at which longitudinal …

… relevant summary measures, such as survival probabilities, which will aid in … dynamically
updated estimates of survival probabilities, namely landmark analysis and joint models …

… The standard survival regression models, such as the Cox proportional hazard (PH)
regression model and the accelerated failure time (AFT) model, impose assumptions like the …

… survival data exists that satisfy the modeling assumptions without additional restrictions, and
propose an algorithm to generate data … models that include the landmark Cox model as a …