… The model is dynamic in that it enables predictions to be … how to develop a dynamic prediction
model using landmarking, … and survival data, and showing how different models can be …

… , survival data describe the occurrence of deaths over time and so form a natural time
series. This motivates the use of dynamic models, … These models combine flexible within-sample …

… Dynamic Frailty Model We now consider serially correlated survival data. To facilitate the
discussion, the serially indexed survival times are represented as indexed occurrence times of …

… We illustrate techniques through development of a dynamic survival prediction model for
people with CF in the United Kingdom using data from the UK CF Registry. Cystic fibrosis is an …

… with survival may change over time. To improve dynamic predictions, we propose a Bayesian
joint model that allows a time-varying coefficient to link the longitudinal and the survival …

… forest (RSF) is a nonparametric ensemble method for the analysis of right censored survival
data, built as a time-to-event extension of random forests for classification [12, 18]. The …

… , reliability and survival analysis literatures. It is our hope that this paper sets a tone for the
direction of future work in the development of models for survival wherein the physics of failure …

… With the increasing availability of longitudinal survival data, this approach discards potentially
valuable information. We investigate the use of temporal convolutions in capturing explicit …

… prognosis and survival. Wang et al. [33] proposed a dynamic Cox regression model in a …
Binder, “Sparse regression techniques in low-dimensional survival data settings,” Statistics …

… the survival analysis problem by utilizing longitudinal data … “Experiments” section compares
our proposed model to … prediction module for survival analysis in a dynamic manner. …