… A straightforward extension is to include a time-varying dynamic regression coe cient to
model the changing effects of the covariates over time. Bayesian inference Using Gibbs …

… treatment regime and widely known as DTR survival analysis.12, 13, 14, … survival analysis
of lung cancer trial data, we faced a challenge to work with repeatedly measured survival data …

… dynamic covariates. Here we consider the application of this model to the clustered survival
data … First we fit a dynamic regression model, that is, including all covariates. The cumulative …

… black-box data adaptive prediction models. We apply our recently developed counterfactual
dynamic survival model (CDSM) to static and longitudinal observational data and testify that …

… This analysis using dynamic logistic regression model enabled us to assess the full effect
of adherence on mortality without confounding from factors on the causal pathway. …

… Significant work has gone into the development of machine learning algorithms that can
accommodate survival data. These nonparametric learning approaches can handle nonlinear …

… marker or several markers combined through a regression model), the receiver operating …
In extending the AUC to accommodate survival data, the concept of a time-dependent AUC …

… The purpose of this paper is to use the dynamic approach in the neural network method,
where in this method a dynamic neural network that suits the nature of discrete survival data …

… In the derivation above, we show that the joint distribution of longitudinal and survival data
that satisfies the landmark Cox model (19.2) exists, and provide an algorithm to generate data …

… models for dynamic prediction using three clinical data sets, that contain both longitudinal
covariate measurements and time-to-event data. … joint models of longitudinal and survival data. …