… employed models. We describe a new approach for survival analysis regression models,
based on learning mixtures of Cox regressions to model individual survival distributions. We …

… Statistical methods for longitudinal data have proposed to model censored survival data by
… Dynamic prediction in clinical survival analysis using temporal convolutional networks. IEEE …

… (2023), we show that fitting hazard deep learning models to a DSA likelihood on EEP data
is … Finally, we propose, survTLS our extension to TLS for survival analysis, allowing to further …

… To address the problems introduced by a different number of treatments as well as by
censoring, the authors proposed modifying the survival data so that each observation has the …

… According to the established nomogram, we further developed a dynamic web-based
survival calculator to simplify application of this nomogram (available from https://nomoresearch.…

… a weighted distance-based dynamic ensemble regression framework and applied it in gastric
cancer survival time prediction to realize dynamic regression for the survival time of gastric …

… In the time-to-event analysis, this latter aspect is expressed by right-censoring, defined as
the loss to follow-up or survival data limited to a given time point. Although censoring is …

… Third, we develop a dynamic prediction framework that … the estimation method and the
dynamic prediction framework. In … present the results of model fitting and dynamic prediction. In …

… The hazard function represents one of the main quantities of interest in the analysis of
survival data. We propose a general approach for modelling the dynamics of the hazard function …

… of survival at diagnosis (0 years) and conditional survival based on years already survived
after diagnosis (1–4 years). Conditional survival curves (A); and updated survival data (B) and …