Background Risk prediction models for time-to-event outcomes play a vital role in
personalized decision-making. A patient's biomarker values, such as medical lab results, are …

Survival data with time-varying covariates are common in practice. If relevant, they can
improve on the estimation of a survival function. However, the traditional survival forests …

In this work, we introduce JointLIME, a novel interpretation method for explaining black‐box
survival (BBS) models with endogenous time‐varying covariates (TVCs). Existing …

This article introduces a new decision tree algorithm that accounts for time-varying
covariates in the decision-making process. Traditional decision tree algorithms assume that …

Developing an informed lapse management strategy (LMS) is critical for life insurers to
improve profitability and gain insight into the risk of their global portfolio. Prior research in …

Elderly patients are at a high risk of suffering from postoperative death. Personalized
strategies to improve their recovery after intervention are therefore urgently needed. A …

Developing an informed lapse management strategy (LMS) is critical for life insurers to
improve their profitability, and gain insight into the risk of their global portfolio. When …

Discrete hazard models are widely applied for the analysis of time-to-event outcomes that
are intrinsically discrete or grouped versions of continuous event times. Commonly, one …

Model-based recursive partitioning (MOB) is a semi-parametric statistical approach allowing
the identification of subgroups that can be combined with a broad range of outcome …

Random survival forests (RSF) have emerged as valuable tools in medical research. They
have shown their utility in modelling complex relationships between predictors and survival …