Risk prediction models can translate genetic association findings for clinical decision-
making. Most models are evaluated on their ability to discriminate, and the calibration of risk …

While analysing time-to-event data, it is possible that a certain fraction of subjects will never
experience the event of interest and they are said to be cured. When this feature of survival …

Predicting risks of chronic diseases has become increasingly important in clinical practice.
When a prediction model is developed in a cohort, there is a great interest to apply the …

In recent years, there has been a significant urge of interest in longitudinal and survival data
modeling. This approach holds particular significance in cancer research, where it enables …

In recent years there have been a series of advances in the field of dynamic prediction.
Among those is the development of methods for dynamic prediction of the cumulative …

The use of Cox proportional hazards regression to analyze time-to-event data is ubiquitous
in biomedical research. Typically, the frequentist framework is used to draw conclusions …

Typically, electronic health record data are not collected towards a specific research
question. Instead, they comprise numerous observations recruited at different ages, whose …

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates
are measured repeatedly. In survival studies, the risk for an event is estimated using Cox …

Multiple biomarkers on different biological pathways are often measured over time to
investigate the complex mechanism of disease development and progression. Identification …

We propose a novel model for hierarchical time-to-event data, for example, healthcare data
in which patients are grouped by their healthcare provider. The most common model for this …