In this thesis, we developed extensions for the joint modeling framework for longitudinal and time-to-event data, motivated by various clinical research questions in cardiothoracic surgery. These extensions focus in the handling of intermediate events during follow-up, feature selection in multivariate settings such as multiple longitudinal outcomes and multi-state processes using Bayesian shrinkage priors and sensitivity analysis for missing data under the joint modeling framework.
|Award date||14 Dec 2021|
|Place of Publication||Rotterdam|
|Publication status||Published - 14 Dec 2021|