Better predictions using big data sets.
This project aims to develop a statistical framework that utilizes big datasets to identify and account for heterogeneity in predictor effects. Hereto, existing meta-analysis methods will be improved upon and a new penalization algorithm will be derived to enhance the estimation and inclusion of relevant predictors during the adaptation and development of prediction models. Ultimately, this will reduce the need for further validation and adaptation of prediction models.