My research is supported by the following grants:
Lead applicant. Better predictions using big data sets. Innovational Research Incentives Scheme VENI. The Netherlands Organisation for Health Research and Development. The Hague, Netherlands. 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.
Total funding amount: EUR 250,000
Supported by. Promoting tailored healthcare: improving methods to investigate subgroup effects in treatment response when having multiple individual participant datasets. TOP grants. The Netherlands Organisation for Health Research and Development. The Hague, Netherlands. The overall aim of this project is to evaluate and improve prevailing approaches, and to develop novel methods where needed, for investigating and interpreting subgroup effects in treatment response when multiple IPD sets are available. It should lead to more credible evidence about whether a relevant subgroup effect exists.
Total funding amount: EUR 674,956
Co-applicant. Predicting Progression in Chronic Kidney Disease. Junior Researcher Project 2016. Radboud Institute for Health Sciences. Nijmegen, The Netherlands. This project aims to compare systematically review and meta-analyse prognostic factors and prediction models for end stage renal disease, cardiovascular outcome and mortality in chronic kidney disease.
Total funding amount: EUR 240,000
Co-applicant. Methods for systematic review and meta-analysis of prognostic factors and prediction modelling studies. Cochrane Methods Innovation Funds (Round 2). Cochrane. London, The United Kingdom. This project aims to facilitate the conduct of systematic reviews in prognostic research by providing methodological guidance to meta-analyze the performance of existing prediction models, and to meta-analyze the incremental value of certain prognostic factors.
Total funding amount: GBP 50,000
Supported by. Incorporating real-life clinical data into drug development. Innovative Medicines Initiative. European Union, Belgium. Incorporating data from "real life" clinical settings into drug development and associated decision-making represents a serious challenge for pharmaceutical companies, regulators, and health authorities alike. By bringing together all key stakeholder groups (namely industry, academia, regulatory agencies, reimbursement agencies, healthcare budget holders, and patient groups) to share their insights and know-how, GETREAL will develop new approaches for incorporating real life data into drug development, and pave the way for a greater consensus on this issue.
Total funding amount: EUR 16,952,280
Supported by. Clinical prediction rules: towards a framework for meta-analysis, validation, and updating. TOP grants. The Netherlands Organisation for Health Research and Development. The Hague, Netherlands. This project aims to develop a framework for obtaining valid and generalisable clinical prediction models for situations where individual patient data are available, and for situations where only published prediction models are available.
Total funding amount: EUR 673,116
Supported by. Midland Hub for Trials Methodology Research at University of Birmingham. Research Grant. Medical Research Council. London, The United Kingdom. The plan for this research is to create a regional facility at the University of Birmingham in which to investigate the best ways to design, conduct and analyse trials, providing advice, support and training to those carrying out trials and to the public. Ultimately this will result in beneficial treatments being delivered to patients more efficiently.
Total funding amount: GPB 2,547,690