Prof. PhD A.H. Zwinderman

Full Professor
Main activities
Education, Research, Other
Focus of research

My research concerns developing statistical methods for new research designs and new data formats in biomedical science. In particular I focus on omics and big data in general. For integrating omics data I am working on high dimensional multivariate models such as canonical correlation and redundancy analysis and partial least squares methods in general. I developed several software tools that are capable of integrating multiple datasets, each consisting of hundredthousands of variables. Another line of research is focused on using existing registry data for epidemiological cohort studies that are performed in the AMC. I developed methods to jointly perform multiple record linkage and association analyses that are truly unbiased even if the record linkage is done with relatively low quality link variables. This is truly big data analysis because my tools are capable of linking/analyzing datasets covering the entire Dutch population. Both the omics and the linked record data analysis tools are based on parallel computing and make use of clustercomputers, GPU computing and the Dutch grid of computing facilities. Related research subject are related to developing dynamic (prediction) models and causal effects analysis. In addition to these methodological subjects I am co-initiator and co-PI of the HELIUS cohort study of about 25,000 inhabitants of Amsterdam.

Key publications
  • Voors Adriaan A., Ouwerkerk Wouter, Zannad Faiez, van Veldhuisen Dirk J., Samani Nilesh J., Ponikowski Piotr, Ng Leong L., Metra Marco, ter Maaten Jozine M., Lang Chim C., Hillege Hans L., van der Harst Pim, Filippatos Gerasimos, Dickstein Kenneth, Cleland John G., Anker Stefan D., Zwinderman Aeilko H. Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure European journal of heart failure 2017;19 (5):627-634 [PubMed]
  • van Iperen E. P. A., Hovingh G. K., Asselbergs F. W., Zwinderman A. H. Extending the use of GWAS data by combining data from different genetic platforms PLoS ONE 2017;12 (2) [PubMed]
  • Musoro J. Z., Struijk G. H., Geskus R. B., ten Berge Ijm, Zwinderman A. H. Dynamic prediction of recurrent events data by landmarking with application to a follow-up study of patients after kidney transplant Statistical methods in medical research 2018;27 (3):832-845 [PubMed]
  • Hof M. H. P., Zwinderman A. H. A mixture model for the analysis of data derived from record linkage Statistics in medicine 2015;34 (1):74-92 [PubMed]
  • Waaijenborg Sandra, Zwinderman Aeilko H. Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis Bioinformatics (Oxford, England) 2009;25 (21):2764-2771 [PubMed]
All Publications