Personal profile
Research interests
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.
Specialisation
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Collaborations and top research areas from the last five years
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Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure
Voors, A. A., Ouwerkerk, W., Zannad, F., van Veldhuisen, D. J., Samani, N. J., Ponikowski, P., Ng, L. L., Metra, M., ter Maaten, J. M., Lang, C. C., Hillege, H. L., van der Harst, P., Filippatos, G., Dickstein, K., Cleland, J. G., Anker, S. D. & Zwinderman, A. H., 2017, In: European journal of heart failure. 19, 5, p. 627-634Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile19 Downloads (Pure) -
Extending the use of GWAS data by combining data from different genetic platforms
van Iperen, E. P. A., Hovingh, G. K., Asselbergs, F. W. & Zwinderman, A. H., 2017, In: PLoS ONE. 12, 2, e0172082.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile7 Downloads (Pure) -
Dynamic prediction of recurrent events data by landmarking with application to a follow-up study of patients after kidney transplant
Musoro, J. Z., Struijk, G. H., Geskus, R. B., ten Berge, I. & Zwinderman, A. H., 2018, In: Statistical methods in medical research. 27, 3, p. 832-845Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile11 Downloads (Pure) -
A mixture model for the analysis of data derived from record linkage
Hof, M. H. P. & Zwinderman, A. H., 2015, In: Statistics in medicine. 34, 1, p. 74-92Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile14 Downloads (Pure) -
Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis
Waaijenborg, S. & Zwinderman, A. H., 2009, In: Bioinformatics (Oxford, England). 25, 21, p. 2764-2771Research output: Contribution to journal › Article › Academic › peer-review