Statistical Methods for Data Integration

Our group is actively involved in innovative research and the development of novel statistical methodologies for the analysis of high-dimensional data with an emphasis on genetics and genomics. We work closely with basic scientists and clinicians in different domains. We evaluate current methodologies, identify gaps, propose improvements, and develop new methodologies for high-dimensional data such as gene expression, DNA copy number variation and Single Nucleotide Polymorphisms, microRNA data, microbiomes, etc.. We are particularly interested in Integrative Genomics, where we are developing methodologies to integrate genetic, genomic, proteomic, clinical, environmental, and lifestyle data. We aim to enhance statistical power, improve precision and accuracy, and gain a better insight into fundamental biological processes through statistical methods.

Clinical Trials, Systematic Reviews and Meta-Analyses

We work on a wide range of methodological issues related to clinical trials and systematic reviews, particularly meta-analyses. Our meta-analysis research topics currently include network meta-analysis, robust random-effects meta-analysis and multivariate meta-analysis. We evaluate the performance of current methodologies, assess relevance to innovative collaborative projects, validate models, identify gaps, and work toward new and improved methodologies. We consider meta-analysis to be one type of data integration, an increasingly important tool in health sciences.