Improving Robust High-Dimensional Causal Inference and Prediction Modelling

researcher looking at Petri dish

Lead by: Celia M.T. Greenwood, Full Member, McGill Centre for Translational Research in Cancer Senior Investigator, Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research. Associate Professor, Departments of Oncology, and Epidemiology, Biostatistics and Occupational Health, and Division of Cancer Epidemiology, McGill University; Gabriela Cohen Freue, Associate Professor, Department of Statistics, University of British Columbia.

Recent advances in “-omics” technologies allow the simultaneous quantitation of thousands of features, revolutionizing the way that scientists can measure pathogenic processes or responses to therapies. Despite their potential to improve diagnostics and prediction methods in routine clinical care, these high-dimensional multi-faceted data still present significant challenges, including measurement errors, outliers, multivariate responses, and complex correlation structures. For example, genomics data, imaging scans and years of standard clinical lab tests are sometimes available from the same subject. Furthermore, many of these features may be inaccurately measured, as a consequence of technical limitations or errors in recording data.

A central goal of this Collaborative Research Team (CRT) is to develop and establish an advanced analytical framework for the study and integration of complex data in biomedical sciences, including advanced regularized regression methods, robust regularized instrumental variable methods, and matrix-valued causal models, all for high-dimensional settings. These advancements are essential for building useful models in precision medicine.

Members of this CRT have recognized expertise in the key components of these themes, including penalized estimators, instrumental variable estimators, robustness, analysis of survival and longitudinal data, as well as estimation of multivariate response models. This CRT will build on their respective strengths to solve methodological challenges and to build software and tools with widespread applicability.

Highly Qualified Personnel (HQP), which will span from undergraduate students to post-doctoral trainees, will get the opportunity to work with this CRT’s partner organizations and their data, to perform applied in-depth data analyses. They will also work on cleaning code to make it more readable and user-friendly, and packaging code for public release on github and repositories such as CRAN. 

Call for Letters of Intent

Researchers can now apply to the next round of CRTs. LOIs are due May 7, 2021. We encourage researchers from any field to apply, no matter what stage you are at in your career.

Learn how to submit your LOI

Team Leader

Celia M.T. Greenwood, Full Member, McGill Centre for Translational Research in Cancer Senior Investigator, Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research. Associate Professor, Departments of Oncology, and Epidemiology, Biostatistics and Occupational Health, and Division of Cancer Epidemiology, McGill University.

Gabriela Cohen Freue, Associate Professor, Department of Statistics, University of British Columbia.

Team Members

Sahir Bhatnagar, Assistant Professor, Departments of Radiology and Epidemiology & Biostatistics, McGill University.

Dehan Kong, Assistant Professor, Department of Statistical Sciences, University of Toronto, Karim Oualkacha, Professeur agrégé, Département de mathématique, Université du Québec à Montréal.

David Soave, Assistant Professor, Mathematics Department, Wilfrid Laurier University.

Linbo Wang, Assistant Professor, Department of Statistical Sciences, University of Toronto.

Brent Richards, William Dawson Professor, Lady Davis Institute for Medical Research, McGill University.

Marie Hudson, Associate Professor, Lady Davis Institute for Medical Research, McGill University.

Tom Blydt-Hansen, Associate Professor of Pediatrics, University of British Columbia. Director, Multi Organ Transplant Program, BC Children’s Hospital.

Zhaolei Zhang, Professor, University of Toronto.

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