Professor of Medicine & Epidemiology

McGill University

Biography

Jay Brophy is a tenured (full) professor with a joint appointment in the Departments of Medicine and Epidemiology and Biostatistics where he works as a clinical cardiologist and does research in cardiovascular epidemiology. His research interests are eclectic and include outcomes research, pharmacoepidemiology, health technology assessment, economic analyses and clinical research which are supported partially through a FRQS chair in health technology assessment and evidence-based medicine.
He is a fellow of the Canadian Academy of Health Sciences and the Canadian Cardiovascular Society.

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Interests
  • Outcomes research
  • Pharmacoepidemiology
  • Health technology assessment (Bayesian statistics)
Education
  • PhD Epidemiology & biostatistics

    McGill University

  • Cardiology

    Université de Montreal

  • MD

    McMaster University

  • M.Eng.

    McMaster University

  • B.Eng.

    McGill University

Skills

MD

33%

Epidemiology

33%

R

15%

Bayesian Statistics

10%

Gordon setter

7%

Painting

1%

Experience

 
 
 
 
 
McGill University
Professor (Joint Medicine and Epidemiology, Biostatistics and Occupational Helath)
Jul 2001 – Present Montreal

Responsibilities include:

 
 
 
 
 
McGill University Health Center
Cardiologist
Jul 2001 – Present Montreal

Responsibilities include:

Recent Publications

Additional publications - see CV

SAMPLE PUBLICATIONS

(2022). Pacemaker risk following transcatheter aortic valve replacement - A Bayesian reanalysis. International Journal of Cardiology 355 (2022) 32–36.

PDF DOI

(2021). Bayesian reanalyses of recent CV trials. Canadian Journal of Cardiology - Sept 2021.

PDF DOI

(2020). Bayesian Interpretation of the EXCEL and other RCTs. JAMA Intern Med 2020 Jun 1;e201647.

PDF DOI

(2019). Spontaneous variability of high sensitivity cardiac troponin. Canadian Journal of Cardiology 35 (2019) 1505-1512.

PDF DOI

Accomplish­ments

Previous accomplishments (2019 - 2020)

Accomplishments (2021)

Statistical methods for risk prediction and prognostic models
Develop and validate risk prediction and prognostic models, specifically for binary or time-to-event clinical outcomes.
See certificate
Matching and Weighting for Causal Inference with R
Quasi-experimental techniques of matching and weighting methods using R to estimate causal effects from observational data.
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Machine Learning
Within R framework, topics included cross-validation and and advanced variable selection methods for regression analysis.
See certificate

Projects

(Mostly) Clinical Epidemiology with R
First 10 chapters of an online E book on clinical epidemiology using R, created with Bookdown (other chapters to follow).
TC4 Ticagrelor compared to clopidogrel in acute coronary syndromes - the TC4 comparative effectiveness study
CIHR funded project looking at the comparative effectiviness of ticagrelor versus clopidogrel in acute coronary syndromes.

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