Academic background: More information can be found here.
Completion time: The expected completion time is two years. Maximum allowed is four years.
Advanced credit: If you wish to apply for advanced credit, you must request it at the time of application. Advanced credit may be granted for graduate level courses with a B grade or higher within the past five years from a recognized institution that are not part of another degree. Advanced credit is not to exceed 1/3 of your graduate program.
This program is an interdisciplinary program, ran jointly by the Departments of Mathematics & Statistics (Faculty of Science) and Community Health Sciences (Cumming School of Medicine). For more information please see: https://obrieniph.ucalgary.ca/groups/university-calgary-biostatistics-centre
Students are required to complete a minimum of five courses, including the following:
The five courses shall include:
- MDCH 640: Fundamentals of Epidemiology
- STAT 721: Statistical Inference
- A minimum of three courses from list A or B, with at least one from each of A and B
Students must also enrol in he following two additional courses, which are not included in the calculation of the GPA, nor count as part of the minimum 15 units of coursework required above:
- MDCH 600 (fall block week): Introduction to Community Health Sciences
- BIST 600: Research Seminar
List A: Epidemiology and health
MDCH 641 Introduction to Clinical Trials
MDCH 644 Surveillance I: Data Handling for Infection Control
MDCH 647 Clinical Epidemiology
MDCH 649 Epidemiology of Infectious Diseases
MDCH 661 Health Economics I
MDCH 662 Economic Evaluation
MDCH 663 Decision Analysis in Health Economics
MDCH 664 Administrative Data Analysis Methodology
MDCH 681 Health Research Methods
MDCH 740 Advanced Epidemiology
MDCH 741 Systematic Reviews and Meta-analysis
List B: Biostatistics/statistics
MDCH 611 Models for Health Outcomes
MDCH 612 Models for Repeated Measures Studies and Time-to-Event Studies
STAT 619 Bayesian Statistics
STAT 625 Multivariate Analysis
STAT 631 Computational Statistics
STAT 633 Survival Analysis
STAT 635 Generalized Linear models
STAT 637 Non-linear Regression
STAT 641 Statistical Learning
STAT 701 Theory of Probability I
- Gemai Chen: Parametric and non-parametric regression, non-linear time series modelling of environmental changes, extreme-value analysis, goodness of fit, emperical process, sample survey, quality control, high dimensional data analysis, survival analysis, financial & Economic model building and forecasting, statistical consulting
- Rob Deardon: Bayesian statistics and computation, infectious disease epidemiology and surveillance, statistical learning, spatial epidemiology, experimental design
- Yunqi Ji: Longitudinal data analysis, survival analysis, missing data and measurement errors, machine learning, data visualization, dynamic system modelling, administrative database analytics, clinical analytics, health services research, health system performance evaluation
- Karen Kopciuk: Multi-state models, survival data analysis, multivariate data analysis, statistical genetics, genetic risk, genetic epidemiology
- Chel Hee Lee: Theory and application of imprecise probabilities, design and analysis of clinical trials, and statistical problems in clinical research and services
- Alex de Leon: Assessment of diagnostic tests, copula models, estimating functions and estimating equations, statistical problems in medicine, diagnostic tests, pseudo and composite likelihood
- Quan Long: Machine learning, precision medicine, omics big-data integration, bioinformatics
- Xuewen Lu: Big data and high-dimensional data analysis, empirical likelihood, survival analysis and randomly censored data, nonparametric and semiparametric models
- Hua Shen: Analysis of recurrent events, longitudinal data analysis, general linear models, causal inference, mixture models, statistical learning, survival analysis
- Jingjing Wu: Minimum distance estimation, non/semi-parametric models, regression/logistic regression models, mixture models, case-control studies, survival analysis, genetic studies
- Qingrun Zhang: Machine learning, high-dimensional data mining, biostatistics, bioinformatics
A master’s thesis-based student must complete a thesis on a topic to be agreed to by the student and their supervisor.
- After completion of the thesis, the student must pass a thesis oral examination.
- A master's thesis oral exam committee contains a supervisor, a co-supervisor (if applicable), an examiner (an additional member of the University of Calgary academic staff), and an internal examiner (a member of the University of Calgary academic staff).
- The exam must be scheduled at least four weeks prior to date of oral exam.
- Examiners must have a copy of the thesis at least three weeks prior to the date of oral exam.
- Final thesis oral examinations are open.
More information can be found on the Faculty of Graduate Studies website under examinations.