Biostatistics

Master of Science (MSc)

Academic background: Successful master's applicants typically have a bachelor’s degree in statistics or closely related field. The following courses are

  • Mathematics courses: Calculus, linear algebra
  • Statistics courses: Introduction to statistics, mathematical statistics, linear regression, sampling and experimental design
  • Some biology or health-related courses would be desirable, but are not essential
  • Computing skills: R or equivalent

The above requirements are in addition to the minimum admission requirements of the Faculty of Graduate Studies. Please note that meeting the admission requirements does not guarantee admission. Undergraduate research assistant experience or work experience in a statistics related field is an asset.

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.

    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: Theory of Estimation
    • 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
    1. List A: Epidemiology and health

      MDCH 641 Introduction to Clinical Trials
      MDCH 643 Research in Healthcare Epidemiology and Infection Control
      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

    2. 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
      STAT 721 Statistical Inference

    • Thierry Chekouo: Bayesian statistics and computation; Markov chain Monte Carlo and related simulation methods, functional data analysis; high-dimensional data analysis; bioinformatics, biostatistics
    • Rob Deardon: Bayesian statistics and computation, infectious disease epidemiology and surveillance, statistical learning, spatial epidemiology, experimental design
    • Gordon Fick: Biostatistics, health/medical-related biostatistical methodology
    • Karen Kopciuk: Multi-state models, survival data analysis, multivariate data analysis, statistical genetics, genetic risk, genetic epidemiology
    • Alex de Leon: Assessment of diagnostic tests, copula models, estimating functions and estimating equations, statistical problems in medicine, diagnostic tests, pseudo and composite likelihood
    • Haocheng Li: Functional data analysis, longitudinal data analysis, survival data analysis, mixed effects models, composite likelihood, missing data, cancer research
    • Quan Long: Bioinformatics, genomics, proteomics
    • Xuewen Lu: Big data and high-dimensional data analysis, empirical likelihood, survival analysis and randomly censored data, nonparametric and semiparametric models    
    • Alberto Nettel-Aguirre: Applications of biostatistics, statistical learning, data mining, social network analysis, health research, shape analysis 

    • Tolulope Sajobi: Classification models for prediction in multivariate repeated measures data, multivariate longitudinal data, statistical methods for quality of life and behavioural outcomes
    • Hua Shen: Analysis of recurrent events, longitudinal data analysis, general linear models, causal inference, mixture models, statistical learning, survival analysis
    • Tyler Williamson: Generalized linear models with non-canonical link functions, correlated binary data, statistics for epidemiology, big data analytics, methods for electronic medical record data
    • Jingjing Wu: Minimum distance estimation, non/semi-parametric models, regression/logistic regression models, mixture models, case-control studies, survival analysis, genetic studies

    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.