A scientist looks at a flask of blue liquid.


Doctor of Philosophy (PhD)

Admission details can be found here.

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

  • Thierry Chekouo: Bayesian statistics and computation; Markov chain Monte Carlo and related simulation methods, functional data analysis; high-dimensional data analysis; bioinformatics, biostatistics
  • 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

Students in biostatistics are required to complete a minimum of six courses on top of BIST 600 Research Seminar and MDCH 600 Block Week course.

The following are the three compulsory courses of the minimum six courses:

  • MDCH 640 Fundamentals of Epidemiology
  • STAT 721 Statistical Inference
  • STAT 641 Statistical Learning or STAT 631 Computational Statistics

Seminar and electives:

  • MDCH 600 (Fall block week): Introduction to Community Health Sciences
  • BIST 600: Research Seminar course
  • A Minimum 3 courses from List A or B, with at least one course from each of List A and B.

Performance level:

Should maintain a minimum cumulative Grade Point Average (GPA) of 3.00 calculated on a four point scale at the end of each registration year and attain at least a B- on each course taken for credit.

  1. 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  

  2. List B: Biostatistics/Statistics

    MDCH 611: Models for Health Outcomes (Biostatistics II)
    MDCH 612: Models for Repeated Measures Studies and Time-to-Event Studies  (Biostatistics III
    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

The PhD is a full-time degree with an expected completion time of four years. The maximum time allowed is six years.

  • Supervisors will decide with their students on what courses the students have to take, and what preliminary exams the students have to write.
  • A supervisory committee must be established within three months after the program starts. The supervisory committee includes a supervisor (and a co-supervisor if there is one), and two supervisory committee members.
  • The supervisory committee should meet with the student regularly to provide guidance through the program.

Written preliminary exams

Biostatistics doctorate students must pass two written preliminary examinations usually during the first year but no later than 18 months from the beginning of their doctoral programs. The material of the 2 preliminary exams will be based two of the following courses:

  • STAT 701
  • STAT 721
  • STAT 631
  • STAT 635
  • STAT 641

Written candidacy proposal and oral candidacy exam

  • Program course work and examination requirements completed (prior to candidacy oral examination)
  • Written proposal submitted to supervisory committee (recommended six months, minimum four months in advance of expected oral examination date)
  • Reading list approved by the graduate program director (at least three months prior to scheduling oral examination)
  • Written research proposal approved (at least two months prior to scheduling oral examination)
  • The oral candidacy examination must be taken no later than 28 months from the start of the doctoral program. Prior to the oral examination, the student must have completed all the course work and the written preliminary examinations
  • The oral candidacy exam must be scheduled at least four weeks before the intended date.
  • The exam committee contains a supervisor, a co-supervisor (if it is applicable), supervisory committee members (usually two), and two examiners (outside of student’s program, within the department or within the university)

More information can be found under the following links:

Faculty of Graduate Studies candidacy regulations

Departmental guidelines for candidacy examinations

Thesis and thesis oral examination

The 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 thesis oral exam committee contains a supervisor, a co-supervisor (if applicable), supervisory committee members (usually two), an examiner (outside of student’s program, within the department or within the university) and an external examiner (from outside of the university)
  • The external examiner must be applied for approval from Faculty of Graduate Studies six weeks prior the intended examination date
  • 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.