Program Requirements
Students will study theoretical and applied statistics and related fields; the program will train them to become independent scientists able to develop and apply statistical methods in medicine and biology and make original contributions to the theoretical and scientific foundations of statistics in these disciplines. Graduates will be prepared to develop new statistical methods as needed and apply new and existing methods in a range of collaborative projects. Graduates will be able to communicate methods and results to collaborators and other audiences, and teach biostatistics to biostatistics students, students in related fields, and professionals in academic and other settings.
Thesis
A thesis for the doctoral degree must constitute original scholarship and must be a distinct contribution to knowledge. It must show familiarity with previous work in the field and must demonstrate ability to plan and carry out research, organize results, and defend the approach and conclusions in a scholarly manner. The research presented must meet current standards of the discipline; as well, the thesis must clearly demonstrate how the research advances knowledge in the field. Finally, the thesis must be written in compliance with norms for academic and scholarly expression and for publication in the public domain.
Required Courses
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BIOS 701 Ph.D. Comprehensive Examination
Overview
Biostatistics : Assessment of student's ability to assimilate and apply statistical theory and methods for biostatistics.
Terms: Winter 2025
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Restriction (s): Enrolment in the Ph.D. in Biostatistics
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BIOS 702 Ph.D. Proposal
Overview
Biostatistics : Essential skills for thesis writing and defence, including essential elements of research proposals, methodological development and application, and presentation.
Terms: Fall 2024, Winter 2025
Instructors: Greenwood, Celia (Fall) Greenwood, Celia (Winter)
Note: Required for Ph.D. students
Complementary Courses (18-46 credits)
0-28 credits from the following list: (if a student has not already successfully completed them or their equivalent)
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BIOS 601 Epidemiology: Introduction and Statistical Models (4 credits)
Overview
Biostatistics : Examples of applications of statistics and probability in epidemiologic research. Source of epidemiologic data (surveys, experimental and non-experimental studies). Elementary data analysis for single and comparative epidemiologic parameters.
Terms: Fall 2024
Instructors: Dupuis, Josée (Fall)
Prerequisites: Permission of instructor. Undergraduate course in mathematical statistics at level of MATH 324.
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BIOS 602 Epidemiology: Regression Models (4 credits)
Overview
Biostatistics : Multivariable regression models for proportions, rates and their differences/ratios; Conditional logic regression; Proportional hazards and other parametric/semi-parametric models; unmatched, nested, and self-matched case-control studies; links to Cox's method; Rate ratio estimation when "time-dependent" membership in contrasted categories.
Terms: Winter 2025
Instructors: Dupuis, Josée (Winter)
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BIOS 624 Data Analysis and Report Writing (4 credits)
Overview
Biostatistics : Common data-analytic problems. Practical approaches to complex data. Graphical and tabular presentation of results. Writing reports for scientific journals, research collaborators, consulting clients.
Terms: Fall 2024
Instructors: Platt, Robert (Fall)
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MATH 523 Generalized Linear Models (4 credits)
Overview
Mathematics & Statistics (Sci) : Exponential families, link functions. Inference and parameter estimation for generalized linear models; model selection using analysis of deviance. Residuals. Contingency table analysis, logistic regression, multinomial regression, Poisson regression, log-linear models. Multinomial models. Overdispersion and Quasilikelihood. Applications to experimental and observational data.
Terms: Winter 2025
Instructors: Steele, Russell (Winter)
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MATH 533 Regression and Analysis of Variance (4 credits)
Overview
Mathematics & Statistics (Sci) : Multivariate normal and chi-squared distributions; quadratic forms. Multiple linear regression estimators and their properties. General linear hypothesis tests. Prediction and confidence intervals. Asymptotic properties of least squares estimators. Weighted least squares. Variable selection and regularization. Selected advanced topics in regression. Applications to experimental and observational data.
Terms: Fall 2024
Instructors: Dagdoug, Mohamed Mehdi (Fall)
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MATH 556 Mathematical Statistics 1 (4 credits)
Overview
Mathematics & Statistics (Sci) : Distribution theory, stochastic models and multivariate transformations. Families of distributions including location-scale families, exponential families, convolution families, exponential dispersion models and hierarchical models. Concentration inequalities. Characteristic functions. Convergence in probability, almost surely, in Lp and in distribution. Laws of large numbers and Central Limit Theorem. Stochastic simulation.
Terms: Fall 2024
Instructors: Khalili Mahmoudabadi, Abbas (Fall)
Fall
Prerequisite: MATH 357 or equivalent
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MATH 557 Mathematical Statistics 2 (4 credits)
Overview
Mathematics & Statistics (Sci) : Sampling theory (including large-sample theory). Likelihood functions and information matrices. Hypothesis testing, estimation theory. Regression and correlation theory.
Terms: Winter 2025
Instructors: Genest, Christian (Winter)
Winter
Prerequisite: MATH 556
12 credits (chosen and approved in consultation with the student's academic adviser), at the 500 level or higher, in statistics/biostatistics.
6 credits (chosen and approved in consultation with the student's academic adviser), at the 500 level or higher, in related fields (e.g., epidemiology, social sciences, biomedical sciences).
