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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 700 Ph.D. Comprehensive Examination Part A
Overview
Biostatistics : Assessment of student's ability to assimilate statistical theory.
Terms: Summer 2020
Instructors: Saha Chaudhuri, Paramita; Hanley, James Anthony (Summer)
Restriction: Enrolment in the Ph.D. in Biostatistics
Exam is held once yearly
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BIOS 701 Ph.D. Comprehensive Examination Part B
Overview
Biostatistics : Assessment of student's ability to assimilate and apply statistical theory and methods for biostatistics.
Terms: Summer 2020
Instructors: Benedetti, Andrea; Greenwood, Celia M T (Summer)
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 2019, Winter 2020
Instructors: Moodie, Erica (Fall) Moodie, Erica (Winter)
Note: Required for Ph.D. students
Complementary Courses (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 2019
Instructors: Hanley, James Anthony (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 2020
Instructors: Saha Chaudhuri, Paramita (Winter)
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BIOS 624 Data Analysis & 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 2019
Instructors: Benedetti, Andrea (Fall)
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MATH 523 Generalized Linear Models (4 credits)
Overview
Mathematics & Statistics (Sci) : Modern discrete data analysis. Exponential families, orthogonality, link functions. Inference and model selection using analysis of deviance. Shrinkage (Bayesian, frequentist viewpoints). Smoothing. Residuals. Quasi-likelihood. Contingency tables: logistic regression, log-linear models. Censored data. Applications to current problems in medicine, biological and physical sciences. R software.
Terms: Winter 2020
Instructors: Neslehova, Johanna (Winter)
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MATH 533 Honours Regression and Analysis of Variance (4 credits)
Overview
Mathematics & Statistics (Sci) : This course consists of the lectures of MATH 423 but will be assessed at the 500 level.
Terms: Fall 2019
Instructors: Yang, Yi (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 2019
Instructors: Stephens, David (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 2020
Instructors: Asgharian-Dastenaei, Masoud (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).
