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Note: This is the 2017–2018 eCalendar. Update the year in your browser's URL bar for the most recent version of this page, or .
Note: This is the 2017–2018 eCalendar. Update the year in your browser's URL bar for the most recent version of this page, or .
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.
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.
Biostatistics : Assessment of student's ability to assimilate statistical theory.
Terms: Summer 2018
Instructors: There are no professors associated with this course for the 2017-2018 academic year.
Restriction: Enrolment in the Ph.D. in Biostatistics
Exam is held once yearly
Biostatistics : Assessment of student's ability to assimilate and apply statistical theory and methods for biostatistics.
Terms: Summer 2018
Instructors: There are no professors associated with this course for the 2017-2018 academic year.
Restriction (s): Enrolment in the Ph.D. in Biostatistics
Biostatistics : Essential skills for thesis writing and defence, including essential elements of research proposals, methodological development and application, and presentation.
Terms: Fall 2017, Winter 2018
Instructors: Abrahamowicz, Michal; Kramer, Michael (Fall) Abrahamowicz, Michal; Kramer, Michael (Winter)
Note: Required for Ph.D. students
0-28 credits from the following list: (if a student has not already successfully completed them or their equivalent)
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 2017
Instructors: Hanley, James Anthony (Fall)
Prerequisites: Permission of instructor. Undergraduate course in mathematical statistics at level of MATH 324.
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 2018
Instructors: Moodie, Erica (Winter)
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 2017
Instructors: Benedetti, Andrea; Hanley, James Anthony (Fall)
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 2018
Instructors: Neslehova, Johanna (Winter)
Mathematics & Statistics (Sci) : This course consists of the lectures of MATH 423 but will be assessed at the 500 level.
Terms: Fall 2017
Instructors: Yang, Yi (Fall)
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 2017
Instructors: Asgharian-Dastenaei, Masoud (Fall)
Fall
Prerequisite: MATH 357 or equivalent
Mathematics & Statistics (Sci) : Sampling theory (including large-sample theory). Likelihood functions and information matrices. Hypothesis testing, estimation theory. Regression and correlation theory.
Terms: Winter 2018
Instructors: Khalili Mahmoudabadi, Abbas (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).