Biostatistics (Ph.D.)
Offered by: Epidemiology and Biostatistics (Faculty of Medicine & Health Sciences)
Degree: Doctor of Philosophy
Program Description
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.
Note: For information about Fall 2025 and Winter 2026 course offerings, please check back on May 8, 2025. Until then, the "Terms offered" field will appear blank for most courses while the class schedule is being finalized.
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
| Course | Title | Credits |
|---|---|---|
| BIOS 701 | Ph.D. Comprehensive Examination. | 0 |
Ph.D. Comprehensive Examination. Terms offered: this course is not currently offered. Assessment of student's ability to assimilate and apply statistical theory and methods for biostatistics. | ||
| BIOS 702 | Ph.D. Proposal. | 0 |
Ph.D. Proposal. Terms offered: this course is not currently offered. Essential skills for thesis writing and defence, including essential elements of research proposals, methodological development and application, and presentation. | ||
Complementary Courses (18-46 credits)
0-28 credits from the following list: (if a student has not already successfully completed them or their equivalent)
| Course | Title | Credits |
|---|---|---|
| BIOS 601 | Epidemiology: Introduction and Statistical Models. | 4 |
Epidemiology: Introduction and Statistical Models. Terms offered: this course is not currently offered. 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. | ||
| BIOS 602 | Epidemiology: Regression Models. | 4 |
Epidemiology: Regression Models. Terms offered: this course is not currently offered. 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. | ||
| BIOS 624 | Data Analysis and Report Writing. | 4 |
Data Analysis and Report Writing. Terms offered: this course is not currently offered. Common data-analytic problems. Practical approaches to complex data. Graphical and tabular presentation of results. Writing reports for scientific journals, research collaborators, consulting clients. | ||
| MATH 523 | Generalized Linear Models. | 4 |
Generalized Linear Models. Terms offered: this course is not currently offered. 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. | ||
| MATH 533 | Regression and Analysis of Variance. | 4 |
Regression and Analysis of Variance. Terms offered: this course is not currently offered. 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. | ||
| MATH 556 | Mathematical Statistics 1. | 4 |
Mathematical Statistics 1. Terms offered: this course is not currently offered. 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. | ||
| MATH 557 | Mathematical Statistics 2. | 4 |
Mathematical Statistics 2. Terms offered: this course is not currently offered. Sufficiency, minimal and complete sufficiency, ancillarity. Fisher and Kullback-Leibler information. Elements of decision theory. Theory of estimation and hypothesis testing from the Bayesian and frequentist perspective. Elements of asymptotic statistics including large-sample behaviour of maximum likelihood estimators, likelihood-ratio tests, and chi-squared goodness-of-fit tests. | ||
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).