Graduates from the Biostatistics Division will be well suited to work as independent researchers within a university setting, and to take a leadership or supervisory role in university research institutes, government departments, hospitals, pharmaceutical/health corporations, and other health agencies such as cancer research units.
Applicants are expected to hold a Master’s degree in biostatistics, statistics, biology, computer science or economics, as well as undergraduate or graduate courses in linear algebra, advanced calculus, probability and mathematical statistics.
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Course Requirements (5.0 FCE)
- All students are required to take the courses listed below. Those who have completed similar courses at the Master’s level, and have achieved at least an A-, may be given an exemption, but would be required to replace these courses with electives.
- Students who have taken their MSc in this department, may have taken some or all of these courses already. In this case their program of study will be designed with consultation of the program director at the time of admission.
Required Courses (4.5 FCE)
|CHL5005H: Public Health Research||0.5|
|CHL5208Y: Advanced Laboratory in Statistical Design and Analysis||1.0|
|CHL5209H: Survival Analysis I||0.5|
|CHL5210H: Categorical Data Analysis||0.5|
|CHL5250H: Biostatistics Seminars||0.5|
|CHL5260H: Doctoral Seminar Series in Biostatistics*||0.5|
|STA2112H: Mathematical Statistics I||0.5|
|STA2212H: Mathematical Statistics II||0.5|
Elective Courses (0.5 FCE)
Electives may be taken from other departments such as the Department of Statistical Sciences, Department of Computer Science or other University departments, with permission of the Division Head.
* Students may Achieve Candidacy prior to completion of CHL5260H
PhD students are required to attempt the qualifying exam within the first year of entering the program. The examination, which is usually offered in the late summer, involves both theoretical and practical components, divided into three parts. The theoretical component comprises two in-class exams of 5 hours each, the first (Part I) covering foundations such as probability and mathematical statistics, and the second (Part II) covering biostatistical methodology. The format for the practical component (Part III) is a take-home exam, where the student is given one week to submit a report which summarizes the statistical analysis of at least one dataset.