In addition to the information on this page, please review the College of Graduate and Postdoctoral Studies Collaborative Program in Biostatistics Program page before you apply to the program.

MSc in Biostatistics

The program prepares MSc graduates for positions that require expertise in data management, study design, and statistical modelling.

After completing the program, graduates will be able to:

• Demonstrate proficiency in statistical theory
• Apply statistical theory to problems in the medical, biological, and agricultural sciences
• Collaborate on research teams
• Address problems involved in the collection and management of data
• Participate actively in the drawing of conclusions from data analysis and in the presentation and writing of research reports and papers.

PhD in Biostatistics

The program prepares PhD graduates to assume faculty or other research-oriented positions in academic institutions or to take leadership positions in organizations that conduct health-related research.

After completing the program, graduates will be able to:

• Develop new statistical methods using existing theory or apply existing statistical methods to address problems in the medical, biological, and agricultural sciences
• Collaborate on research teams
• Take a leadership role in study design, data management, statistical analysis, and interpretation of study results
• Be proficient in both the oral and written presentation of research results.

• Download Collaborative Biostatistics Program Factsheet
• Student Handbook

MSc

• A four-year BSc or BA degree from a recognized university in one of the following disciplines: mathematics, statistics, applied statistics, biostatistics, quantitative psychology, or any other discipline with a strong background in mathematics or statistics
• A cumulative weighted average of at least 75% (U of S grade system equivalent) in the last two years of study (e.g. 60 credit units)
• Proof of English language proficiency may be required for international applicants and for applicants whose first language is not English

PhD

• Thesis-based M.Sc. in a relevant program - mathematics, statistics, applied statistics, biostatistics, quantitative psychology, or another discipline with a strong background in mathematics or statistics
• A cumulative weighted average of at least 75% (U of S grade system equivalent) in the last two years of study (e.g. 60 credit units)
• Proof of English language proficiency may be required for international applicants and for applicants whose first language is not English

To be eligible for admission as a PhD student, an applicant must have taken the following courses (or equivalents) during his/her MSc program: CHEP 800.3 (Epidemiology I), STAT 850.3 (Mathematical Statistics and Inference), and PUBH 842.3 (Current Topics in Biostatistics and Statistical Applications).

Thesis or project based program

Graduate students in a thesis or project based program pay tuition three times a year for as long as they are enrolled in their program. https://grad.usask.ca/programs/biostatistics.php#Tuitionandfunding

Submit an online application

• Apply now

The Biostatistics Program is a collaborative venture of the School of Public Health, Department of Community Health and Epidemiology and the Department of Mathematics and Statistics. The core faculty involved in this graduate program are identified below:

Faculty	Department/College	Areas of Research Interests
Shahed Khan Program Director	Department of Mathematics and Statistics, College of Arts and Science	Modeling changepoint data; Longitudinal data analysis; Bayesian inference and Markov Chain Monte Carlo; Survival analysis
Punam Pahwa	Department of Community Health and Epidemiology, College of Medicine	Applications of longitudinal data analysis techniques to various diseases/health conditions (focus on respiratory diseases, cancer, and farm injuries) related to rural occupational and environmental exposures; Analysis of longitudinal complex survey data analysis; Missing data; Analysis of quality of life data
William Laverty	Department of Mathematics and Statistics, College of Arts and Science	Multivariate statistics; Time series analysis; Experimental and sampling design; Spatial statistics; Computer security
Longhai Li	Department of Mathematics and Statistics, College of Arts and Science	Classification and regression with high-dimensional measurements; Detecting differential variables from high-throughput data; Classification and regression with high-order interactions; Modelling DNA sequences
Hyun Lim	Department of Community Health and Epidemiology, College of Medicine	Statistical methods for longitudinal data; Recurrent event models in survival analysis; Design and analysis of clinical trials; Epidemiologic studies; HIV/AIDS treatment and prevention studies
Juxin Liu	Department of Mathematics and Statistics, College of Arts and Science	Model misspecification; Measurement error and misclassification; Interaction models; Markov Chain Monte Carlo algorithms; Missing data in longitudinal analysis, interplay between Bayesian and frequentist theories
Chris Soteros	Department of Mathematics and Statistics, College of Arts and Science	Statistical mechanics; Computer simulation; Combinatorics and bioinformatics
Raj Srinivasan	Department of Mathematics and Statistics, College of Arts and Science	Applied probability; Queueing theory; Queueing networks
Michael Szafron	Assistant Professor, School of Public Health	Monte Carlo simulations; Statistical Methods associated with Monte Carlo data; Computational biomodelling of the structure/function of biopolymers; Statistical Methods for the health sciences including survey data

For further information on the Biostatistics program, please contact:

thesis.admissions@usask.ca