The required credit and non-credit courses for the PhD degree in the collaborative Biostatistics Program are:
CHEP 806.3 - Applied Statistical Methods for Follow-up Data - Explores the application of advanced multivariate statistical methods which are commonly used in life sciences and is an extension and continuation of CHEP 805. Topics covered are: general approaches for longitudinal data analysis, which include analysis of repeated measures using analysis of variance, survival analysis, statistical methods based on generalized estimating equations and maximum likelihood theory; and brief introduction to handling missing data. Computer software used: SPSS and SAS. Prerequisite(s): CHEP 805 or equivalent.
STAT 848.3 - Multivariate Data Analysis - A survey of methods for analyzing discrete and continuous multivariate data. Includes: log-linear models, logistic regression, canonical correlation, discriminant analysis, cluster analysis, multivariate analysis of variance (MANOVA), and factor analysis.
STAT 841.3 - Probability Theory - Probability spaces and random variables. Distribution functions. Convergence of random variables. Characteristic functions. Fundamental limit theorems. Conditional expectations.
BIOS 990.0 - Seminar in Biostatistics - The seminar in Biostatistics is a required non-credit course for all MSc and PhD students.
GSR 960.0 - Introduction to Ethics and Integrity - Is a required course for all first year graduate students at the University of Saskatchewan. The purpose of this course is to discuss ethical issues that graduate students may face during their time at the University. All students will complete modules dealing with integrity and scholarship, graduate student-supervisor relationships, conflict of interest, conflict resolution and intellectual property and credit.
CHEP 810.3 - Advanced Topics in Clinical Trials - Explore methods and issues in the design, conduct and analysis of clinical trials, focusing on Phase II and III trials. Topics covered include: patient selection, treatment allocation, randomization techniques, endpoint definition, protocol development, sample size calculation, p-values, quality data collection, intent-to-treat analysis, analysis of compliance data, equivalency testing, surrogate endpoints, multiple comparisons, sequential testing, interim analysis, data analysis procedures and interpretation of results.
STAT 834.3 - Advanced Experimental Design - Review of the linear model; Randomization theory; Randomized blocks and Latin Squares; Factorial treatment structure; Calculus of factors; Incomplete block designs; Fractional factorials; Response surface designs; Optimal designs.
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