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We invite you to check out these Term 2 Courses that have space available!

SPPH  513: Clinical Epidemiology


An overview of major themes in clinical epidemiology, comprising knowledge and skills that allow one to formulate management recommendations: these inform one’s fellow practitioners of possible ways to translate evidence to action. Critical appraisal of the literature, clinical approaches to evidence about diagnosis, therapy, prognosis etc, and the current frameworks for formulation of management recommendations are parts of this discipline. Our aim is to prepare you to contribute effectively to authorship of management recommendations.
Pre-requisite(s):  One of SPPH 400 or SPPH 502


Tuesdays: 2:00 pm to 5:00 pm

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SPPH 549: Advanced Economic Evaluation in Health Care

Taught by Dr. Stirling Bryan from the School of Population and Public Health and Dr. Mohsen Sadatsafavi from the Faculty of Pharmaceutical Sciences, this course provides an in-depth exploration into the economic evaluation of health technologies. Students will gain hands-on experience in conducting model-based economic evaluations, understanding the policy context, and engaging in current methodological debates. The course is designed for those aiming to perform ‘production level’ economic evaluations for publication or reporting to stakeholders. Through a combination of lectures, practical modeling sessions, and individual projects, students will develop key skills and understanding in applied techniques, policy context, and theoretical considerations. The course requires foundational knowledge of economic evaluation, statistics, epidemiology, and health economics.

Wednesdays: 9:00 am to 12 noon

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SPPH 581C: Methods for Analyzing Routinely Collected Data


This is a data-driven course that focuses on applying supervised and unsupervised machine learning methods and non-standard analytic problems with healthcare data. The data that will serve as the motivation will be large clinical and administrative databases commonly used in health services research in Canada, such as hospital discharge data. Students in this course will be exposed to, and apply, advanced statistical methods for analyzing sophisticated healthcare-based data problems.
Drawing from machine learning applications and traditional statistical methods, students will encounter topics including: Cluster Analysis
Principal Components
Missing Data Problems
Classification and Regression Trees
Mixture Models
Spline Regression Models
Additive Models

  • Acquisition of methods will be based on problem-based learning. During the course, students will be introduced to new analytic concepts, progress through the principles of advanced methods, learn the adjuvant analytic techniques with software tools (SASR), identify resources to assist with the development of their skills, and synthesize their learnings by applying the methods to observational datasets, interpreting their results and sharing their findings with their peers.
    Progressing through the statistical methods, students will acquire methods for data manipulation, learn coding in SAS, data cleaning, summarizing data, preparing brief reports and presenting findings to peers. SAS will be used for at least one-half of the programming; be prepared to learn SAS.
    Pre-requisite(s):  Permission of the instructor is required to register for the course.

Mondays: 9:00  am to 12:00 noon

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