Course Descriptions – MSc/PhD CEHCR

Enrolment Policies

Due to the high demand for Clinical Epidemiology courses, the following enrolment policies and procedures will apply:

  • Priority will be given to MSc or PhD students registered in the Clinical Epidemiology and Health Care Research concentration, followed by other students in the Institute of Health Policy, Management and Evaluation.
  • Only if there is space permitting, students from other graduate degree programs at the University of Toronto may be admitted into a course.
  • Non-IHPME students must submit an SGS add/drop form to the CEHCR program assistant.
  • In most cases, non-IHPME students will be limited to two IHPME courses.


HAD5301H: Introduction to Clinical Epidemiology and Health Care Research
HAD5302H: Measurement in Clinical Research
HAD5303H: Controlled Clinical Trials
HAD5304H: Clinical Decision Making and Cost Effectiveness
HAD5305H: Evidence-Based Guidelines
HAD5306H: Introduction to Health Services Research Methods and the Use of Health Administrative Data
HAD5307H: Introduction to Applied Biostatistics
HAD5308H: Evidence Synthesis: Systematic Reviews and Meta-Analysis
HAD5309H: Observational Studies: Theory, Design and Methods
HAD5310H: Pragmatic Issues in Conduct of Controlled Trials
HAD5311H: Clinical Epidemiology and Health Care Research Comprehensive Course
HAD5312H: Decision Modeling for Clinical Policy and Economic Evaluation
HAD5313H: Advanced Design and Analysis Issues in Clinical Trials
HAD5314H: Applied Bayesian Methods in Clinical Epidemiology and Health Care Research
HAD5315H: Advanced Topics In Measurement
HAD5316H: Biostatistics II: Advanced Techniques in Applied Regression Methods
HAD6360H: Research Internship
HAD7002H-S: Writing Mentorship
HAD7002H-F1: Biostatistics III: Advanced Biostatistical Techniques for Observational Studies
HAD7002H-F2: Advanced Evidence Synthesis: Meta-analysis, Meta-regression, Network Meta-analysis Individual patient data meta-analysis
HAD7002H-S2: Philosophical Perspectives in Epidemiology


Course Number HAD5301H
Course Name Introduction to Clinical Epidemiology and Health Care Research
Prerequisite None
Delivery Format Summer – 2.5 hour session twice a week, 1/2 lecture/ 1/2 tutorial (offered to IHPME students in the Clinical Epidemiology concentration only).
Winter – 2.5 hour session once a week, 1/2 lecture/ 1/2 tutorial (open to non-Clinical Epidemiology IHPME students, students from outside IHPME with clinical knowledge or training, and auditing students).Those interested in auditing the course in the Winter term must submit a letter of intent and a letter of approval from their supervisor by December 1 to
Semester Offered Summer (reserved only for CEHCR students) and Winter
Instructors Winnie Seto
Nicole Look Hong
Note:It is not recommended that students who are considering applying to the Clinical Epidemiology program audit the course. Please contact the Program Assistant ( for more information.
Description:To introduce principles of epidemiology as applied to clinical research, emphasizing diagnosis, prognosis, treatment, the measurement of signs and symptoms of health and disease, and the evaluation of diagnostic, treatment and compliance-improving maneuvers.

  1. To introduce the clinical epidemiology program and the courses offered
  2. To develop an approach for addressing health research questions using appropriate research methods
  3. To introduce the types of research designs used in clinical and epidemiologic research, including those using primary and secondary sources of data
  4. To understand the threats to the validity of different study designs, and to become familiar with the methods used to enhance the validity of clinical research
  5. To be able to critically appraise a biomedical research article
  6. To be able to write a clinical research protocol

Class participation 5%
Interim assignment 35%
Final assignment 60%



Course Number HAD5302H
Course Name Measurement in Clinical Research
Prerequisite Minimum one half course in research methods (e.g. HAD5301H)
One half course in biostatistics (HAD 5307H) or (CHL 5201H) or (MSC 1090H)
Delivery Format Once a week
Semester Offered Winter
Instructors Cory Borkhoff
Zahi Touma
Description:The ultimate goal of good measurement is to generate a numeric score that has meaning so that we can use it to represent a given concept (depression, health, disease activity)  in our statistical analyses in a given population. Measurement is like the “basic science” of clinical epidemiology and impact on our measurement of causal, prognostic and outcome variables. The purpose of this course is to learn principles of measurement (good scale development, clinical usefulness, validity and reliability) so that they can be applied to the critical appraisal of a given instrument when a measurement need is defined.In the course we will help you define a particular measurement need – what do you need to measure, in whom, and why? – and from that move to the appraisal of a scale of your choice to see if it would be appropriate for that application.Students taking this course will focus on measures that are based on expertise, clinical judgment, experience, or the subjective perceptions of either the providers or consumers of health care.  These might include clinimetric indices which are aggregated scores across various domains – such as disease activity indices, or prognostic indices; or more psychometric scales where there are multiple items to tap a single concept like depression, health, performance or function. Measures that are single items, or which are uncontested or irrefutable gold standards of truth would not be good selections for work in this course. The classes are split into two:  lecture (instructors or guest lecturer) and student led presentations/seminars.  Tutorials are offered in the hour preceding the course on certain topics.
Objectives:The students will work through the principles of measurement, and at each stage reflect on this for their chosen measurement instrument and need.  The assignment is best done as the course progresses.  By the end of the course, students are to apply measurement principles and methods in the critical assessment and development of measures employed in clinical and epidemiological research.  Many of our students have published their final assignments.
Evaluation:The final mark is composed of:• 15% – First Assignment  (Introduction of measurement need) • 20% – Second Assignment  (Critique of development and practical usefulness of the scale/index) • 45% – Final Assignment  (Critical appraisal of development, practical usefulness, validity, reliability and responsiveness of the chosen scale/index, final decision as to whether you can use it). • 10% – Presentation (in class presentation related to the topic for that day) • 10% – Participation



Course Number HAD5303H
Course Name Controlled Clinical Trials
Prerequisite HAD5301H – Introduction to Clinical Epidemiology and Health Care Research
Delivery Format Once a week for 2.5 hours over 12 weeks
Semester Offered Fall
Instructor Niall Ferguson
Bruno da Costa
Description: Students are provided with weekly readings from textbooks and from the original literature. Each session consists of a 1-hour didactic lecture providing an overview of the subject matter of the particular week’s topic followed by a small group tutorial during which time students will develop their protocols with the assistance of tutors. Students are expected to develop their own controlled clinical trial proposal throughout the term. These proposals will serve as the focal points for the discussions during the tutorial sessions. At the completion of the course students will have completed a fully developed proposal which will be presented at an oral presentation and submitted as a final protocol.
Objectives: This introductory course is designed to provide the student with necessary background and tools for the design and conduct of a 2-arm parallel group controlled clinical trials. It is geared for the individual who wishes to pursue a career as an independent investigator and clinical trialist but will be of interest in others who wish to be involved in clinical trial in other capacities. Students should prepare for the course by developing a research question that can be addressed using randomized controlled trial design and starting their literature review. Students are encouraged to design a 2-arm parallel group superiority trial; students should contact the course instructors if considering a different design. As this is an introductory course, students seeking advanced design may consider HAD5313H: Advanced Design and Analysis Issues in Clinical Trials after its completion. In order to assist the coordinators in assigning small group tutorials, please inform the Tutorial Assistant of your clinical specialty.

Written 2-page research question 10%
Final written research protocol 50%
Oral presentation 10%
Written student protocol review 10%
Course participation 20%



Course Number HAD5304H
Course Name Clinical Decision Making and Cost Effectiveness
Prerequisite HAD5301H – Introduction to Clinical Epidemiology and Health Care Research
HAD5307H – Introduction to Applied Biostatistics (may be taken concurrently)
Delivery Format Once a week, 2 hours separate private tutorials – 5 hours per student Total hours = 31
Semester Offered Fall
Instructor Beate Sander
Description: This course will provide an introduction to the principles and applications of decision sciences as they relate to clinical decision-making. The major themes will be a method of evaluating diagnostic and therapeutic strategies in order to optimize individualized patient care and inform policy decision, including those in which a fixed amount of resources are an important consideration. The basic building blocks of decision analysis (Bayes theorem, test and test-treatment thresholds, tree building, utility measurement, Markov processes and cost-effectiveness) will be reviewed and synthesised. Students will use decision analysis software to build and test their own decision analyses.

  1. To learn the principles of decision analyis
  2. To learn how to use decision analysis software
  3. To perfom a decision analysis by developing a model, gathering the relevant data, and perfoming complete sensitivity analyses
  4. To learn how to present a decision analysis orally and in writing

Mid-term assignment 20%
Oral presentation 40%
Written Paper 40%



Course Number HAD5305H
Course Name Evidence-Based Guidelines
Prerequisite HAD5301H – Introduction to Clinical Epidemiology  OR equivalent
HAD5303H – Controlled Clinical Trials
Recommended: HAD5308H – Evidence Synthesis: Systematic Reviews and Meta-Analysis
Delivery Format One 2 hour session per week, seminar, interactive & project-based
Semester Offered Spring
Instructors Samir Gupta
Natasha Leighl
Description: Each student will select a guideline topic applicable to their field and apply principles learned during seminars to the development of the guideline. During the latter part of the course, participants will present their guideline to classmates to experience the consensus development phase of the course.

  1. To understand the characteristics of high-quality guidelines
  2. To be able to develop an analytic framework to guide evidence extraction and synthesis
  3. To discuss criteria for grading quality of evidence wrt diagnostic tests and interventions
  4. To understand strength of recommendations
  5. To develop skills in forming recommendations based on strength of evidence

Participation 20%
Analytic framework 20%
Evidence tables 20%
Final paper 40%



Course Number HAD5306H
Course Name Introduction to Health Services Research Methods and the Use of Health Administrative Data
Prerequisite HAD5301H – Introduction to Clinical Epidemiology and Health Care Research OR equivalent
HAD5307H – Introduction to Applied Biostatistics OR equivalent
Delivery Format Twice a week: 2 hour lecture & 2 hour tutorial
Semester Offered Spring
Instructors Antoine (Tony) Eskander
Jessica Widdifield

NOTE: This course is limited to 21 students. Priority is in the following order: 1) IHPME Clinical Epidemiology, 2) IHPME Other Programs, 3) DLSPH Other Programs, 4) UofT Other Programs, 5) Others. The waiting list starts accruing each July.

Description: An introduction to research methods for evaluating the outcomes and effectiveness of health care services using secondary data (with an emphasis on administrative databases). These methodologies are used to answer questions about which treatments, services and policies are effective when applied to whole populations in real practice and policy settings. In this course students will learn not only about the use of secondary data for research purposes, but also how to apply and think about these research findings in the context of the current health care system. This will include the strengths and weaknesses of secondary databases, data accuracy, bias and risk adjustment, study design and a variety of analytical tools. The course will have a strong focus on sources of secondary data available in Ontario.

  1. To recognize the diversity of research questions, data sources and methodologies that are applied in health services research using secondary data.
  2. To identify key study design & analysis considerations when using secondary data sources for health services research.
  3. To understand the importance of a critical appraisal approach in reviewing and interpreting health services research.
  4. To develop basic skills and knowledge for carrying out health services research with secondary data:
    a. develop a research question for a health care issue,
    b. assess data validity,
    c. select an appropriate study design, and
    d. plan appropriate statistical analyses.
  5. To further develop written and oral communication skills for use in planning, reviewing and disseminating health services research.

Assignment #1: Written Assignment 20%
Assignment #2: Written Assignment (Dataset Creation Plan) 20%
Assignment #3: Written Assignment (Critical Appraisal) 20%
Student Presentation 30%
Participation 10%



Course Number HAD5307H
Course Name Introduction to Applied Biostatistics
Prerequisite SAS training
Delivery Format Twice a week: 2 hour lecture & 2 hour tutorials
Semester Offered Fall
Instructors Alexander Kiss
Description:This course is designed to give clinical epidemiology students’ knowledge and skills in statistical methods that apply to clinical epidemiology.  Students will acquire working experience in applying these methods to datasets, analysing epidemiological data, and interpreting findings. As well, students will develop statistical writing skills and learn how to present results to assist them with future research publications.
For each statistical method, this course will be focused in teaching:  “what is it” and “how to do it”.  Topics covered in this course include: data types, measures of central tendency, measures of variability, testing for the difference between two groups (analysis of means, rates and proportions), constructing 95% confidence intervals, nonparametric analyses sample size and study power estimation, testing for trend, analysis of variance, analysis of covariance, simple and multiple linear regression,  logistic regression, survival analysis-life table and Kaplan-Meier curves, log-rank tests and Cox proportional hazards models. The final part of the course focuses on how to build a good multivariable model by assessing details such as the number of variables allowed and statistical fit. Computing is also part of this course.  Knowledge in SAS or other equivalent statistical packages (such as SPSS, STATA, MINITAB etc.) is a prerequisite of the course.  Students are recommended to get training in a statistical packages (SAS) prior to taking this course.

  1. To learn about data types, measures of central tendency, 95% confidence intervals, measures of variability, and both parametric and nonparametric tests of differences between two groups.
  2. To learn how to compare three or more groups usings tests such as analysis of variance and analysis of covariance.
  3. To learn how to calculate sample size and statistical power for a study.
  4. To carry out multiple linear regression, logistic regression, and survival analysis.
  5. How to build a good multivariable model by assessing details such as the number of variables allowed and statistical fit.
  6. To use SAS statistical package for data analysis.

Weekly Assignment 20%
Mid-Term Project 35%
Final Assignment 45%



Course Number HAD5308H
Course Name Evidence Synthesis: Systematic Reviews and Meta-Analysis
Prerequisite HAD5301H – Introduction to Clinical Epidemiology and Health Care Research
HAD5307H – Introduction to Applied Biostatistics
Delivery Format One 2 hour lecture per week
Semester Offered Winter
Instructors Vibhuti Shah
Neill Adhikari
Description: This course is designed to instruct healthcare professionals, who have some background in critical appraisal of the literature and study design, how to systematically review available evidence either from randomized controlled trials, observational studies or diagnostic tests. The course will also cover the aspect of appropriate summarizing of the evidence using statistical techniques.
Objectives: The primary objective of this course is for the participant to conduct a systematic review of a health care intervention that will be acceptable for publication within the Cochrane Collaboration or in a peer-reviewed journal. The course will focus on systematic review/meta-analysis of randomized controlled trials, but review of cohort studies is also acceptable. A secondary objective is to develop scientific writing skills.
Evaluation: Students will be evaluated on in-course assignments (protocol – 25%) and the completion of a systematic review on an appropriate topic of their choice (systematic review – 65%) and class participation and attendance (10%). It is expected that the students will publish their reviews in the Cochrane Library and/or a peer reviewed journal.Prior to the first session please identify the proposed topic of your review and search the literature to identify any published reviews that might overlap. Download the Cochrane Handbook from the Cochrane Centre in Hamilton. If you are planning to perform a systematic review of randomized controlled trials we encourage you to contact the Cochrane Review group that you think your review would come under the scope of and “claim the right to your title”. Bring this information to the first session.



Course Number HAD5309H
Course Name Observational Studies: Theory, Design and Methods
Prerequisite HAD5301H – Introduction to Clinical Epidemiology and Health Care Research
HAD5307H – Introduction to Applied Biostatistics OR CHL5201H – Biostatistics I
Delivery Format lectures, group discussions, and class presentations
Semester Offered Spring
Instructors Joseph Kim
Bruce Perkins
Eddy Fan
Jefferson Wilson
Description: This course covers conceptual, design and methodological issues related to research using observational methods. Prerequisites include an understanding of basic research design and statistics including regression techniques. Knowledge and experience with clinical patient care as well as familiarity with existing data sources are an asset but are not prerequisites for the course. The format of the course includes lectures, group discussions, and class presentations along with individual feedback and mentoring sessions with the instructors. The 12-session course is divided into three different blocks. The first four sessions will deal with conceptual and theoretical issues related to causality and bias in observational research. The next three sessions will deal with design issues in observational research. The final five sessions will address specific methodological topics in observational research.
Objectives:Objective 1:  To provide the students with an overview of current conceptual and theoretical issues related to causality and bias in observational research.Performance Expectation: At the end of the course the students should understand theories of causality and be able to apply those theories and ideas to their research.Objective 2:  To provide the students with an overview of the design options for observational research. Performance Expectation: At the end of the course the students will understand the available design options and be able to assess observational study designs and to conduct research using different designs. Objective 3: To provide students with an overview of current topics in key methods used in observational research. Performance Expectation: At the end of the course the students will understand different methods that can be used in observational research and will be able to assess the methods used in observational research and conduct research using different methods.

Assignment 1 25%
Assignment 2: presentation 30%
Assignment 3: final paper 45%



Course Number HAD5310H
Course Name Pragmatic Issues in Conduct of Controlled Trials
Prerequisite HAD5303H – Controlled Clinical Trials
HAD5305H or HAD5308H are recommended
Must have completed or near completed an RCT proposal that has been or will be submitted for ethics approval and funding.
Delivery Format Once a week, 1.5 hours
Semester Offered Winter (alternating years – next offered Winter 2021)
Instructors Brian Cuthbertson
Description: Each session will be devoted to common issues or concerns that arise during the conduct of an RCT.  You will be expected to consider each issue in the context of your own clinical trial and to develop a written strategy to address the issue. You should be prepared to discuss the strategies you developed at each session.  Individual strategies can be developed by review of the pertinent published literature (a list of suggested references accompanies the assignments for each session), as well as relying on your own RCT experiences.  Each session related strategy should be no more than one page in length.  Course coordinators will collect the assignments on two occasions: mid term and at the end of the course.  These assignments will be used, in combination with your class participation, for your evaluation.Sessions will be moderated by one of the two course coordinators and a content expert.  In addition, one student will be responsible for moderating each session.  The responsibility of the moderator is to encourage discussion among your colleagues.
Objectives: The aim of this course is to equip the student with strategies to deal with common issues that arise in the conduct of randomized controlled trials.  To meet this aim the student will be required to have already developed a protocol for a randomized controlled trial.
Evaluation: Attendance and participation at each session: 20% Mid term assignment: 40% (10% of which is completion of REB submission) Final assignment: 40% (10% of which is your review of REB submission)



Course Number HAD5311H
Course Name Clinical Epidemiology and Health Care Research Comprehensive Course
Prerequisite Attendance begins once PhD transfer is approved.
Delivery Format Synthesis component: Four 1/2 day sessions per year held in the Fall, Total hours = 16
Semester Offered September – June (continuous course)
Instructor Various Clinical Epidemiology Faculty

  • Comprehensives component: A methodologic topic will be chosen by the PhD student together with her/his supervisor and committee members. An appropriate reading list will be developed by the student, and approved by the supervisor / committee. The student will be expected to conduct a thorough review of the literature on the chosen topic, and prepare a summary of the material (10-pages, written, single spaced – ideally appropriate for publication in a peer-review journal). The summary will be presented orally by the student to her/his supervisor / committee (20 minute presentation, similar to a thesis defense) followed by a 30-40 minute question and answer period during which the supervisor / committee will determine whether the student has a clear understanding of the material presented, and has developed a degree of expertise in the area.
  • Synthesis component: PhD students will attend four 1/2 day seminars, for a total of 16 hours. Each seminar will be led by a recognized leader in the field of clinical epidemiology, who will focus the discussion on the history and philosophy of the area of research of focus within clinical epidemiology, and/or perform informal “mentoring” of the students about developing a successful clinical research career.
Objectives: Our expectation of a successful PhD in clinical epidemiology is that she/he will have sufficient breadth and depth of knowledge in their chosen field of clinical research – sufficient to be considered an expert in this field. This implies a thorough understanding not only of the research methods (which is the focus of the majority of the PhD course work), but also of the theoretical underpinnings of these methods. The intent is for the Comprehensives / Synthesis course outlined here to ensure the latter. In addition, through the Synthesis component, we hope the students will have a good understanding of the history / evolution, and philosophical principles underlying, the field of clinical epidemiology.
Evaluation: Evaluation is based on 3 components of the course, all of which must be completed with a passing grade.

  1. Attendance at all Synthesis sessions, with active participation in the discussion.
  2. Written Comprehensive paper proposal; and, a final paper that is in a format ready for peer review in a scholarly venue.
  3. Oral presentation of the Comprehensive project, with acceptable responses to committee questions regarding the


Course Number HAD5312H
Course Name Decision Modelling for Clinical Policy and Economic Evaluation
Prerequisite HAD5304H – Clinical Decision Making and Cost Effectiveness
Delivery Format One session per week. Each session involves a didactic and a practical aspect.
Semester Offered Winter
Instructor David Naimark
Description: This course will overview the principles and applications of decision analytic modeling for the purposes of developing clinical policy (e.g. what’s the optimal screening method and interval for cervical cancer screening) and evaluating the efficiency (cost effectiveness/ cost utility) of health interventions. The course will involve both theoretical and practical aspects. Students will have an opportunity to read more deeply in the history and theoretical underpinnings of decision analysis. However, students will also be expected to learn practical skills in advanced modeling by constructing, debugging, and presenting their own complex decision model. Themes covered in the course will include: a brief history of decision analysis, descriptive and normative theories of decision making, measuring health outcomes with patient-derived and community weighted utility measures, using the QALY and it’s competitors, Markov modeling, Monte Carlo simulation, using mathematical functions in models, modeling for cost effectiveness analysis, and an introduction to Bayesian approaches in modeling.
  1. Understand the theoretical assumptions used in decision modeling.
  2. Develop advanced practical modeling skills.


Oral presentation 20%
Written paper 50%
Class participation and assignments 30%




Course Number HAD5313H
Course Name Advanced Design and Analysis Issues in Clinical Trials
Prerequisite HAD5303H – Controlled Clinical Trials
HAD5316H – Biostatistics II: Advanced Techniques in Applied Regression Methods
Delivery Format 2.5 hour seminar each week
Semester Offered Spring
Instructors George Tomlinson
Description: This course will overview issues identified by students conducting clinical trials. It is expected that this course will meet the individual needs of enrolled students.

  1. To identify individual needs of students conducting clinical trials
  2. To discuss certain topics such as: cluster randomization designs, cross-over designs, n-of-1 designs, group sequential and other adaptive designs, cost-effectiveness clinical trials, issues in sample size development, Bayesian trials, safety monitoring and interim analysis, composite endpoints, subgroup analysis.
  3. To identify readings related to this topic.
  4. To present the readings in a seminar format.
Evaluation: The final mark is composed of:

Class participation 10%
Weekly assignments 70%
Organizational of “in charge” weekly session 20%



Course Number HAD5314H
Course Name Applied Bayesian Methods in Clinical Epidemiology and Health Care Research
Prerequisite HAD5316H – Biostatistics II: Advanced Techniques in Applied Regression Methods, Some simple programming (e.g., SAS data step, R, S-Plus) – may be taken concurrently with course.
Delivery Format 3 hour weekly lecture
Semester Offered Winter
Instructor George Tomlinson

Description: This course will introduce students to Bayesian data analysis. After a thorough review of fundamental concepts in statistics and probability, an introduction will be given to the fundamentals of the Bayesian approach, including a look at how computer simulation can be used to solve statistical problems. Students will learn how to use the brms package in the R statistical software environment to carry out Bayesian analyses of data commonly seen in health sciences. Bayesian methods will be covered for binary, continuous and count outcomes in one and two samples, for logistic, linear and Poisson regression, and for meta-analysis.

Objectives: By the end of this course, students will:

  1. Understand what is meant by a “Bayesian Analysis” and how it differs from a typical analysis under the frequentist framework
  2. Understand the role and importance of Markov Chain Monte Carlo in modern Bayesian methods
  3. Understand how modern Bayesian models are fitted
  4. Be able to fit Bayesian models to common types of study designs and data types
  5. Know what aspects of the Bayesian analysis are an essential part of a statistical report
  6. Have worked through some case studies (in lectures, tutorials and as part of assignments)
  7. Have developed expertise in using the brms program within the R environment

Evaluation: 4 Individual Assignments each worth 25%

Textbook: 2nd edition of the book by Richard McElreath:



Course Number HAD5315H
Course Name Advanced Topics In Measurement
Prerequisite HAD5302H Measurement in Clinical Research
HAD5316H – Biostatistics II: Advanced Techniques in Applied Regression Methods
Delivery Format 2 hours per week combining lecture and seminars
Semester Offered Spring (alternating years – next offered Spring 2022)
Instructors Carolina Barnett-Tapia
Description: This course will cover topics in measurement theory and application beyond the basic principles covered in HAD5302H, Measurement in Clinical Research. Specifically, it will cover the theory, application and interpretation of more advanced approaches and statistical techniques such as confirmatory factor analysis, structural equation modeling, item response theory approaches, measurement error, minimally clinically important differences, response shift, conjoint analysis, discrete choice experiments and the mapping of measures to utility functions as they apply to measurement theory. The course mainly will be structured such that the first week will provide the theory and with the subsequent week(s) providing discussion of study design issues and interpretation of data output. Students will not be analyzing data.
Objectives: The intent of the course is that students will understand the theory of the approach such that they can consider when the application is appropriate to use and critique work published work from a methodological and interpretive perspective.
Evaluation: Students will be responsible for writing a structured synopsis/review of maximum two pages on four topics, each worth 25% of the final grade. The review topics include: 1) confirmatory factor analysis/SEM; 2) measurement error and minimally clinically important differences; 3) item response theory approaches or conjoint analysis; and, 4) response shift. The articles to be reviewed will be provided to the student two weeks prior to the review due date.



Course Number HAD5316H
Course Name Biostatistics II: Advanced Techniques in Applied Regression Methods
Prerequisite HAD5307H – Introduction to Applied Biostatistics
Delivery Format Weekly lecture and tutorial
Semester Offered Winter
Instructor J. Charles Victor
Objectives:At the end of the course, the student will be able to develop a complex analysis plan to answer a clinical research question, to carry out the analyses using the statistical package SAS, to verify the appropriateness of the analyses based on the findings, and to report and interpret the results. In particular, the student will be able to:i) understand the purpose of regression analysis, and be able to differentiate between various forms of regression including linear, logistic, poisson, and Cox-proportional hazards regressionii) understand the requirements for each regression method and be able to adjust the methods to account for or examine: – clustering within data structure/sampling frame – hierarchical structures within data – repeated-measures and longitudinal data iii) be able to evaluate the validity of the results from each type of regression based on statistical criteria iv) understand different methods for variable selection in regression models v) be able to interpret and present the results from each type of regression model in a manner that is meaningful to clinicians and applied health scientists
 Evaluation: Homework assignments  30% – Group assignments involving data analysis, reporting, and interpretationMid-Term assignment  30% – Individual, take-home assignment in which students will be required to obtain data, develop a research question provide a preliminary analysis plan, and provide preliminary statistics and a written reportFinal assignment   40% – Individual, take-home assignment in which students will complete their analytic plan from the Mid-Term assignement and provide a complete written report in manuscript format


Course Number HAD7002H-S
Course Name Writing Mentorship
Prerequisite n/a
Delivery Format Weekly seminar
Semester Offered Winter
Instructor Allan Detsky
Aaron Drucker
Objectives: The course objective is to teach students to write for medical and healthcare journals.  Students will learn how to frame a paper, how to write clearly, how to prepare the tables and figures, how to succinctly discuss the results and how to deal with peer and editorial review.  Each student is required to bring a topic to pursue as manuscript during the time period of the course.  The weekly sessions will consist of a class discussion of the manuscript in preparation with specific feedback from the instructor.  This will require each student to continuously write and edit their papers throughout this course.  Students planning to publish their research will benefit the most, if their data has already been analysed and is ready for presentation.  Students may also wish to write papers that have no new data (e.g. commentaries, editorials, reviews).  Students who already have theses prepared are encouraged to use that work to convert into peer review papers.  By the end of the course, students will have a manuscript that is suitable for submission to a journal.

In class participation 15%
Draft of paper due at week 5 25%
Final paper 60%


Course Number HAD7002H-F1
Course Name Biostatistics III: Advanced Biostatistical Techniques for Observational Studies
Prerequisite HAD5301H: Introduction to Clinical Epidemiology and Health Care Research, or equivalent
HAD5307H: Introduction to Applied Biostatistics, or equivalent
HAD5316H: Biostatistics II: Advanced Techniques in Applied Regression Methods, or equivalentRecommended prior courses:
HAD5309H: Observational Studies: Theory, Design and Methods
Delivery Format Weekly seminar plus tutorial
Semester Offered Fall
Instructor Bettina Hansen

Objectives: At the end of the course, the student will be able to understand and apply more advanced biostatistical techniques in the setting of complex observational studies. The student will be able to design and develop a statistical analysis plan, to carry out basic analysis using the statistical package SAS, to verify the findings and to interpret and report the results in a manner that is meaningful to clinicians and applied health scientists.  The course will be given from an applied point of view; more theoretical understanding will be touched upon, but only to the extent of useful in applications and for understanding the models. This course will cover 3 advanced topics:

BLOCK I. Complex Survival Models: Aim is to understand and analyse multivariate survival data when

  • subjects transit between multiple states; including time-dependent covariates
    recurrent episodes of disease
  • time-to-failure of two “linked subjects” (e.g. kidneys, hips, twins)

BLOCK II. Longitudinal Models and Multi Level Models: Aim is to understand and analyse linear and generalized linear models in the presence of

  • longitudinal and repeated measurements
  • hierarchical structured, nested or clustered data

BLOCK III. Prediction Models: Aim is to design a prediction model in the setting of binomial outcome and survival data. The process includes

  • building a model for prediction purpose
  • applying validation tools
  • developing a user friendly tool /calculator to be used by clinicians in a day-to-day practice

Class Participation 10%
BLOCK I Assignment in Complex Survival Models 30%
BLOCK II Assignment in Longitudinal Models and Multi Level Models 30%
BLOCK III Assignment in Prediction Models 30%



Course Number HAD7002H-F2
Course Name Advanced Evidence Synthesis: Meta-analysis, Meta-regression, Network Meta-analysis Individual patient data meta-analysis
Prerequisite HAD5308H: Evidence Synthesis: Systematic Reviews and Meta-Analysis
Delivery Format Intensive
Semester Offered Spring
Instructor Bruno da Costa
Peter Juni
Objectives: This intensive 4-day course, with each session separated by a two-week interval, will enable students to conduct and critically appraise mainstream advanced evidence synthesis methods encountered in medical research. Students will be introduced to important topics in evidence synthesis, building on their previous introductory training on basic concepts of meta-analysis. By the end of the course, students will be able to conduct frequentist and Bayesian meta-regression, pairwise and network meta-analysis, properly conduct individual patient data meta-analysis, and meta-analyze diagnostic test accuracy estimates. The course will use a balanced combination of lectures and practical work to introduce concepts and provide students with supervised hands-on experience on these analysis methods. Course assignments will assess the students’ ability to appropriately select and conduct the analysis methods taught, and to develop a brief protocol of a future evidence synthesis project they would like to conduct using one of the analysis methods taught.

Participation 10%
Individual Assignments 60%
Group Assignment 30%


Course Number HAD7002H-S2
Course Name Philosophical Perspectives in Epidemiology
Prerequisite None
Delivery Format Weekly
Semester Offered Winter
Instructor Brian Feldman
Brian Baigrie
Mat Mercuri

Objectives: Epidemiology, and more generally, patient and population based research is founded on a set of theories and assumptions – the scientific method. There is a strong philosophy of science that has led to our understanding of the scientific method. Our graduate students are taught ways of applying science to get valid and meaningful inferences that will, hopefully, improve human health. However, not everyone agrees with the science that we teach, nor should they. We should challenge the methods that we use and teach, and the assumptions that guide these methods; challenge will make sure that our science is as strong as it can be, and our impact is as high as it can be. In this course, we will examine i) theories of causation and the hegemony of ‘evidence based medicine’, ii) statistical inference, frequentist and Bayesian epistemology, and the ‘reproducibility crisis’, iii) measurement theories, their implied ontologies and issues of ‘validation’, and iv) how values and cognitive science inform clinical and policy decisions. The course is meant for students near the end of their coursework who have some familiarity with these principles of science.

Evaluation: This course is a seminar course in which students will lead discussions based on readings. This is a pass/fail course and there are no assignments outside of the readings and class discussions. The facilitators (scientists and philosophers of science) will aid in the reading choices and discussion process.