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Health Technology Assesment related Courses

Courses in health technology assessment are offered through a number of departments and faculties across the University of Toronto. In addition, the Institute of Health Policy, Management and Evaluation offers an International Master of Science degree in Health Technology Assessment and Management.

The following are current offerings. Enrolment may be limited and subject depends on instructor's approval (click  for more information).

Joint and Collaborative Programs

Institute of Health Policy, Management and Evaluation

Course descriptions from Health Technology Assessment & Management, Clinical Epidemiology & Health Care Research and Health Services Research programs. For more information, please contact the IHPME directly.

Please note: Health Technology Assessment & Management program is being restructured and will not be offered in September 2015.

  • HAD5304H: Clinical Decision Making
    HTA & Management program is being restructured this course will not be offered in September 2015.
    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.
  • HAD5306H: Introduction to Health Care Research Methods and the Use of Health Administrative Data
    An introduction to the research methods using secondary data (e.g., administrative databases) for evaluating the outcomes and effectiveness of medical care. These methodologies are used to answer questions about which treatments and services work when applied to whole populations in real practice settings. In this course, the student will learn about the use of secondary data for research purposes. This will include the nature of secondary databases, data accuracy, risk adjustment, and a variety of statistical analyses (e.g., small area variation, logistic regression, and multilevel modelling) . The course will focus on existing sources of administrative data in Ontario.
  • HAD5307H: Introduction to Applied Biostatistics
    This course is designed to give clinical epidemiology students the knowledge and skills in statistical methods that apply to clinical epidemiology. As well, students will acquire working experience in applying these methods to datasets, analysing epidemiological data, interpreting findings and presenting results.
  • HAD5308H: Principles and Practices in Systematic Reviews and Health Technology Assessment
    HTA & Management program is being restructured this course will not be offered in September 2015.
    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.
  • HAD5312H: Decision Modeling for Clinical Policy and Economic Evaluation
    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
  • HAD5314H: Applied Bayesian Methods in Clinical Epidemiology and Health Care Research
    This course will introduce students to Bayesian data analysis. After an introduction 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 WinBUGS program to carry out analyses of data commonly seen in health sciences. Bayesian methods will be presented for binary and continuous outcomes in one and two samples, for linear and logisitic regression, and for meta-analysis.
  • HAD5315H: Advanced Topics In Measurement
    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.
  • HAD5316H: Biostatistics II: Advanced Techniques in Applied Regression Methods
    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
  • HAD5730H: Economic Evaluation
    HTA & Management program is being restructured this course will not be offered in September 2015.
    Health Economics is concerned with the study of resource allocation within the health sector and between that sector and other sectors. This course is designed to introduce participants to an array of economic evaluation methods used to assess health care programs, services, technologies, and other interventions. Prior knowledge of economics is not required; however, participants are expected to possess quantitative skills (e.g., the ability to undertake statistical analyses). Upon completion, participants will not only have analytic skills that are applicable to economic evaluation, they will also know how economists approach important issues in health services research and decision-making.
  • HAD5738H: Advanced Methods for Economic Evaluation
    The course is about advanced methods for estimation and uncertainty of cost-effectiveness statistics. The focus is on techniques to create and explain economic information in person-level data (e.g., from a clinical trial or an administrative data set). Students must have taken HAD 5730 and be familiar with statistical techniques like regression. Permission of the instructor is required for this class. Upon completing this course, participants will be able to create and explain the results of a cost-effectiveness analysis of person-level data.
  • HAD5744H: Introduction to Health Econometrics
    This course is designed to provide an introduction to econometric methods. That is, the basic principles of regression model development and testing that underlie much of applied health economics and health services research. The starting point is the fact that a great number of possible data generating processes yield very similar looking data series. The course deals with how to determine which data generating process, from among the range of possible ones, has actually generated the data you are working with. To that end, the course deals with application of statistical tests and procedures in the context of distinguishing between potential regression models. Students will learn about important methodological considerations when working with both survey and administrative datasets. It is assumed that students have a basic training in statistics.
  • HAD5745H: Where Health Economics Hits the Road: Practical Applications of Economics to Real Health Care Problems
    This seminar course is designed for graduate students in the Institute of Health Policy, Management and Evaluation and perhaps other students at the University of Toronto who have an interest in examining the use of economic concepts as they apply to real health care problems in a hospitalized setting, i.e. the front line (see Incorporating Economic Reality Into Medical Education: Sessions, Samuel Y., MD, JD; Detsky, Allan S., MD, PhD. (JAMA, September 15, 2010-Vol 304, No.11). The course will consist of seven core sections that illustrate health care problems and the use of economic thinking in solving those problems. Micro economic theory will be used as a platform. Students will be expected to participate in all of the sessions and to take one patient or administrative scenario and work it up as an economic problem. They will use this conceptual framework to develop both a paper and an oral presentation. Grade will be determined by class participation, oral and written presentations
  • HAD5755H: Health Economics Graduate Seminar Series
    The focus of this seminar series is on the practicalities of doing research in health economics. It is open to all IHPME graduate students. The students will gain experience in the application of the methodological and theoretical tools of economics to their own work. The aim in part of the seminar is to reinforce concepts covered in IHPME graduate courses. The seminar also aims to expose students to emerging issues in the field of health economics. There will be invited speakers who will discuss particular issues in research in health economics theory and methods as they are encountered in actual ongoing research projects. Thesis stage students will be required to present their own research and to actively participate in discussions of each other’s presentations and presentations of invited speakers. The seminar series takes place over 2 terms (September to April) and students are expected to attend 75% of all sessions.
  • HAD5760H: Health Systems, Economics of Health Care, Equity, HTA and Policy-making
    HTA & Management program is being restructured this course will not be offered in September 2015.
    Economic models of human and institutional behaviour are employed in this course to analyse the workings of the medical market. Specific attention is paid to the behaviour of both health care providers (e.g., physicians and hospitals) and health care clients. In analysing the behaviour of these participants in the health care industry, attention is paid to the socio-economic dimensions of health, health reform, physician supply management and payment reform, and health system restructuring.
  • HAD5763H: Institutional Management and Impact Evaluation
    HTA & Management program is being restructured this course will not be offered in September 2015.
    This seminar course covers conceptual and methodological issues related to descriptive and observational health services research.
  • HAD5771H: Resource Allocation Ethics
    This course will introduce students to key topics in priority setting (resource allocation) from both theoretical and practical viewpoints. The goal is for students to develop a better understanding of priority setting (resource allocation) in health care institutions and health systems from an interdisciplinary perspective. We will explore the contributions and interaction of ethics, economics, political science , and management science approaches to priority setting. Case studies will be a constitutive component of each session.
  • HAD5772H: Intermediate Statistics for Health Services Researchers
    This course is designed to prepare students in the following areas: analysis of variance for one-way and multiway data for fixed, mixed and random effects; repeated measures analysis of variance; analysis of covariance; linear and multiple regression; logistic regression factor analysis; and structural equation modeling (introduction).
  • HAD6750H: Advanced Health Economics and Policy Analysis II
    This is a seminar course required for all PhD students in the Health Economics PAS of the IHPME HSR doctoral program. It focuses on teaching the tools of microeconomic theory in modeling individual and firm behaviour using examples drawn from the health literature. The course introduces students to problems of unconstrained and constrained optimization in a discrete time framework. Additional topics considered include non-negativity constraints, questions concerning planning over multiple periods and the issues of uncertainty and unanticipated health shocks. Students are expected to develop their own theoretical model with testable predictions, which, in most cases, will serve as the basis for the theoretical chapter of their dissertation. Students must have completed Advanced Health Economics and Policy Analysis (HAD5760H) and have familiarity with intermediate calculus.
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Faculty of Pharmacy

Course descriptions from the PharmD (Doctor of Pharmacy) and Graduate Programs (MSc + PhD) programs. For more information, please contact Leslie Dan Faculty of Pharmacy directly.

  • PHM213H1 Health Economics and Pharmacoeconomics
    This course surveys the economic aspects of the pharmaceutical sector. Specific topics include the economics of the development of new drugs; economic appraisal of new drugs (“pharmaco-economics”); different approaches that insurers can use to procure generic drugs and remunerate pharmacists; and economic models of the pharmacist labour market. The course will use the methods of economic analysis to investigate how markets allocate resources, when they work well and the role for government when they do not work well.
  • PHM380H1 Health Technology Assessment  
      
    This course will take a practical approach to health technology assessment, the multidisciplinary field of policy analysis that examines the medical, economic, social, and ethical implications of the incremental value, diffusion and use of technologies in health care. Students will learn about pertinent topics selected from the three paradigms which provide the foundation and principles for technology assessment: evidence based medicine, health economics, and social sciences.
  • PHM1132H Applied Health Econometrics
    Application of econometric methods to predict or forecast, to estimate treatment effects, and to assess the precision of predictions or treatment effects estimates in a variety of different scenarios distinguished by:
    - The nature of the outcome variable (such as continuous, binary, ordered categorical).
    - The research design (experimental or observational)
    - The type of observations (cross sectional, time series or longitudinal)
    In the case of treatment effects estimation, whether treatment effects are the same for all observations or if they are heterogeneous.
  • HAD5312H Decision Modeling for Clinical Policy and Economic Evaluation II
    This course will review 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 mode
  • HAD5744H Introduction to Health Econometrics
    This course is designed to provide an introduction to econometric methods. That is, the basic principles of model development and testing that underlie much of applied health economics and health services research. The starting point is the fact that a great number of possible data generating processes yield very similar looking data series. The course deals with how to determine which data generating process, from among the range of possible ones, has actually generated the data you are working with. To that end, the course deals with application of statistical tests and procedures in the context of distinguishing between models. It is therefore assumed that students have a basic training in statistics.
  • LMP1407H Introductory Biostatics and Clinical Investigation
    The course is intended to provide a "user's guide" to biostatistics and the SPSS statistical software package. This course does not require previous experience in biostatistics, but rather its purpose is to introduce a broad audience of biologically-oriented graduate students to fundamental considerations in experimental design, analysis and interpretation.
    The aim is to develop an ability to understand the statistical implications of various experimental designs and hypotheses, and to analyze and present research results clearly and objectively.
  • PPG2010H Panel Data Methods for Public Policy Analysis
    The course provides a rigorous introduction to statistical methods for the analysis of panel data with specific application to the major Canadian longitudinal data sets. This course is offered in collaboration with the Toronto RDC. The RDC provides secure access to Canada's preeminent panel data sets for public policy analysis as well as variety of other Statistics Canada data. The course will take place within RDC providing students hands on experience with these important sources of information on public issues. The RDC offers both lecture space and a computer lab for tutorials. While the specific goal of this course is to introduce students to empirical methods for the analysis of longitudinal data, an important by product is their exposure to the RDC data. These data are increasingly "the basis" for new survey based research in health, education, economics and other social sciences in Canada. Instruction includes a combination of lectures and tutorials. In tutorials, students will
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Dalla Lana School of Public Health

For more information, please contact the Dalla Lana School of Public Health directly.

  • Applied Bayesian Methods
    The goal of this course is to gain an understanding of the basic theory of Markov chain Monte Carlo methods; and gain proficiency in performing Bayesian data analysis on complex data problems.
  • CHL5225H Advanced Statistical Methods for Clinical Trials
    Academic and professional statisticians are frequently included as co-investigators on clinical trials. Their responsibilities as a co-investigator usually include trial design, involving issues of randomization, stratification and sample size determination, as well as statistical data analysis, reporting and presentation. Consequently, there is a substantial demand from academia and the pharmaceutical industry for graduate level statisticians with training and experience in advanced statistical methodology for clinical trials. In response to that demand this course has been designed to provide exposure to the advanced statistical methods used in clinical trials for students seeking graduate degrees in biostatistics or statistics.
    Go to: Complete Details for more information
  • CHL5308H Tools and Approaches for Public Health Policy Analysis and Evaluation
    This course will provide skills and training in public health policy analysis and evaluation, including economic analysis. Students will be exposed to approaches to public health policy analyses and evaluations and will become familiar with the nuts and bolts of conducting analysis and evaluation. Readings and discussions will offer critical perspectives on the practice of policy analysis and evaluation through public health case studies highlighting challenges, limitations and strengths.
    There will be twelve seminar style meetings in which instructors will outline approaches, techniques and critical perspectives and facilitate discussions. Public health policy case studies will be used to anchor critical discussions of the application of tools and approaches to real-life situations.
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Department of Mechanical and Industrial Engineering

Course descriptions from the Department of Mechanical and Industrial Engineering and Centre for Research in Healthcare Engineering (CRHE) that are relevant to careers in healthcare engineering. For more information, please contact the Department of Mechanical and Industrial Engineering directly.

  • MIE253: Data Modelling
    This course provides an understanding of the principles and techniques of information modelling and data management, covering both relational theory and SQL database systems (DBMS), as well as entity-relation conceptual modelling. The course also familiarizes the student with analytical applications (OLAP) and provides an introduction to XML data modelling. The laboratory focuses on database application development using SQL DBMS, OLAP queries and entity-relation data modelling.
  • MIE263: Operations Research II: Stochastic
    Modeling and analysis of systems subject to uncertainty using probabilistic methods. Introduction to decision analysis. Derivation and application of Bernoulli and Poisson processes, Markov chains, and queuing models. Stochastic optimization and extensions. Applications to engineering, games of chance, health care, and management.
  • MIE451: Decision Support Systems
    This course provides students with an understanding of the role of a decision support system in an organization, its components, and the theories and techniques used to construct them. The course will cover basic technologies for information analysis, knowledge-based problem solving methods such as heuristic search, automated deduction, constraint satisfaction, and knowledge representation.
  • MIE1605: Stochastic Processes
    A course on the fundamentals of stochastic processes and their application to mathematical models in operational research. Topics discussed will include a review of probability theory, Poisson processes, renewal processes, Markov chains and other advanced processes. Emphasis on applications in inventory, queuing, reliability, repair and maintenance, etc. After completing this courses, students will be able to:
    • Capture uncertain system dynamics using appropriate stochastic processes.
    • Predict short-term behaviors of stochastic systems.
    • Evaluate long-term system performance.
  • MIE561: Healthcare Systems
    MIE 561 is a cap-stone course. Its purpose is to give students an opportunity to integrate the industrial
    engineering tools learned in previous courses by applying them to real world problems. While the
    specific focus of the case studies used to illustrate the application of industrial engineering will be the
    Canadian health care system, the approach to problem solving adopted in this course will be applicable
    to any setting. After completing this courses, students will be able to:
    • Identify, describe and apply an appropriate model for analyzing unstructured problems in a complex decision making environment.
    • Define, describe, analyse and criticize methods for resolving complex problems in a practical situation.
    • Describe appropriate (and inappropriate) applications of industrial engineering techniques.
    • Effectively communicate an understanding of a complex problem and a method of resolution, in writing, at a level consistent with current bus
  • MIE566: Decision Analysis
    The purpose of this course is to provide a working knowledge of methods of analysis of problems and of decision making in the face of uncertainty. Topics include decision trees, subjective probability assessment, multi-attribute utility approaches, goal programming, Analytic Hierarchy Process and the psychology of decision making. After completing this courses, students will be able to:
    • Formulate a decision making problem that is informally stated into an organized diagram that can be used to find good alternatives.
    • Perform quantitative analysis to understand various aspects of the identified solutions.
    • Understand common mistakes made in decision making processes.
    • Make decisions with consideration of dynamic interactions between independent decision makers.
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Various HTA Related Courses

  • Measuring and Valuing Health  
      
    FREE online course from the University of Sheffield, UK
    Learn how Patient Reported Outcome Measures and Quality Adjusted Life Years can compare treatments and inform healthcare spending.
  • R Programming
    Online for fee course offered by Johns Hopkins University
    In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
  • OUR PARTNERS
  • Leslie Dan Faculty of Pharmacy at the University of Toronto
  • Institute of Health Policy, Management and Evaluation (IHPME)
  • UHN - Toronto General Hospital
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