Short Courses

The ISPOR Short Course Program is offered in conjunction with ISPOR conferences around the world as a series of 4- and 8-hour training courses, designed to enhance your knowledge and technique in 6 key topic areas (“Tracks”) related to health economics and outcomes research (HEOR).  Short courses range in skill level from Introductory to Experienced.

For more information on ISPOR Short Courses, please visit this page or for questions related to this or any other ISPOR Short Course Program, please contact shortcourse@ispor.org.

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12 September 2019

08:00 - 17:00

Introduction to Health Economics

Level

Introductory

Track

Economic Evaluation


  • Description
  • headphonesPresented in English and Spanish with simultaneous English / Spanish interpretation 

    This course is designed to teach clinicians and new researchers how to incorporate pharmacoeconomics / health economics into study design and data analysis. Participants will learn how to collect and calculate the costs of different healthcare or healthcare economic evaluation alternative treatments, determine the economic impact of clinical outcomes, and how to identify, track, and assign costs to different types of healthcare resources used. The development of economic protocols and data collection sheets will be discussed. Different health economics models and techniques will be demonstrated with case studies. These include: cost-minimization, cost-of-illness, cost-effectiveness, cost-benefit, and cost-utility analysis. Decision analysis, sensitivity analysis, and discounting will also be demonstrated and practiced. Participants will learn to compare and evaluate interventions such as drugs, devices and clinical services.



12 September 2019

08:00 - 12:00

Health-Related Quality-of-Life / Utility Measures

Level

Intermediate

Track

Patient-Centered Research


  • Description
  • Presented in Spanish 
    Conceptual, methodological, and practical methods for measuring quality of life, health status, and other types of health outcomes will be presented. Utility measurement (a method of determining an individual’s preference for a certain outcome represented by a quantitative score—utility), will also be reviewed. Methods for measuring preference-based outcomes like the standard gamble, time trade-off, and visual analogue scale will be demonstrated. Additionally, utility-based instruments such as the EQ-5D, HUI, QWQ and SF-36 will be briefly discussed. Utility measurement, however, is not only about mastering these techniques, it is about using them in such a way that healthcare decision makers can apply the results, for instance, in cost per QALY analyses. For this purpose, one needs to be aware of shortcomings of the available utility measures and potential solutions. Furthermore, one should be aware of the decision-making context and the way results are interpreted. To equip participants with expertise in the field of utility measurement, the most important issues will be discussed, such as potential insensitivity of generic instruments for disease-specific problems, and to what extent adaptation of generic or disease-specific quality-of-life instruments may offer a solution. Also, the issue of “Whose values count: patient values or values from the general public?” will be discussed. Finally, we turn to the interpretation in the context of resource allocation.



12 September 2019

08:00 - 17:00

Multi-Criteria Decision Analysis

Level

Intermediate

Track

Health Technology Assessment


  • Description
  • headphonesPresented in English and Spanish with simultaneous English / Spanish interpretation  

    Many healthcare decisions—such as portfolio optimization, benefit-risk assessment (BRA), health technology assessment (HTA), and shared decision making (SDM)—require a careful assessment of the underlying options and the criteria used to judge these options. This assessment can be challenging given the trade-offs between multiple value criteria. In light of this, many decision makers have begun investigating the use of multi-criteria decision analysis (MCDA) in support of these decisions. This course provides an introduction to MCDA for healthcare. The course will focus on the use of MCDA for HTA, and will be organised around the following parts: 1) Introduction to MCDA: What is it and how is it being used in HTA?; 2) Implementing MCDA 1: Practical tips when implementing MCDA; 3) Implementing MCDA 2: Methodological options when designing an MCDA; and 4) Using MCDA for HTA: Challenges and possible solutions. These parts are designed to familiarize participants with the steps involved in undertaking an MCDA, the alternative ways of implementing these steps, and good practice guidelines. The course will also review the current MCDA HTA landscape, including current use of MCDA for HTA and the challenges this poses. The course is designed for those unfamiliar with MCDA, but who have a basic understanding of other evaluation methodologies.



12 September 2019

08:00 - 12:00

Introduction to Modeling

Level

Intermediate

Track

Methodological & Statistical Research


  • Description
  • Presented in Spanish
    This course includes a review of Markov models, discrete event models, and other modeling techniques and their appropriate applications, including a review of the ISPOR Principles of Good Practice for Decision Analytic Modeling in Health Care Evaluations, as well as the recent ISPOR-SMDM guidelines (Value Health 2012). Using a series of related examples, the course will carefully review the practical steps involved in developing and using these kinds of models. Instructors will cover the practical steps involved in the selection and modeling of data inputs and practical aspects related to the determination of when, why and how to handle stochastic (i.e., first order Monte Carlo Simulations) and probabilistic uncertainty (i.e., second order Monte Carlo Simulations). Issues related to the selection of model input parameters and their distributions for use in probabilistic sensitivity analyses will be considered.



12 September 2019

08:00 - 12:00

NEW! Introduction to Real-World Evidence: Between Epidemiology and Digital Tools

Level

Introductory

Track

Real World Data & Information Systems


  • Description
  • headphones Presented in English and Spanish with simultaneous English / Spanish interpretation 
    Real-World Evidence (RWE), which emerges from Real-World Data (RWD) (i.e., from outside randomized clinical trials), has been under increasing attention and use due to its potential to improve quality and control costs in healthcare systems. Although RWE and RWD resemble traditional epidemiology in many respects, there are also distinct features due to a strong focus on secondary data and novel approaches contributed by evolving health IT infrastructures and evolving pharmacoepidemiology and statistical methods. This introduction to RWE will focus on the intersection of traditional methods and new tools. After presenting RWE and RWD topics (i.e., definitions; sources of RWD which focus on secondary data sources generated in the care process, such as claims data and electronic medical records; and uses of RWE), we will focus on key methodological aspects to conduct and assess the quality of a RWE study: study designs, the identification and remediation of selection, confounding and measurement bias, and considerations concerning statistical modeling. Finally, we will bring together these different topics in a workshop demonstrated by live demonstrations using the tools developed by the Observational Health Data Sciences and Informatics (OHDSI) collaborative consortia, which will allow discussion on common data models and open source tools for descriptive analyses and treatment population effect estimations. Finally, closing remarks will focus on RWE governance and emerging trends.


12 September 2019

08:00 - 12:00

Network Meta-Analysis

Level

Experienced

Track

Study Approaches


  • Description
  • Presented in Spanish 
    Faculty will discuss meta-analytic methods used to assess the quality of evidence for healthcare interventions. Statistical approaches to pooling results from several studies and application of meta-analysis in pharmacoeconomic studies and healthcare decision making will be presented. 


12 September 2019

08:00 - 12:00

NEW! Evaluation of Medical Devices: How to Manage HTAs

Level

Intermediate

Track

Health Technology Assessment


  • Description
  • headphones

     Presented in English and Spanish with simultaneous English / Spanish interpretation

    The objective of this short course is to promote understanding of the key challenges associated with the evaluation of medical devices for HTA and provide robust alternative solutions.

    The course will begin by exploring core differences between HTA for drugs and medical devices. We will reflect on the international experience on evaluation of medical devices for HTA and compare this with the approaches currently implemented by HTA agencies in Latin America to evaluate these health technologies. Exemplary case studies will be used to illustrate potential solutions to key methodological challenges in the evaluation of medical devices for HTA. The potential contribution of real-world evidence and real-world data to address core methodological issues associated with medical device evaluation for HTA will be considered. Participants will have the opportunity to apply the theoretical concepts considered in the lectures by developing a protocol for HTA evaluation of a medical device. By the end of the course, participants will have a clear understanding of core challenges associated with HTA evaluation of medical devices and information on robust alternative methods to address these.



12 September 2019

13:00 - 17:00

Applied Modeling

Level

Experienced

Track

Methodological & Statistical Research


  • Description
  • Presented in Spanish

    This course will provide a basic understanding of the concepts of discretely-integrated condition event (DICE) simulation as it is applied in health technology assessment (HTA). Topics to be covered are: what is the basic idea of DICE; what are its components; how does it work; how is it conceptualized; how are outcomes obtained; how to implement a DICE in EXCEL® (including both discrete event simulation and Markov models, and their combination in a single structure); how to do structural sensitivity analyses; and what are the advantages and disadvantages of DICE. Participants who wish to build a model themselves should bring a Windows laptop computer with Microsoft Excel® installed and running.



12 September 2019

13:00 - 17:00

Budget Impact Analysis for Health Decision Making in Latin America

Level

Intermediate

Track

Economic Evaluation


  • Description
  • headphones Presented in English and Spanish with simultaneous English / Spanish interpretation 
    The purpose of this course is to present the main concepts, elements and discussions on budget impact analysis (BIA) for health decision making in the context of Latin American countries. Conceptual aspects of the BIA of a new healthcare technology are reviewed, its relevance for decision-making in coverage policies in the regional context is examined, and the recommendations of the BIA inclusion and methods of the economic evaluation guides of Latin American countries as well as the ISPOR Good Practice Guide for the realization of the BIA are presented. These concepts and discussions are illustrated through an application exercise with an interactive BIA model.



12 September 2019

13:00 - 17:00

NEW! Introduction to Machine Learning

Level

Intermediate

Track

Study Approaches


  • Description
  • Pre-requisite: Basic knowledge of linear regression is required.

    Presented in Spanish
    Machine learning studies representations and algorithms that allow machines to improve their performance on a task from experience. Machine learning is all about finding patterns in data to get computers to solve complex problems. Instead of explicitly programming computers to perform a task, machine learning lets us program the computer to learn from examples and improve over time without human intervention. This requires addressing a difficult question: how to generalize beyond the examples that have been provided at "training time" to new examples that you see at "test time". This course will show you how this generalization process can be formalized and implemented. We will look at it from various perspectives, illustrating the key concepts in the field of health economics. This course covers fundamental concepts, methodologies, and algorithms related to machine learning. Topics covered include bias/variance theory, model calibration, decision trees, linear and non-linear regression, support vector machines, neural networks, and ensemble methods.