* Program subject to change
Track: Economic Evaluation
This highly practical course will outline the computational and transparency advantages of using R, for those used to health economic modelling using Microsoft Excel. This course explores the use of R for health economic modelling in the context of health economics and outcomes research (HEOR) and faculty will guide the participants through practical examples of HEOR. The faculty are expert speakers who have diverse experience in academia, national Health Technology Assessment agencies (NICE, NCPE), and industry. The faculty will lead participants through practical examples of health economic modelling including using R for Markov models from deterministic analysis through to probabilistic sensitivity analysis and EVPI. Additional useful packages for modelling using R will also be discussed. All sessions will interchange between descriptive lectures and hands-on exercises. Participants will be provided with materials, including model examples in R and information on where to go for further learning.
This course is designed for those with some familiarity with modelling techniques, such as the concepts of discrete time cohort Markov models and probabilistic sensitivity analysis, but familiarity with R coding is not required.
Attendees will require a laptop with RStudio (v1.1.0 or higher) and R (v4.2.1 or higher) downloaded and installed.
Gianluca Baio, PhD
University College London, London, United Kingdom
Rose Hart, PhD
Lumanity Inc., Sheffield, United Kingdom
Felicity Lamrock, PhD
Queens University Belfast, Belfast, ANT, United Kingdom
Howard Thom, MSc, PhD
University of Bristol, Bristol, UK; Clifton Insight, Bristol, SOM, United Kingdom
Track: Real World Data & Information Systems
In this course, participants will be guided through a hands-on analysis of real-world data to develop decision-grade real-world evidence (RWE) that could be used to support an indication expansion. The first section of the course focuses on what makes RWE “decision-grade.” We will review the most recent RWE frameworks and guidelines set by regulatory agencies and professional organizations, and we will examine case studies in which these guidelines were used in regulatory and HTA approval. The second half of the course is an active workshop where participants will use principles from the first half of the course to execute a decision-grade RWE study. Participants will be guided step-by-step in using a software platform that will enable them to work within a longitudinal US insurance claims database with anonymized patients. After the study has been executed, we will discuss how these results could be communicated to decision makers. Participants should come with a laptop with Google ChromeTM installed.
PREREQUISITE: Students are expected to be familiar with relevant concepts and methodologies for analyzing real-world data, but this course does not require specific programming skills.
Dorothee Bartels, PhD, MSc
Aetion, Inc., Neuss, Germany
Shirley Wang, PhD, MSc
Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Track: Health Policy & Regulatory
During recent years, Managed Entry Agreements (MEAs) have become instrumental in ensuring the access of innovative medicines. This course is designed for healthcare professionals (including public decision-makers, academia, and industry) involved in pricing and reimbursement decisions who are wishing to understand the applicability and technical aspects of managed entry agreements (MEAs) in countries with severe economic constraints and explicit cost-effectiveness criterion. The topic will be introduced with key features of pricing and reimbursement systems in representative countries to understand why special methods are needed to facilitate evidence-based reimbursement policies of new health technologies. Faculty will present an economic model to explain the methodology and implications of managed entry agreements in cost-effectiveness and budget impact analysis. Participants will then have the opportunity to apply what they have learned through a hands-on exercise on making pricing and reimbursement decisions. A decision algorithm will be presented to support evidence and value-based policy decisions of high-cost new technologies. A series of password protected economic models will add more and more complexity to a pragmatic case study on a new pharmaceutical product in oncology. To close the course faculty will lead a discussion on the applicability of a pragmatic decision tool illustrating the pros and cons of different managed entry agreements and their usefulness in CEE settings. Participants who wish to gain hands-on experience must bring their laptops with Microsoft Excel for Windows installed.
Rok Hren, PhD, MSc IHP (HE)
University of Ljubljana, Ljubljana, Slovenia
Katarzyna Kolasa, PhD
PAREXEL and Kozminski University, Warsaw, MZ, Poland
Bertalan Nemeth, PhD
Syreon Research Institute, Budapest, Hungary
Track: Study Approaches
For several medical questions of interest, many treatment options exist for the same indication. These treatments may have been compared against placebo or against each other in clinical trials. Knowing whether one specific treatment is better than placebo or some other specific comparator is only a fragment of the big picture, which should incorporate all available information. Ideally, one would know how all the treatment options rank against each other and the level of differences in treatment effects between all the available options. Network meta-analysis provides an integrated and unified method that incorporates all direct and indirect comparative evidence about treatments. Based in part on the ISPOR Task Force Reports on Indirect Treatment Comparisons, the fundamentals and concepts of network meta-analysis will be presented. The evaluation of networks presents special challenges and caveats, which will also be highlighted in this course. The material is motivated by instructive and concrete examples. The ISPOR-AMCP-NPC questionnaire for assessing the credibility of a network meta-analysis will also be introduced.
This course requires at least a basic knowledge of meta-analysis and statistics.
Sarah Goring, MSc
SMG Outcomes Research, Vancouver, BC, Canada
Jeroen Jansen, PhD
School of Pharmacy, University of California San Francisco, San Francisco, CA, USA, and PRECISIONheor, Oakland, CA, USA
Track: Health Technology Assessment
This introductory course is designed to teach academic researchers, health policy decision makers, manufacturers, and clinicians about the key elements, methods, and language of health technology assessment (HTA). The course provides an overview of basic HTA principles including benefit assessment (biostatistics, clinical epidemiology, patient-relevant outcomes, risk-benefit assessment), economic evaluation (costing, cost-effectiveness analysis, pharmacoeconomic modeling, budget impact analysis, resource allocation), and ELSI (ethical, legal, and social implications). Using real world examples covering both drugs and devices, the course will review the practical steps involved in developing and using HTA reports in different countries and healthcare systems. Discussion with participants will focus on the implementation of HTA in health care decision making and stakeholder perspectives.
This course is suitable for those with little or no experience with HTA.
Petra Schnell-Inderst, MPH, PhD, Dipl. Biol
UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, 7, Austria
Uwe Siebert, MD, MPH, MSc, ScD
UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria. ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria. Harvard T.H. Chan School of Public Health and Harvard Medical School, Hall in Tirol, 7, Austria
Track: Methodological & Statistical Research
PREREQUISITES: To get the most out of the course, students should have a basic statistical background. Participants who wish to gain hands-on experience are required to bring their laptops with Radiant (https://radiant-rstats.github.io/docs/install.html) installed.
Wei-Hsuan Lo-Ciganic, PhD, MSPharm, MS
University of Florida, Gainesville, FL, USA
William Padula, PhD, MSc, MS
University of Southern California, Los Angeles, CA, USA
This course will describe the methods used to estimate the budget impact of a new health care technology and will present six basic steps for estimating budget impact: (1) estimating the target population; (2) selecting a time horizon; (3) identifying current and projected treatment mix; (4) estimating current and future drug costs; (5) estimating change in disease-related costs; and (6) estimating and presenting changes in annual budget impact and health outcomes. Both static and dynamic methods for estimating the budget and health impact of adding a new drug to a health plan formulary will be presented. These six steps will be illustrated using actual budget impact models.
This course is designed for those with some experience with pharmacoeconomic analysis.
Thor-Henrik Brodtkorb, PhD
RTI Health Solutions, Ljungskile, O, Sweden
Stephanie Earnshaw, PhD, MS
RTI Health Solutions, Pittsboro, NC, USA
C. Daniel Mullins, PhD
University of Maryland School of Pharmacy, Baltimore, MD, USA
Track: Patient-Centered Research
Health state utility (HSU) estimates are among the most important and uncertain data inputs in cost-utility models which are increasingly being used to inform health technology assessment, pricing, and reimbursement decisions in many countries. This course will provide an in-depth consideration of best practice in the collection of health utility data in clinical trials, real-world and other studies, to provide high quality HSU estimates appropriate for economic modeling. Centered on the ISPOR Outcomes Research Guideline, Collecting Health-State Utility Estimates for Economic Models in Clinical Studies (Wolowacz et al., 2016), the course will address key challenges surrounding study design, data collection and analysis. This will include how to anticipate and address common issues that may affect data quality, alignment with the needs of economic model, acceptability to the model audience, and how to apply good research practices for HSU estimation in future research. The course will also address issues associated with collection of utility data for rare diseases and from special populations including cognitively impaired and pediatric populations. The course will be of value for researchers actively involved in the design or implementation of HSU data collection or analysis, those involved in patient-reported outcomes research, economic modeling, economic evaluation, or health technology assessment.
The course will not cover in any depth the fundamentals of utility theory, development of generic or condition-specific preference-based multi-attribute utility instruments, or how to perform time trade-off or standard gamble experiments. Nor will it cover statistical methods for mapping/cross-walking from a condition-specific HRQL measure. Although these topics will be touched on in overview, the focus of this course will be on optimizing the collection of utility data to provide HSU estimates for economic models.
Lynda Doward, MRes
RTI Health Solutions, Manchester, United Kingdom
Emma Hawe, MSc
Andrew Lloyd, PhD
Acaster Lloyd Consulting Ltd, London, United Kingdom
Sorrel Wolowacz, PhD
This course will provide an introduction to statistical concepts with an emphasis on the use of techniques commonly employed in health economics and outcomes research. Faculty will begin by defining statistics, then introducing the concept of random variables and probability before proceeding to discuss the foundations of statistical inference (estimation and the testing of hypotheses). This is followed by bootstrapping, statistics in cost-effectiveness analysis and generalized linear modeling (for cost and utility outcomes). The differences between a classical (frequentist) approach to statistics and a Bayesian view of probability will also be outlined.
This course is intended for participants with little (or rusty!) statistical training.
Derrick Bennett, MSc, PhD, CStat
University of Oxford, Headington, Oxford, United Kingdom
Jim Lewsey, PhD, CStat
University of Glasgow, Glasgow, United Kingdom
Unlike marketing authorization for pharmaceuticals, mainly regulated at the European level by EMA, pricing and reimbursement decisions in Europe are managed by individual member states. Health care services are generally covered by a single public health insurer operating under the Ministry of Health supervision. As a monopoly buyer, this situation provides a leading position for the public health insurer to set reimbursement conditions. Therefore, based on each country’s set of regulations, processes, and values, wide variations exist in pricing and reimbursement decisions of pharmaceuticals. Using up-to-date governmental regulation sources and the ISPOR Global Health Care Systems Roadmap, this course will discuss health technology decision-making processes for reimbursement decisions for pharmaceuticals in France, Germany, Hungary, Italy, Poland, Spain, Sweden, and the UK. The course will describe these reimbursement systems, as well as compare, and bring into contrast their key characteristics.
This course is designed for individuals with intermediate experience within a single healthcare system wishing to broaden their appreciation of other reimbursement systems.
Mondher Toumi, MD, PhD, MSc
Aix Marseille University, Marseille, PACA, France
This intermediate level course is for industry, researchers, advisory, regulatory, and funding agencies, governments, payers, clinicians, patients, and others who have a stake in improving care and services. The course focuses on Health Technology Assessment (HTA) concepts and methodologies in the early stages of product development (ie, early HTA). The research and development of a new medical technology is generally an expensive process; information about the potential clinical impact of a product—and its adoption and implementation into practice—at an early stage can justify the investment and guide further decision making all along the line. A major challenge is how to generate this information when no or only limited data is available.
This course provides an overview of the importance and constraints of early HTA, the evaluation and decision frameworks of different stakeholders, relevant research methodologies and the potential impact of the analyses. Using real-world case studies, we will present the process of working with stakeholders to frame the analysis, to gather evidence and to produce careful insights regarding potential new health technologies. Through a breakout exercise and group discussions, course participants will work on framing a product development decision and identify appropriate data collection and analysis methods.
PREREQUISITE: Attendance at short courses "Introduction to Health Technology Analysis", "Cost-Effectiveness Analysis Alongside Clinical Trials” and/or equivalent concepts and methodologies is prerequisite to attending this course.
Janet Bouttell, MSc, PhD
University of Glasgow, Glasgow, Scotland, United Kingdom
Sara Graziadio, PhD
York Health Economics Consortium, York, United Kingdom
Janneke Grutters, PhD
Radboud Institute for Health Sciences, Nijmegen, GE, Netherlands
Ties Hoomans, PhD
London School of Economics and Political Science, London, United Kingdom
Lotte Steuten, PhD
Office of Health Economics, London, Greater London county, United Kingdom
Historically, economic models for cost-effectiveness analyses have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). But more recently there has been increasing interest in using R and other programming languages for cost-effectiveness analysis which can offer advantages regarding the integration of input parameter estimation and model simulation, the evaluation of structural uncertainty, and the quantification of decision uncertainty, among others. Programming languages such as R also facilitate reproducibility of model-based cost-effectiveness analysis which is more relevant than ever given recent calls for increased transparency. While these tools are still relatively new, there is an increased interest in learning opportunities as evidenced by recent tutorials, workshops, and development of open-source software.
In this short course, participants will learn how to use R to develop a number of different types of economic models to perform cost-effectiveness analysis. Economic models will include time-homogeneous and time-inhomogeneous Markov cohort models, partitioned survival models, and semi-Markov individual patient simulations. The underlying assumptions of each model type will be summarized and the implementation in R will be presented in an accessible manner. Participants will be asked to modify the models in R (eg, adding health states, use of alternative time-to-event distributions) and run analyses (eg, cost-effectiveness analysis, probabilistic sensitivity analysis, evaluating structural uncertainty, and value of information analysis). To make this interactive aspect of the course as efficient as possible, all participants will have access to the GitHub repository prior to the course. It will contain R code to run the economic models and R Markdown files to explain and reproduce the analyses covered in the course. Participants who wish to gain hands-on experience are required to bring their laptops with R packages and scripts available. To get the most out of the course, it is important that registrants are able to do some R programming. Special instructions will be provided before the course.
Devin Incerti, PhD
EntityRisk, Inc., San Francisco, CA, USA
The value of medical innovation depends on the perspective. Registration authorities (EMA, FDA) mainly consider the clinical value of the medical innovation, whereas national health authorities take a broader perspective by including clinical, economic criteria, and potential other criteria like equity and social values. Value-based pricing is the most widely accepted approach in the pricing and reimbursement process in Europe, which varies from the narrow concept based on the incremental cost-effectiveness ratio (ICER) threshold to broader approaches. Value-based pricing determines the maximum price from the national payer perspective. This price should exceed the minimum price for the investor acting in the international financial market, which is based on economic valuation theory. Finally, there are other stakeholders, eg, patients, physicians’ healthcare insurers, employers, with their specific assessment of the value of medical innovation varying from, respectively, quality of life, effectiveness, budget impact, and costs of lost productivity. This course offers an overview of the perspectives of the relevant stakeholders and their respective data requirements for value assessment of innovative drugs. The course will then describe in-depth description of the various value-based pricing methods, eg, ICER, multicriteria decision analysis (MCDA), comparative effectiveness research (CER), and relative effectiveness (RE). We include examples of orphan drugs and ATMPs which are most striking to illustrate the concepts, but we also include value assessment for more traditional innovative drugs in broad indications. Familiarity with health economic evaluation is desirable, but the course assumes little or no familiarity with economic valuation theory.
Afschin Gandjour, MD, PhD, MA, MBA
Frankfurt School of Finance & Management, Frankfurt, Germany
Lou Garrison, PhD
The Comparative Health Outcomes, Policy, and Economics Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
Marlene Gyldmark, MPhil
Idorsia Pharmaceuticals, Allschwill, BS, Switzerland
Mark Nuijten, MBA, PhD, MD
A2M, Amsterdam, Netherlands
Fred W. Sorenson, MSc
Cencora, Basel, Switzerland
Survival modeling techniques are commonly used to extrapolate clinical trial outcomes like overall survival to a time horizon that is appropriate for health economic evaluations. Standard parametric distributions, such as the exponential and Weibull, have been the de-facto standard for conducting such extrapolations but, with the advent of novel potentially curative therapies, these standard parametric distributions fail to capture the underlying survival trend. Newer techniques like response based landmark models, parametric mixture models, and mixture cure models provide novel ways to capture these more complex survival patterns. The purpose of this course is to enable participants to identify which methods are most appropriate in a specific context, considering underlying structural assumptions, and discuss how modeling choices propagate into health economic evaluations. To gain a more in-depth understanding of the impact of the choice for a specific method, there will be walkthroughs of exercises which participants will be able to practice in their own time.
Elisabeth Fenwick, PhD
OPEN Health Evidence & Access, Oxford, OXF, United Kingdom
Sven Klijn, MSc
Bristol-Myers Squibb, Utrecht, ZH, Netherlands
Claire Simons, PhD, MSc, MMATH
OPEN Health Group, York, NYK, United Kingdom
PREREQUISTE: Participants should possess the basic skills and understanding of cost effectiveness and budget impact models.
Jochen Klucken, MD
University of Luxembourg, Luxembourg, N/A, Luxembourg
Alison Ritchie, PhD
Parexel, Gamston, England, United Kingdom
Economic evaluations make up a large proportion of the literature in health economics and outcomes research. These studies are increasingly used in healthcare decision making and it is critical that they are reported correctly. This course is intended to familiarize participants with the rational for, and content of, the important reporting items in economic evaluations. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) are an EQUATOR guideline and represent the state of art for reporting economic evaluations. CHEERS consists of 28 reporting items relating to the introduction and design of economic evaluations, their methods, and the reporting of results. The rationale for the various items will be given, along with illustrative examples explaining their content. Participants will learn how to use CHEERS in reporting their own economic evaluations, and in reading evaluations conducted by others. In addition, key issues relating to the use of CHEERS will be discussed.
Federico Augustovski, MD, MSc, PhD
Institute for Clinical Effectiveness and Health Policy (IECS), Buenos Aires, B, Argentina
Nathorn Chaiyakunapruk, PharmD, PhD
Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
Michael Drummond, PhD
University of York, Lichfield, STS, United Kingdom
This course covers the concrete application of the 6-step approach for developing budget impact analyses and provides hands-on learning with two different budget impact models programmed in Excel. The course will review the basics of budget impact analysis, interpretation of results, simplicity versus accuracy and face validity, and how budget impact analyses are used by payers and other decision makers. Technical topics will include static versus dynamic budget impact models, considerations for device and diagnostic technologies, and realistic features such as patient copayments and use of generics. The instructors will walk through 2 different budget impact analyses programmed in Excel (one static and one dynamic) and work with participants during hands-on exercises to enhance these models. The instructors will also review good practices for building budget impact models and provide a number of Excel tips. The Excel-based budget impact models used for the course will be provided to participants in advance of the presentation. This course is designed for those who have basic knowledge of budget impact analyses and desire exposure to these analyses in Excel. Participants who wish to gain hands-on experience must have Microsoft Excel for Windows installed on their computers.
Anita Brogan, PhD, MSc
RTI Health Solutions, San Diego, CA, USA
Ashley Davis, PhD, MSc
RTI Health Solutions, Research Triangle Park, NC, USA
In January 2022, a significant milestone was reached in the field of Health Technology Assessment (HTA) with the enforcement of the EU-HTA regulation. This regulation aims to harmonize the HTA process across the EU and improve efficiency by reducing duplication of efforts for national HTA authorities. Replacing the existing voluntary network of national HTA agencies and the EU-funded Joint Actions (EUnetHTA), the EU-HTA regulation introduces a permanent framework for collaborative work. To this end, it covers horizon scanning, joint scientific consultations, joint clinical assessments, and voluntary cooperation on non-clinical aspects of HTA which remains a national responsibility. From 2025 onwards, the regulation will be gradually implemented, starting with cancer treatments and advanced therapy medicinal products (ATMPs), followed by orphan drugs in 2028 and all other drugs in 2030. In this interactive course, we will discuss the methodological deliverables, procedural guidelines and templates produced by EUnetHTA 21, a consortium of EU-HTA agencies in charge of the key deliverables. We will focus on the latest developments around the joint clinical assessment, aiming to produce one single assessment of the relative clinical benefit of the new drug that is the basis for all EU member states. This assessment starts with a survey among the member states asking for the patient/population, intervention, comparison and outcomes (PICOs) they need. The course will also address the challenges associated with the position of the joint clinical assessment alongside the EMA assessment and the national market access processes, and the involvement of manufacturers and patient representatives.
In the final segment of this course, we will examine the potential implications and future prospects of the EU-HTA regulation. We will discuss how this regulation could potentially reshape the landscape of HTA in the EU, and what this means for stakeholders, including healthcare providers, patients, and pharmaceutical companies. We will also consider the potential challenges and opportunities that may arise as the regulation is progressively implemented. This will include a discussion on how to navigate the transition period and how to prepare for the full implementation of the regulation.
To ensure a practical understanding of these concepts, we will divide the classroom into smaller groups for a portion of the course. These groups will work on a practical exercise involving a hypothetical cell or gene therapy, using evidence from single-arm studies. By the end of this course, participants will have gained a comprehensive understanding of the EU-HTA regulation and will be equipped with the knowledge and tools to navigate this new era of health technology assessment in the EU. Join us as we unravel the complexities of the EU-HTA regulation and its impact on the future of HTA in the EU.
PREREQUISTE: Participants who wish to gain hands-on experience are required to bring their laptops.
Karin Becker, MSc, PhD
Boehringer Ingelheim GmbH, Ingelheim, Germany
Maureen Rutten-van Mölken, PhD
Erasmus University Rotterdam, Erasmus School of Health Policy and Management (ESHPM) and Institute for Medical Technology Assessment (IMTA), Rotterdam, Netherlands
Christophe Sauboin, MSc
Frederick Thielen, PhD
Erasmus University Rotterdam, Erasmus School of Health Policy and Management (ESHPM), Rotterdam, Netherlands
Innovative causal inference methods are needed for the design and analysis of big real-world observational data and pragmatic trials used for outcomes research and health technology assessment. This course will introduce the principles of causation in comparative effectiveness research, the use of causal diagrams (directed acyclic graphs; DAGs) and focus on causal inference methods for time-independent confounding (multivariate regression, propensity scores) and time-dependent confounding (g-formula, marginal structural models with inverse probability of treatment weighting, and structural nested models with g-estimation). The “target trial” concept and a counterfactual approach with “cloning – censoring – weighting” will be used to apply causal methods to big real-world datasets with case examples from oncology, cardiovascular disease, HIV, nutrition and obstetrics. The course consists of lectures, practical training exercises, case examples drawn from the published literature which will be used to comprehend the use of causal inference in health technology assessment bodies (eg, NICE , IQWiG), and an interactive discussion with Q&A. The intended audience includes all stakeholders in medical and public health decision making and healthcare, and researchers from all substance matter fields, statisticians, epidemiologists, outcome researchers, health economists, modelers and health policy decision makers interested either in methods of causal analysis or causal interpretation of results based on the underlying method. Course material includes all session handouts, exercises with solutions, a comprehensive background reading library, and software recommendations.
PREREQUISITE: Students are expected to have a basic knowledge in epidemiologic studies and methods (including the concept of confounding).
Douglas E. Faries, PhD
Eli Lilly and Company, Indianapolis, IN, USA
Felicitas Kuehne, MSc
Pfizer Pharma GmbH, Berlin, 0, Germany