Short Courses

All short courses require a separate registration fee.

* Program subject to change

Sun 17 Nov8:00 - 12:00Short Course Morning SessionsValuation of Innovative DrugsLevel: IntermediateTrack: Health Policy & Regulatory

Separate registration required.

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.

Faculty Member

Afschin Gandjour, MD, PhD, MA, MBA

Frankfurt School of Finance & Management, Frankfurt, Germany

Afschin Gandjour is a medical doctor, health economist, and philosopher. His research focuses on cost-effectiveness analysis, decision modeling, and value-based pricing of pharmaceuticals. Gandjour received an MD from Hannover Medical School in Germany, an MBA from Duke University, a PhD in health economics from the University of Cologne in Germany, and an MA in philosophy from the University of Düsseldorf in Germany. He held faculty positions at the University of Cologne Medical School, Baylor College of Medicine, and Louisiana State University Pennington. In terms of research productivity, he ranks among the top professors in business administration in Germany (#20 in 2022 based on Wirtschaftswoche magazine).

Lou Garrison, PhD

University of Washington, Seattle, WA, USA

Lou Garrison, PhD, is Professor Emeritus in The Comparative Health Outcomes, Policy, and Economics Institute in the School of Pharmacy at the University of Washington, where he joined the faculty in 2004. Dr. Garrison has worked in non-profit, industry, and academic settings. He received a PhD in Economics from Stanford University and has more than 200 publications in peer-reviewed journals. Dr. Garrison was elected as ISPOR President for 2016-2017, and currently serves as co-chair of ISPOR’s Policy Outlook Committee for the Health Science Policy Council. In September 2022, he was announced as the recipient of the 2022 Avedis Donabedian Outcomes Research Lifetime Achievement Award from ISPOR.

Marlene Gyldmark, MPhil

Idorsia Pharmaceuticals, Allschwill, Switzerland

Marlene is a senior leader with several years’ experience in health service research, academia, and life science industry from both developing as well as developed countries. Work experience includes building state-of-the art HEOR teams at Idorsia AG, Switzerland, at Roche Diabetes Care, Switzerland, and Roche Pharma global headquarters in Switzerland, as well as holding various positions within the field of Market Access, Pricing and HEOR at Roche Pharma, Novo Nordisk, and Pfizer, Denmark. Prior to the experience in industry, Marlene worked for more than 7 years in health service research in Denmark, Nepal and Tanzania. In addition, Marlene also has many years of experience as an external lecturer in health economics at Copenhagen University, as well as leading an executive course in “Market Access for Pharmaceuticals” under Copenhagen University life-long learning. Marlene’s interest from her early days at York University is to strengthen and support good decision making in healthcare and to foster collaboration and solutions across all stakeholders in the healthcare sector. Marlene is a member of the Board of Directors at ISPOR, and also supports the patient organization Institute of Neurodiversity (ION). Marlene is educated as Cand Polit from Copenhagen University, and Health Economist from University of York, UK.

Mark Nuijten, MBA, PhD, MD

Ben Gurion University, Be'er Sheva, Israel

Mark Nuijten is a medical doctor, health economist, valuation economist, and healthcare publicist. He is a visiting professor at Ben-Gurion University in Israel, setting up the department on Clinical and Economic Valuation of Medical Innovation. He has become a leading health policy and economics expert over the last 2 decades, reflected in more than 200 publications and leading positions in scientific societies and editorial boards. Dr. Nuijten was board director of ISPOR (2002-2004) and chair of the Management Board of Value in Health (2002-2004). He was a member of the Editorial Advisory Board of Value in Health. He obtained his PhD in health economics (2003) on the thesis “In search for more confidence in health economic modelling” at the Erasmus University, Rotterdam. Mark Nuijten is founder of A2M (Ars Accessus Medica) and founding partner of the Minerva International Health Economic Network. He was trained as a physician and worked in clinical research before obtaining his international MBA from Erasmus University, Rotterdam, where he later was a senior staff member. Prior to setting up Ars Accessus Medica, Dr. Nuijten was the founding managing director of the IQVIA Quintiles office in the Netherlands, which included European responsibility for the policy and health economic division. He is a pioneer in the field of healthcare innovation in biotechnology and has been the first classical health economist successfully applying and developing Discounted Cash Flow methodologies for valuation of biotechnology innovation (eg, a pricing model to assess prices of expensive orphan drugs from an investor’s perspective—published in a Nature journal). He also developed an integrated valuation model, an interactive dynamic tool for the economic valuation of R&D projects, which can be used to optimize the initial clinical program (eg, indication, comparator, outcomes, and study design), and the associated pricing and market access pricing strategy.

Fred W. Sorenson, MSc

Cencora, Basel, Switzerland

Fred Sorenson is assistant director, Global Health Economics, Outcomes Research (HEOR) & Market Access at Xcenda, the consulting division of AmerisourceBergen where his work includes comparative effectiveness research, retrospective database studies, prospective studies and chart reviews, systematic literature reviews, global value dossiers and contributing to posters and publications. Mr. Sorenson also has experience, most predominantly in Europe, in developing strategy and gathering payer insights through payer interviews and advisory panels to inform HEOR initiatives and guide market access at the affiliate level. Mr. Sorenson has more than 35 years of experience in the healthcare industry, working in numerous therapeutic areas including cardiology, endocrinology, dermatology, oncology, neurology, autoimmune diseases, virology, and infectious disease. Fred received his BS in both Psychology and Philosophy from the University of Southern Colorado and did his post-graduate studies in Sociology and Economics (master’s thesis on “Registration of Vital Birth Statistics in Nepal”) at the University of Basel in Switzerland.

Introduction to Applied Generative AI for HEORLevel: IntroductoryTrack: Methodological & Statistical Research

Separate registration required.

The rapid advancement in generative artificial intelligence (Gen AI) presents an opportunity for transformative potential in the field of Health Economics and Outcomes Research (HEOR). This course provides an introductory understanding of generative AI models with a particular focus on large language models (LLMs), which are revolutionizing the field of HEOR. Participants will be provided with an overview of the most appropriate ways to access LLMs, going beyond the use of chatbots. Further, they will be given insights into how to use prompt engineering to conduct scientific research and gain an understanding on issues pertaining to privacy and security when using Gen AI for HEOR. Participants will further explore specific applications of these models for conducting robust scientific HEOR research in systematic literature review (SLR), real-world evidence analysis, and economic evaluation. The course aims to equip participants with the knowledge to begin to use generative AI techniques for specific HEOR contexts and to appreciate how these innovative approaches can enhance HEOR activities. Practical exercises using Python and relevant AI frameworks will be incorporated for participants to follow along. Participants who wish to gain hands-on experience are required to bring their laptops with Python installed.

PREREQUISITES: Students should have a general understanding of common HEOR concepts such as SLRs and cost-effectiveness models. Knowledge of Python or similar programming languages such as R is considered a benefit, but not required.

Faculty Member

Sven Klijn, MSc

Bristol Myers Squibb, Utrecht, ZH, Netherlands

Sven Klijn is Director at Bristol Myers Squibb in the Global HEOR Economic & Predictive Modeling group, where he leads the innovative modeling agenda in hematology and cell therapy. In addition, Sven has an active role in providing modeling education and masterclasses at international congresses. He has widely published on innovative methods, especially in the field of survival extrapolation and Generative AI. Sven has a training in public health and health economics and previously had various roles in CROs related to health-economic modeling.

William Rawlinson, MPhysPhil

Estima Scientific Ltd, London, United Kingdom

Will has a depth of experience in conceptualising, building, and validating health economic models, having worked in HEOR consultancies in both the private and public sector. His central motivation is to enhance the efficiency and quality of health economic modelling through technology. In his current role, he utilises his background in health economics as well as a keen interest in programming to develop novel AI-based applications for HEOR.

Tim Reason, MSc

Estima Scientific Ltd, South Ruislip, LON, United Kingdom

Tim Reason is co-founder of Estima Scientific and specialises in AI and evidence synthesis, having spent 15 years in the field of HEOR and technology. Tim is Managing Director of Estima, driving business activities, innovation and strategy for the company. Tim’s specialises in the intersection of HEOR, software development and AI to drive better outcomes for patients. Tim is the lead author on two seminal papers in AI for HEOR, showing that AI can be used to automate health economic modelling and NMA.

Core Concepts and Approaches to Early Stage Health Technology AssessmentLevel: IntroductoryTrack: Health Technology Assessment

Separate registration required.

As the cost of bringing a new health technology to market continues to climb, more and more firms, developers, and investors are searching for tools to prioritize their efforts on the technologies with the greatest potential for clinical impact and market viability. While health economic analysis has long been established as a necessity to inform decision making for market access and reimbursement, it is increasingly being used at earlier stages of product development for healthcare and life sciences to increase the access rate of R&D and efficiently prioritize data collection. The number of available methods for this field has continued to expand.

This course aims to demystify the objectives of early-stage health technology assessment and the methods of translational health economics. Students in the course will gain a thorough understanding of available methods for early-stage technology assessment, the specific challenges and solutions, and a clear sense of how to implement this in the complexity of health technology development, funding, regulation, pricing, and reimbursement. The course will utilize real-world examples and students will have the opportunity to strategize about the creation of a research plan for their purposes.

PREREQUISITE: Familiarity with key elements, methods, language, and basic health technology principles are prerequisites to attending this course.

Faculty Member

William Canestaro, PhD, MSc

Washington Research Foundation, Seattle, WA, USA

Will Canestaro is a venture capitalist, health economist and meta-epidemiologist with a demonstrated history of working in the biotechnology industry. He is currently the managing director of the Seattle-based Washington Research Foundation—a foundation operating at the intersection of academic research, technology development, public and private investment, philanthropy, and industry. Will manages the Foundation’s biotech and engineering VC investment portfolio. Through the WRF, he has made 25 investments in 19 Washington-based companies. Will also teaches courses at the UW School of Business and School of Pharmacy on Health Innovation and Meta-Analytic methods, respectively. Will obtained his PhD in Health Economics and Outcomes Research from the University of Washington and has a MSc in Medical Anthropology from the University of Oxford.

Erik Landaas, PhD, MPH

W. L. Gore & Associates, Inc., Flagstaff, AZ, USA

Erik Landaas, PhD, MPH Senior Manager-Global Market Access & Value Strategy W. L. Gore & Associates Erik is a doctoral level health economics outcomes research (HEOR) professional with extensive experience in reviewing new and emerging medical technologies. He currently leads global market access initiatives at W. L. Gore & Associates, where he is responsible for early-HTA, value proposition development, economic studies, global evidence development, value dossiers, and market access strategies. Prior to joining W. L. Gore & Associates, Erik developed a new Health Economics team at Cook Medical, where he integrated health economics into strategic business functions to add value to Cook's products. He introduced and consulted on HEOR and market access projects for pre- and post-launch technologies, advising on new medical innovations rising out of start-ups and consulting with Pharma and Device companies to optimize uptake and adoption in the US and global markets. Erik spent five years at UW Medicine in Seattle, where he developed and led a new hospital-based HTA program that included four large hospitals and their affiliates. He conducted hospital-based HTAs for devices and diagnostics and established a comprehensive decision framework for the hospital to make more informed technology adoption decisions. Before joining UW Medicine, Erik worked in health insurance in Washington State, where he developed and led a new HTA process for a group of public payers, including pricing, fee schedule, clinical guidelines, and payment policy. Erik holds a PhD in HEOR from the Comparative Health Outcomes, Policy and Economics (CHOICE) Institute at the University of Washington's School of Pharmacy, as well as an MPH from the University of Washington's School of Public Health. With his advanced skills and extensive experience, Erik is a valuable asset to any organization seeking to optimize market access and adoption of new medical technologies.

Lotte Steuten, MSc, PhD

Office of Health Economics, London, LON, United Kingdom

Lotte Steuten, PhD, is deputy chief executive of the not-for-profit Office of Health Economics (OHE) and the scientific and business lead for its international research-led work program—maintaining its reputation for objective, innovative, and high-quality research, and meeting its charitable goals. She is also an honorary visiting professor at City, University of London (UK) contributing to its teaching programs in health economics. For more than 20 years, Lotte has worked in academic, research, and consulting HEOR roles with one focus—improving healthcare decision-making through high-quality research and analysis. To achieve that, she has collaborated with HTA authorities, pharmaceutical and medical device companies, patients, payers, policymakers, academic researchers, clinical specialists, and venture capitalists. She has gained broad HEOR expertise of various technologies: precision medicine, oncology, vaccines, antibiotics, diagnostics, digital health, and medical devices. Lotte’s career has been based in the United States, the Netherlands, and the UK. Collaborations with HEOR experts in Southeast Asia, Africa, and Latin America have provided her with deep insights into the differences and commonalities between the role of HEOR in different healthcare systems, cultures, and societies. With this background, she has: led diverse international teams; developed strategies and delivered on program and organizational missions, values, and objectives; acquired research funding and managed budgets; and been responsible for executive decision-making as well as legal and fiduciary matters. As an active ISPOR member since 2004, Lotte has served on various task forces and committees, delivered short courses, was co-chair of ISPOR EU 2021, and is currently a member of the Board of Directors, and an associate editor for Value in Health. She aims to be a role model for diversity and played an active role in the “Women in HEOR” initiative. Before joining OHE, Lotte worked in Seattle (US) as an associate professor at the University of Washington and at the Fred Hutch Cancer Research Center. She graduated cum laude with her PhD from Maastricht University (NL) and then worked at Brunel University (UK) and Twente University (NL).

Causal Inference and Causal Estimands From Target Trial Emulations Using Evidence From Real-World Observational Studies and Clinical TrialsLevel: ExperiencedTrack: Real World Data & Information Systems

Separate registration required.

In recent years, real-world evidence (RWE) has been increasingly used to inform regulatory, payer, and health technology assessment (HTA) decisions, as well as clinical guideline development. In addition, it has been recognized that the analysis of hypothetical estimands in clinical trials is necessary when the standard intention-to-treat (ITT) analysis does not answer the decision problem, usually because of treatment switching. An innovative framework for causal inference methods, target trial emulation, causal estimands and causal modeling guides the design and analysis of observational studies and clinical trials. This course will (1) introduce causal principles, causal diagrams (directed acyclic graphs; DAGs), and target trial emulation to avoid self-inflicted biases (e.g., time-zero bias, immortal time bias), (2) provide an overview of causal methods for baseline confounding (multivariate regression, propensity scores) and time-varying confounding (e.g., g-formula, marginal structural models with inverse probability of treatment weighting, and rank-preserving structural failure-time models with g-estimation), (3) propose appropriate estimands to ensure decision problems are directly addressed when analyzing observational data or data from clinical trials affected by treatment switching, (4) present lessons learned from applied case examples in HTA, such as single arm-trials with external control arms or trials affected by treatment switching, (5) provide recommendations regarding the use of causal inference methods and estimands and their application in causal modeling, and (6) discuss acceptance and barriers from an HTA agency perspective. The target audience includes all stakeholders and researchers from all fields in health and healthcare.

PREREQUISITE: Students are expected to have a basic knowledge in epidemiologic studies and methods (including the concept of confounding).

Faculty Member

Felicitas Kuehne, MSc

UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Innsbruck, Austria

Felicitas Kuehne is a senior scientist in decision-analytic modeling and epidemiology at UMIT TIROL. She is leading the Program on Causal Inference in Science and is the Coordinator of the HTADS course “Causal Inference for Assessing Effectiveness in Real World Data and Clinical Trials”. She conducts decision-analytic as well as epidemiological studies in several disease areas and teaches epidemiology classes. Her research interests include evaluating public health intervention by applying evidence-based quantitative methods from epidemiology, comparative effectiveness research, health services and outcomes research, economic evaluation, and decision sciences. Her current substantive research focuses on identifying synergies of causal inference and decision science.

Nicholas R Latimer, PhD, MSc

University of Sheffield & Delta Hat Limited, Sheffield, United Kingdom

Nick is a Professor of Health Economics at the University of Sheffield, a Yorkshire Cancer Research Senior Fellow, and an Analyst for Delta Hat Ltd. He was a member of NICE Technology Appraisal Committee B for 5 years and is a member of the NICE Decision Support Unit (DSU). Nick’s research focuses on survival analysis in economic evaluations. He has authored four DSU technical support documents related to survival analysis, treatment switching, and partitioned survival models. His current work involves investigating the use of cancer registry datasets to estimate the comparative effectiveness of cancer treatments used in clinical practice.

Uwe Siebert, MD, MPH, MSc, ScD

UMIT TIROL - University for Health Sciences and Technology Hall in Tirol, Austria and Harvard Chan School of Public Health Harvard University, Hall in Tirol, Austria

Uwe Siebert, MD, MPH, MSc, ScD, is a Professor of Public Health, Medical Decision Making and Health Technology Assessment (HTA), Chair of the Department of Public Health, Health Services Research and HTA at UMIT TIROL - University for Health Sciences and Technology in Austria and Director of the Division for HTA in the ONCOTYROL–Center for Personalized Cancer Medicine in Austria. He is also Adjunct Professor of Epidemiology and Health Policy & Management at the Harvard T.H. Chan School of Public Health and Affiliated Researcher in the Program on Cardiovascular Research at the Institute for Technology Assessment and Department of Radiology at the Massachusetts General Hospital, Harvard Medical School, Boston. After medical school, he worked for several years as a physician in international public health projects in West Africa, Brazil, and Germany. He then earned an MPH at the Munich School of Public Health and completed an MSc in Epidemiology and a ScD in Health Policy and Management with a concentration in Decision Sciences at the Harvard School of Public Health. His research interests include applying real-world evidence-based quantitative, causal and translational methods from public health, epidemiology, artificial intelligence, comparative effectiveness research, health services and outcomes research, economic evaluation, modeling, and health data a d decision science in the framework of health care policy advice and HTA as well as in the clinical context of routine health care, clinical guideline development, public health policies and patient guidance. His research focuses on cancer, infectious disease, cardiovascular disease, neurological disorders, and others. He has been leading projects/work packages in several EU FP7, H2020 and Horizon Europe projects (eg, ELSA-GEN, BiomarCaRE, MedTecHTA, DEXHELPP, EUthyroid, FORECEE, MDS-RIGHT, RECETAS, CORE-MD, EUREGIO-EFH, CIDS, OnCoVID, 4D PICTURE, CATALYSE, EUCAPA, PREMIO COLLAB). He teaches HTA, health economics, modeling, epidemiology, causal inference and target trial emulation, and data and decision science for academia, industry, and health authorities in Europe, North and South America, and Asia. He directs the Continuing Education Program on Health Technology Assessment & Decision Sciences (htads.org). He has served as member of the ISPOR Directors Board and as president of the Society for Medical Decision Making (SMDM). He is a leadership member of the ISPOR Personalized/Precision Medicine SIG, a member of the Latin America Consortium Advisory Committee of ISPOR, and co-chair of the ISPOR-SMDM Modeling Good Research Practices Task Force. He is a member of the Oncology Advisory Council and the National Committee for Cancer Screening of the Austrian Federal Ministry of Health. He has authored more than 400 publications (>30,000 citations, H index >80), and is editor of the European Journal of Epidemiology. Further information Internet: http://htads.org, umit-tirol.at/dph, hsph.harvard.edu/uwe-siebert, Twitter: @UweSiebert9, LinkedIn: uwe-siebert9.

Real-World Evidence in External Control Arms: Driving Innovation in Drug DevelopmentLevel: ExperiencedTrack: Real World Data & Information Systems

Separate registration required.

Gain new skills in the use of real-world evidence (RWE) in external control arms (ECAs) – an advanced application that has become critical in drug development. During this course you will learn more about the history and regulatory landscape of the application of ECAs and how to analyze the current regulatory and payer landscape for ECA acceptance and application in drug development and market access. You will explore the relevant data sources and sourcing methodologies for ECAs and learn how to develop external control arms using synthetic data and data from multiple data sources. End the course by designing and developing an external control arm with your peers.

PREREQUISITE: To get the most out of the course, students should be familiar with basic definitions and concepts of RWE including regulatory applications.

Faculty Member

Ulka Campbell, PhD, MPH

Aetion, Inc, New York, NY, USA

Ulka Campbell is an epidemiologist and the Head of Scientific Strategy at Aetion (a healthcare technology and research services company) providing methods and regulatory support across therapeutic areas and leading research to inform regulatory RWE best practices. Previously, she was at Pfizer for 14 years leading regulatory studies and serving as the Head of Safety Surveillance Research, overseeing a team responsible for post-approval safety studies obligated to FDA and EMA. She has co-authored several publications and taught courses on pharmacoepidemiology, standards for decision-grade real world studies, causal inference, and epidemiologic methods, and is an Adjunct Assistant Professor of Epidemiology at Columbia University. She received an MPH from the University of Michigan and a PhD from Columbia University in epidemiology.

Doug Foster, MBA

Advanced Data Sciences, San Francisco, CA, USA

Doug Foster is a Partner at Advanced Data Sciences (ADS), an advisory firm focused exclusively on the use of healthcare data for research and applications. He has spent over twenty-five years in healthcare innovation. Previously, Doug was Co-founder, Chief Executive Officer, and Chief Strategy Officer at Verana Health a real-world evidence company. Before this, Doug was Managing Director at Longitude Capital, where his focus was on early to late-stage venture capital investments in the life sciences industry.

Aaron Kamauu, MPH, MS, MD

Navidence LLC, Bountiful, UT, USA

Leanne Larson, MHA

ZS Associates, Wilmette, IL, USA

Health Economic Modeling in R: A Hands-On IntroductionLevel: IntroductoryTrack: Economic Evaluation

Separate registration required.

This highly practical course will outline the computational and transparency advantages of using R, for those used in 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.

Faculty Member

Gianluca Baio, PhD

University College London, London, United Kingdom

Gianluca Baio is a professor of Statistics and Health Economics in the Department of Statistical Science at University College London (UK). Gianluca graduated in Statistics and Economics from the University of Florence (Italy). He then completed a PhD programme in Applied Statistics again at the University of Florence, after a period at the Program on the Pharmaceutical Industry at the MIT Sloan School of Management, Cambridge (USA); he then worked as a Research Fellow and then Temporary Lecturer in the Department of Statistical Science at University College London (UK). Gianluca's main interests are in Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in the health systems, hierarchical/multilevel models and causal inference using the decision-theoretic approach. Gianluca leads the Statistics for Health Economic Evaluation research group within the department of Statistical Science and he is the co-director of UCL MSc Programme in Health Economics and Decision Science.

Rose Hart, PhD

Lumanity Inc., Sheffield, United Kingdom

Rose Hart is a senior health economist at Lumanity, where she has been part of the modeling team for over 5 years with a focus towards advanced analytics and strategic solutions. She is experienced in developing health economic models for early-phase cost effectiveness and for health technology assessment. As a specialist in R modelling, with and without Shiny interface functionality, she is responsible for internal Shiny training and is a development leave in expanding Lumanity’s intRface™ services. Rose previously worked in the BioPharm team at GlaxoSmithKline and researched for her PhD at the University of Sheffield.

Felicity Lamrock, PhD

Queens University Belfast, Belfast, ANT, United Kingdom

Dr. Felicity Lamrock is a Lecturer in Data Analytics at Queen’s University Belfast. She is currently the Director of the Northern Ireland Centre for Health Analytics and Decision Science (NI-CHADS) with a focus on the analysis of health data for decision modelling. Current projects include a range of disease areas including cancer, rare diseases, diabetes, COVID-19, and cardiovascular disease. Felicity was previously a statistician at the National Centre for Pharmacoeconomics (NCPE) working with a team of pharmacists and clinicians on Health Technology Assessments to advise the Health Service Executive on the recommendation of new drug therapies in Ireland. She remains involved with NCPE as a statistical advisor and is exploring how Northern Ireland could benefit from more decision modelling/pharmacoecononomic assessment.

Howard Thom, MSc, PhD

University of Bristol, Bristol, United Kingdom

Howard Thom is a Senior Lecturer in Health Economics at the University of Bristol and Senior Director at the consultancy Clifton Insight. His research interests are value of information, uncertainty in economic models, network meta-analysis, and R for health economics.

Developing Decision-Grade Real-World EvidenceLevel: IntermediateTrack: Real World Data & Information Systems

Separate registration required.

In this course, participants will be introduced to the principles of what makes real-world evidence (RWE) decision-grade, including an extended example. In the first half of the course, will review the most recent RWE frameworks and guidelines, and examine case studies in which RWE was used in regulatory and HTA approval. The second half of the course is an extended example in which participants will examine a study that could support an indication expansion, and interactively discuss how choices made in the design and implementation may affect the meaning and interpretability of results.

PREREQUISITE: Students are expected to be familiar with relevant concepts and methodologies for analyzing real-world data.

Faculty Member

Jeremy Rassen, ScD

Aetion, Inc., New York, NY, USA

Jeremy A. Rassen, MS, ScD is a pharmacoepidemiologist with 25 years of academic and industry experience. He is co-Founder, president, and chief technology officer at Aetion, a healthcare technology company that delivers real-world evidence for life sciences companies, payers, and regulatory agencies. Prior to founding Aetion, Dr. Rassen was assistant professor of medicine at Harvard Medical School, where he focused on methods to improve the quality and validity of real-world data studies. He also worked in Silicon Valley in a variety of tech companies. Dr. Rassen received his bachelor’s degree in computer science from Harvard College and his master’s and doctorate degrees in Epidemiology from the Harvard T.H. Chan School of Public Health.

Sebastian Schneeweiss, MD, ScD

Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Sebastian Schneeweiss, MD, ScD, is a Professor of Medicine and Epidemiology at Harvard Medical School and Chief of the Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women’s Hospital. His research focuses on assessing the effectiveness and safety of biopharmaceuticals in clinical practice. He has developed analytic methods to improve the accuracy of estimating causal treatment effects of new drugs using complex digital healthcare databases. His work is published in >500 articles and is used for regulatory and coverage decision making around the globe. He is funded by NIH, PCORI, IMI, and FDA where he is also a voting consultant. He is Principal Investigator of the FDA Sentinel Innovation Center and co-leads the RCT-DUPLICATE initiative to understand when and how real-world evidence studies can reach causal conclusions.

Shirley Wang, PhD

Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA

Dr. Wang is an associate professor at Brigham and Women’s Hospital, Harvard Medical School, lead epidemiologist for the Food and Drug Administration’s (FDA) Sentinel Innovation Center, and member of the PCORI Methodology Committee. She leads the Meta-Research in Pharmacoepidemiology program, with recent projects aimed at improving the transparency, reproducibility and robustness of evidence from healthcare databases (www.repeatinitiative.org) and informing when and how real-world evidence studies can draw causal conclusions to inform regulatory or other healthcare decision-making (www.rctduplicate.org). She is currently PI on multiple NIH R01s and is also funded by FDA. Her methods work has received multiple awards from international societies.

8:00 - 17:00Short Course Full Day SessionIntroduction to Health Economics and Outcomes ResearchLevel: IntroductoryTrack: Economic Evaluation

Separate registration required.

This course is designed to teach clinicians and new researchers how to incorporate health economics into study design and data analysis. Participants will first review the basic principles and concepts of health economic evaluations, then discuss how to collect and calculate the costs of different alternatives, determine the economic impact of clinical outcomes, and how to identify, track, and assign costs to different types of healthcare resources used. Different health economic models and techniques will be demonstrated including cost-minimization, cost-effectiveness, cost-benefit, cost-utility, and budget impact analysis. Decision analysis, sensitivity analysis, and discounting will all be demonstrated and practiced. This course is suitable for those with little or no experience with health economics.

Faculty Member

Lorne Basskin, PharmD

Strategic Economics Ltd., Cary, NC, USA

Lorne Basskin received his Bachelor's degree in business at the University of Toronto and worked as an accountant with a national firm and subsequently developed his own consulting business. He later received his PharmD from the University of the Pacific and completed a post-doctoral residency with Valley Medical Center in Seattle in 1995. Dr. Basskin went on to serve as an Associate Professor in Clinical Practice at two different schools of pharmacy in the United States, and was the Director of Post-Graduate and Continuing Education there for seven years. In 2002, Dr. Basskin started his own Medical Education firm, specializing in education for pharmacists and other health care professionals. From 2005 to 2011 he held various positions with HealthSouth Corporation, a for-profit group of 100 rehabilitation and long-term acute care hospitals. Most recently he served as their National Director of Pharmacy Clinical and Information Services and was involved in implementing the Cerner system of electronic medical records throughout the network. After a two year period with Wingate University College of Pharmacy as their Regional Dean, he moved to Asheville, NC, where he carries on a consulting practice specializing in education about health care technology and outcomes research. Dr. Basskin has written and spoken extensively on the topics of Pharmacoeconomics and Outcomes Research, and published a book on the topic in 1998. He has over 40 peer-reviewed publications and has made over 120 presentations on both clinical and research related matters. He is on the Editorial Board of several National Pharmacy and Medical publications and serves as a peer reviewer for several professional organizations. He has written continuing education programs including that of diabetes, asthma, and Medicare reimbursement. Additionally, he has conducted over 100 one and two day workshops on the meaning and use of pharmacoeconomics for health care decision makers and health care researchers.

Karen L Rascati, PhD, RPh

The University of Texas at Austin, Austin, TX, USA

Karen L Rascati, PhD, is professor emeritus of pharmacy at The University of Texas College of Pharmacy. Dr. Rascati has supervised 37 PhD and 58 MS graduate student projects. She has authored or co-authored more than 200 publications (including many textbook chapters and three editions of her textbook Essentials of Pharmacoeconomics) and 280 national/international meeting presentations. Her research interests include economic and outcomes evaluations for several disease states as well as for pharmacy services. She has conducted over 60 funded research projects and served on various grant review panels. She is a fellow of the American Pharmacist Association (FAPhA), has served as president of the Rho Chi National Pharmacy Honor Society, and on the Board of Directors (treasurer) of ISPOR. She currently teaches in the on-line MS program at the University of Florida and serves as an assistant editor for the Journal of Managed Care & Specialty Pharmacy (JMCP).

Reimbursement Systems for Pharmaceuticals in EuropeLevel: IntermediateTrack: Health Policy & Regulatory

Separate registration required.

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, learn about 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.

Faculty Member

Mondher Toumi, MD, PhD, MSc

Laboratoire de Santé Publique Aix-Marseille University, Marseille, France

13:00 - 17:00Short Course Afternoon SessionsApplied Cost-Effectiveness Modeling With RLevel: IntermediateTrack: Methodological & Statistical Research

Separate registration required.

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.

Faculty Member

Devin Incerti, PhD

EntityRisk, Inc., San Francisco, CA, USA

Devin Incerti is the head of Data Science at EntityRisk with experience spanning health economics, biostatistics, and software engineering. Previously, Dr. Incerti was a Principal Data Scientist at Genentech working on statistical methodology for real-world and observational data. In his research, he developed approaches for causal inference in hybrid and external control studies and for prediction modeling with linked clinical and genomic data. Prior to Genentech, he was a senior economist at Precision Health Economics and the lead economist for the Open-Source Value Project at the Innovation and Value Initiative (IVI), where he performed research and developed software related to the value of medical technologies. He has developed software in a variety of languages (eg, Python, R, C++) and is an active contributor to the open source and health economics communities, including the development of hesim—a software tool for cost-effectiveness modeling and analysis. Dr. Incerti received BA degrees in Mathematics/Economics and Political Science from the University of California, San Diego, and a PhD in Public Policy from Princeton University.

Jeroen Jansen, PhD

Department of Clinical Pharmacy, School of Pharmacy, University of California – San Francisco, USA; PrecisionHEOR, Oakland, CA, USA

Jeroen is a methodologist working at the intersection of evidence synthesis, biostatistics, and health economics. He has worked on treatment sequence models as part of the Innovation and Value Initiative. He is an Associate Professor in the Department of Clinical Pharmacy in the School of Pharmacy at the University of California, San Francisco. He also acts as Chief Scientist - Health Economics & Outcomes Research (HEOR) at the Precision Medicine Group.

AI-Powered HEOR: Advancing Insights and Decisions With Large Language ModelsLevel: IntermediateTrack: Study Approaches

Separate registration required.

This course delves into the transformative role of Generative AI, particularly large language models (LLMs), in enhancing key areas of HEOR such as systematic literature reviews (SLR) and real-world evidence synthesis, economic modeling, regulatory decision-making, and the development of external control arms.

Participants will learn to apply Generative AI technologies to conduct comprehensive and efficient systematic literature reviews, synthesize evidence at scale, and construct robust economic models that support healthcare policy and market strategies. The course also covers the strategic use of AI in developing external control arms, essential for clinical trials and regulatory submissions, thereby improving the quality and speed of healthcare decisions.

This program is ideal for HEOR data analysts, outcome researchers, epidemiologist, health economists, regulatory affairs professionals, and anyone in all substance matter fields involved in the planning and implementation of healthcare strategies. Through a combination of expert lectures, hands-on exercises, and case study analyses, attendees will gain practical skills and insights into leveraging AI to streamline research processes, enhance data analysis, and forge ahead in the dynamic field of healthcare research and policy. Participants are required to bring a laptop with the capability to connect to Wi-Fi and sufficient processing power to handle basic analytical tasks. For conducting SLR, participants may need access to databases such as PubMed, Cochrane Library, or Embase.

PREREQUISITE: Students are expected to have a basic knowledge of HEOR concepts, health data analysis, research methods, and interest in artificial intelligence.

Faculty Member

Turgay Ayer, PhD

Georgia Institute of Technology, Atlanta, GA, USA

Turgay Ayer, PhD, stands at the forefront of innovative healthcare solutions as the Chief Scientific Officer at Value Analytics Labs. Under Dr. Ayer's guidance, Value Analytics Labs has developed ValueGen.AI, a revolutionary generative AI-based platform designed to transform market access and Health Economics and Outcomes Research (HEOR). This platform exemplifies Dr. Ayer's commitment to leveraging advanced data science and machine learning techniques to drive real-world evidence and health economics modeling. Beyond his role as a Chief Scientific Officer, Dr. Ayer is a distinguished academic, serving as a professor and the research director for Healthcare Analytics and Business Intelligence in the Center for Health & Humanitarian Systems at Georgia Tech. He also holds a courtesy appointment at Emory Medical School and is a senior scientist at the CDC. His academic work, deeply rooted in health economics, real-world evidence, data science, and machine learning, has led to the publication of over 75 journal papers and more than 250 conference abstracts, particularly enriching the field of oncology. Dr. Ayer's contributions have not only been recognized in top-tier academic journals but have also captured the attention of major media outlets such as the Wall Street Journal, Washington Post, U.S. News, and NPR, highlighting his impact on both academic and practical aspects of healthcare analytics.

Jagpreet Chhatwal, PhD

Harvard Medical School, Boston, MA, USA

Dr. Chhatwal is an associate professor at Harvard Medical School and director of the Institute for Technology Assessment at Massachusetts General Hospital. His research is centered in data/decision analytics, AI, and health economics. Dr. Chhatwal has co-authored over 120 original research articles and editorials in peer-reviewed journals. His work has been cited in leading media outlets, including CNN, Forbes, National Public Radio, New York Times, and Wall Street Journal. Since 2011, Dr. Chhatwal has taught several workshops and short courses on decision modeling at the annual meetings of the Society for Medical Decision Making and the ISPOR. He serves as an associate editor of Value in Health. He recently served as the guest editor for special issues on the opioid crisis and COVID-19 published in Value in Health. He is a co-chair of 2024 SMDM meeting, whose theme is "The future of medical decision making in the era of generative AI".

Xiaoyan Wang, PhD

Tulane Univeristy, New Orleans, LA, USA

Dr. Xiaoyan Wang is a professor at Tulane University and Chief Scientist of IMO Health. She focuses on translating scientific insights and health intelligence into partnerships and programs that improve healthcare outcomes for patients, providers, and researchers. Dr. Wang has spent two decades committed to driving innovations in healthcare AI modeling and transforming health care with data intelligence. She published hundreds of papers in NLP, AI modeling and data intelligence in health care and life sciences. She was most recently VP of Healthcare Analytics and Informatics and VP of Biopharma Solutions at GeneDx Mount Sinai Genomics Inc, where she led the development of clinical evidence generation platforms and clinical research in oncology, immunology, cardiovascular, respiratory, and rare diseases. Before joining the biopharma industry, she was a faculty member and principal investigator at the University of Connecticut, UConn Health Center, and Mount Sinai Health Systems bridging research, health services, and teaching. She holds a Ph.D. in Biomedical Informatics and NLP from Columbia University School of Medicine.

Hua Xu, PhD

Yale University School of Medicine, New Haven, CT, USA

Dr. Hua Xu is Robert T. McCluskey Professor and Vice Chair for Research and Development, Section of Biomedical Informatics and Data Science at Yale School of Medicine (YSM), as well as Assistant Dean for Biomedical Informatics at YSM. He received his Ph.D. in Biomedical Informatics from Columbia University. His primary research interests include biomedical natural language processing (NLP) and data mining, as well as their applications in secondary use of electronic health records data for clinical and translational research. His research is funded by multiple agencies (i.e., NLM, NCI, NIGMS, NIA, AHA, and CPRIT), and methods/tools developed in his lab have been top ranked in a number of biomedical NLP shared tasks and widely used to support diverse biomedical applications. He served as the Chair of American Medical Informatics Association (AMIA) NLP Working Group and now leads the Observational Health Data Sciences and Informatics (OHDSI) NLP Working Group. Dr. Xu is a fellow of both the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).

Health Technology Assessment for Rare DiseasesLevel: IntermediateTrack: Health Technology Assessment

Separate registration required.

This short course is designed for professionals with a basic understanding of health technology assessment (HTA) who seek to deepen their knowledge and skills in evaluating interventions for rare diseases. Participants will gain an appreciation of the unique challenges that rare diseases pose for HTA due to limited evidence, small patient populations, and high treatment costs. A case study from Europe will be shared. The session will close with a discussion on the methodological challenges of HTA in rare disease.

Faculty Member

Josh Byrnes, PhD, MHEcon, BEcon

Griffith University, Brisbane, QLD, Australia

Professor Josh Byrnes has worked as an academic and consulting health economist since 2011 after he was awarded his PhD in Health Economics from The University of New South Wales. Joshua’s research expertise centers on the design and analysis of studies for the purpose of measuring and demonstrating value in health. He is the current president of the Australian Health Economics Society and immediate past-president of the Australian Chapter of The Professional Society for Pharmacoeconomics and Outcomes Research. He has significantly impacted and influenced policy, practice and funding decisions in the Australian health system. This has included leading the economic assessment of plain packaging tobacco legislation, pregnancy warning labels on alcohol, and opt-out arrangements for the MyHealth Record.

Giuseppe Caputo, BSc (Hons)

Vista Health Pte Ltd., Singapore, Singapore

Hwee-Lin Wee, PhD

Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

A/P WEE Hwee Lin is Associate Professor at the Saw Swee Hock School of Public Health at the National University of Singapore. She is a member of the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council, member of the International Editorial Advisory Board for Journal of Patient Reported Outcomes, and member of the National Advisory Committee on Cancer in Singapore. A/P Wee’s current work focuses on the use of real world evidence in health technology assessments.

Advanced Patient-Reported OutcomesLevel: ExperiencedTrack: Patient-Centered Research

Separate registration required.

This course provides an in-depth discussion of the steps needed to successfully implement patient-reported outcomes (PRO) measurement within the drug development program to generate data to support patient-centered value messages. Formulation of a successful PRO strategy requires an understanding of PRO instrument selection, psychometric evaluation, data capture, and interpretation to negotiate regulatory, reimbursement, and market access drug development hurdles. Judging PRO instrument quality and appropriateness can be challenging.

The course will present the key elements to consider at each step in reviewing and selecting PRO measures and determining the need for new instruments. In addition, participants will gain a better understanding of regulatory expectations for qualitative and quantitative evidence to support the quality of PRO measures and aspects to consider when interpreting meaningful change. The course will include interactive discussions of PRO success stories and common pitfalls to watch out for during PRO implementation in clinical trial programs.

Participants will gain the knowledge and skills required to take on a more active and confident role in the PRO strategy and implementation process.

PREREQUISITE: This course assumes that participants will have a basic knowledge of key PRO-related concepts (eg, health-related quality of life, symptoms, impacts, a general knowledge of the PRO development steps, and a working knowledge of PRO measurement within clinical programs.)

Faculty Member

Rebecca Crawford, MA

RTI Health Solutions, Manchester, United Kingdom

Ms. Crawford has 13 years of experience providing consultative support to pharmaceutical companies with a focus on the development of patient-reported outcome (PRO) measurement strategies to best meet the needs of their clinical trial programs. Ms. Crawford has developed, culturally adapted, and validated clinical outcome assessment measures, including PROs for several different therapeutic areas. Ms. Crawford has expertise in research design and in the application of both traditional and innovative qualitative research methods, including the collection and analysis of social media data to provide insights into the patient disease and treatment experience.

Lynda Doward, MRes

RTI Health Solutions, Manchester, United Kingdom

Ms. Doward has over 30 years of experience conducting patient-centered outcomes research including the provision of strategic advice to pharmaceutical companies in the incorporation of the patient voice into drug development programs. Ms. Doward is an expert in the development of clinical outcome assessment (COA) strategies including the development of patient-centered clinical trial endpoints, the implementation of patient-reported and other COA outcome measures in clinical trial programs, and the inclusion of PRO and other COA value messages at key drug development hurdles. Ms. Doward has extensive experience in supporting pharmaceutical clients in their COA-related submissions to regulatory agencies in Europe and the US and advises on health-utility measurement strategies for reimbursement agencies in Europe. Ms. Doward has led the development of over 40 COA questionnaires that have been adapted and validated for use in over 60 languages worldwide. Ms. Doward currently serves on the ISPOR COA Special Interest Group (leadership committee) and the ISPOR Patient Council (member) and was a member of the leadership committee of the completed ISPOR Good Research Practices Task Force for the measurement of health state utilities in clinical trials. Ms. Doward has acted as a consultant to the World Health Organization and has served as a Research Advisor to the UK Department of Health, and medical charities in the United Kingdom.

Ari Gnanasakthy, MSc, MBA

RTI Health Solutions, Succasunna, NJ, USA

Ari Gnanasakthy is a Principal Scientist at RTI Health Solutions. Prior to his appointment seven years ago at RTI-HS Ari was an Executive Director at Novartis Pharmaceuticals, and headed the Patient Reported Outcomes Center of Excellence. He has been in the pharmaceutical industry for almost 35 years. Within Novartis he has been in various functions including Biostatistics, Health Economics, Pricing, and Outcomes Research. Ari has extensively published in topics related PROs.

Shanshan Qin, PhD

RTI Health Solutions, Durham, NC, USA

Shanshan Qin, PhD, received her training on Qualitative Methodology (including statistic inference and estimation, traditional and modern testing theories, structural equation modeling, and mixed and mixture modeling) at University of Georgia. She has 10 years of experience as a psychometric analyst and statistic consultant and has been working on psychometric evaluation of clinical outcome assessments (COAs) and support of regulatory review of COA labeling claims in various therapeutic areas, including dermatology, gastroenterology, diabetes, oncology, ophthalmology, and mental disorder. She is an experienced programmer of SAS, R, and IRT PRO.

Nicholas J. Rockwood, PhD

RTI Health Solutions, Bend, OR, USA

Nicholas Rockwood, PhD, is a senior psychometrician in the Patient-Centered Outcomes Assessment group with RTI Heath Solutions and has been working on psychometric evaluations of clinical outcome assessments. Prior to joining RTI-HS, Dr. Rockwood was an assistant professor within the School of Behavioral Health at Loma Linda University, where he conducted quantitative research, taught doctoral-level statistics courses, and provided statistical consulting services to medical and behavioral health faculty and researchers. His statistics and psychometrics research, which has been published in top psychometrics journals such as Psychometrika and Multivariate Behavioral Research, broadly focuses on the development and evaluation of generalized latent variable modeling methods (eg, item response theory, multilevel modeling, structural equation modeling).

Introduction to Machine Learning MethodsLevel: IntermediateTrack: Methodological & Statistical Research

Separate registration required.

Healthcare data are often available to payers and healthcare systems in real time, but are massive, high dimensional, and complex. Artificial intelligence and machine learning merge statistics, computer science, and information theory and offer powerful computational tools to enhance the extraction of useful information from complex healthcare data and prediction accuracy. This course gives an overview of basic machine learning concepts and introduces a few commonly used machine learning techniques and their practical applications in healthcare and pharmaceutical outcomes research. Participants will be introduced to foundational principles and concepts of statistical machine learning, then be provided with several specific machine learning techniques and their applications in health and pharmaceutical outcomes research. The course faculty will use R or Radiant to demonstrate several machine learning methods such as penalized regression and tree-based methods, as well as techniques for dimension reduction/feature selection. Participants will have hands-on practical experiences with machine learning and gain experience interpreting and evaluating the results and prediction performance that comes from machine learning modeling. Distinguishing prediction modeling from causal inference research in pharmacoepidemiology will be also presented and discussed. This is an entry-level course but is designed for those with some familiarity with traditional statistical modeling techniques (eg, linear regression, logistic regression).

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.

Faculty Member

Wei-Hsuan Jenny Lo-Ciganic, PhD, MSPharm, MS

University of Pittsburgh, Pittsburgh, PA, USA

Dr. Wei-Hsuan Jenny Lo-Ciganic is a pharmacoepidemiologist and associate professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida College of Pharmacy. Her research agenda focuses on drug safety and addiction. Dr. Lo-Ciganic has extensive experience applying advanced predictive analytics including machine learning and trajectory modeling with large healthcare datasets. She conducts research to develop risk prediction algorithms and tools, and practical intervention applications for use in real-world settings to improve health outcomes and patient care. She is also a core faculty member in the Center for Drug Evaluation and Safety (CoDES) at the University of Florida College of Pharmacy.

William Padula, PhD, MSc, MS

University of Southern California, Los Angeles, CA, USA

William Padula, PhD is Assistant Professor of Pharmaceutical & Health Economics at the University of Southern California School of Pharmacy, and a Fellow in the Leonard D. Schaeffer Center for Health Policy & Economics. He is a Co-Founder & Principal at Stage Analytics. His research focuses on the theoretical foundations of medical cost-effectiveness analysis and applications of machine learning. He was the 2021 recipient of ISPOR’s Bernie O’Brien New Investigator Award, Co-Chair of the ISPOR Machine Learning Task Force, and is an Associate Editor for Value in Health.

Going Beyond the Standard: Exploring Advanced Survival Modeling TechniquesLevel: IntermediateTrack: Methodological & Statistical Research

Separate registration required.

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 parametric mixture, mixture-cure, and non-mixture cure models are among novel ways to capture these more complex survival patterns. In addition, the incorporation of external evidence has gained prominence. 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.

PREREQUISITE: Participants must have a basic understanding of Kaplan-Meier curves, standard survival modeling techniques, such as a Weibull distribution in a partitioned survival framework, and R in order to follow along with the exercise walkthroughs.

Faculty Member

Elisabeth Fenwick, PhD

OPEN Health Evidence & Access, Oxford, United Kingdom

Elisabeth Fenwick is Chief Scientific Officer for HEOR & Market Access at Open Health, based in Oxford in the UK. Liz provides scientific and strategic support to HE projects globally. She has extensive experience in economic evaluation and health economic modeling having worked in the field for over 20 years. She has worked on a variety of projects in a wide range of disease areas including oncology, respiratory, infectious diseases, cardiology, ophthalmology, and orphan diseases. Liz has also contributed to methods in the field, in particular relating to decision analytic modeling and simulation methods, probabilistic decision analytic modeling and value of information analysis. Liz was a member of the ISPOR joint task force on good research practices in modeling and a co-author on the joint taskforce paper on uncertainty and co-chaired/co-authored the recent ISPOR task force assessing emerging good practice in value of information analysis for research decisions. Liz has a PhD and MSc in Health Economics as well as an MSc in Operations Research and joined Open Health from ICON plc where she led the modeling team for the global HE group. Prior to her consultancy career, Liz spent over 15 years as an academic working at University of York, McMaster University, and most recently University of Glasgow.

Sven Klijn, MSc

Bristol-Myers Squibb, Utrecht, ZH, Netherlands

Claire Simons, PhD, MSc, MMATH

OPEN Health - HEOR and Market Access, York, NYK, United Kingdom

Claire Simons, PhD, is an associate director in OPEN Health’s HEOR & Market Access business, based in the UK. In her role, Claire leads the data analytics capabilities of the Modelling and Meta-analysis Centre of Excellence and has gained extensive experience in survival analysis, data analysis to inform health economic models and Bayesian statistics. In addition, she has experience in economic evaluation and health economic modelling across many clinical areas including oncology, rare diseases, cardiology, and neurology. Claire holds a PhD in biostatistics (University of Cambridge), an MSc in health economics (University of York) as well as an MMATH in mathematics and statistics (University of Oxford). Prior to OPEN Health, Claire worked as a researcher in health economics at the University of Oxford.

ISPOR Europe 2024

17 - 20 November

Short Courses

All short courses require a separate registration fee.

ISPOR–The Professional Society for
Health Economics and Outcomes Research