Sun 8 Nov
8:00 - 12:00
Developing Decision-Grade Real-World Evidence
Session Type: Short Course
Topics: Real World Data & Information Systems
Level: Intermediate
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, we 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.
Speakers
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Jeremy Rassen, ScD
Aetion, Inc., New York, NY, United States
Jeremy A. Rassen, MS, ScD is a pharmacoepidemiologist with 25 years of academic and industry experience. He is cofounder, 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 TH Chan School of Public Health.
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Jennifer Polinski, ScD, MPH, MSc
Datavant, Boston, MA, United States
Dr. Polinski serves as senior vice president and head of science delivery at Datavant. Dr. Polinski is an epidemiologist and pharmacoepidemiologist with more than 25 years of experience in the healthcare industry and academia. Her specialties include real-world evidence generation from real-world data in the comparative safety and effectiveness space as well as health economics and outcomes research. Before joining Datavant and its predecessor Aetion, Dr. Polinski held analytics leadership roles at CVS Health and at Haven, the Amazon, Berkshire Hathaway, JPMorgan Chase healthcare venture. Prior to her industry experience, she spent 11 years at the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital. She was an Assistant Professor at Harvard Medical School and the Harvard T.H. Chan School of Public Health. She has published more than 75 articles in peer-reviewed medical journals. Dr. Polinski received her bachelor's degree from the University of Virginia, master's degrees from both Emory University (Public Health) and Harvard T.H. Chan School of Public Health (Epidemiology), and a doctorate degree in Epidemiology from the Harvard T.H. Chan School of Public Health.
Prompt Engineering for HEOR: Practical Skills and Use Cases for HEOR Professionals
Session Type: Short Course
Topics: Methodological & Statistical Research
Level: Introductory
Separate registration required.
Prompt engineering—the art and science of designing effective inputs for generative AI—has become a critical skill for health economists and outcomes researchers. Mastery of prompt engineering can significantly enhance productivity, accuracy, and innovation in HEOR, unlocking the full potential of large language models (LLMs) and other AI tools. This course delivers a comprehensive introduction to prompt engineering, tailored specifically for the HEOR context. Participants will gain hands-on experience with practical prompt strategies for systematic literature reviews (SLRs), economic modeling, real-world evidence generation, and more. The curriculum also addresses current best practices and common pitfalls, equipping attendees to confidently apply prompt engineering in regulated and high-stakes settings.
PREREQUISITE: Basic knowledge of systematic literature reviews and economic modeling will be helpful. No prior knowledge or use of AI is required.
Speakers
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Jag Chhatwal, PhD
Harvard Medical School / Massachusetts General Hospital, Boston, MA, United States
Jag Chhatwal, PhD, is the director of the Institute for Technology Assessment at Massachusetts General Hospital and an associate professor at Harvard Medical School. He also serves as core faculty at the Center for Health Decision Science, Harvard T.H. Chan School of Public Health. Dr. Chhatwal has co-authored more than 125 original research articles and editorials in leading peer-reviewed journals. His research has informed health policy decisions at prominent organizations including the White House, the World Health Organization, and the CDC, and has been featured in major media outlets such as CNN, Forbes, National Public Radio, The New York Times, and The Wall Street Journal. Dr. Chhatwal serves as an associate editor of Value in Health and as guest editor for its special issue on artificial intelligence. He is also a member of the ISPOR Generative AI Working Group.
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Dalia Dawoud, BSc, MSc, PhD
Cytel, London, United Kingdom
Dalia Dawoud, PhD, is Research Principal, HTA Policy and Strategy. She is also the Director and CEO of PEHTA Consulting Ltd. and holds a professor position at the Faculty of Pharmacy, Cairo University. She has over 15 years experience as a health economist and researcher. Her work is largely focused on the application of HEOR in HTA and clinical guideline development. She worked at leading organizations including NICE, where she led a portfolio of HORIZON Europe projects such as HTx, EDiHTA and SUSTAIN HTA, and the Royal College of Physicians, London. She is widely published in the areas of health economics and outcomes research and serves as associate editor for Value in Health and as director on ISPOR Board of Directors (2023-2026). She is also a member of the ISPOR AI Working Group and ISPOR Living HTA Working Group.
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Turgay Ayer, PhD
Value Analytics Labs, Boston, MA, United States
Turgay Ayer, PhD, holds the Virginia C. and Joseph C. Mello Chair and serves as the research director for Healthcare Analytics and Business Intelligence at the Center for Health & Humanitarian Systems at Georgia Tech. He is also the chief technology officer at Value Analytics Labs. Dr. Ayer holds a courtesy appointment at Emory Medical School where he teaches Big Data Analytics courses and serves as a Senior Scientist at the Centers for Disease Control and Prevention (CDC). Dr. Ayer’s research focuses on health economics modeling (HEOR), real-world evidence, data science, machine learning, econometric modeling, and healthcare analytics. He has published over 80 peer-reviewed journal papers and more than 300 conference abstracts, with his work featured in top-tier business, engineering, medical, and health policy journals. His research has attracted substantial attention from major media outlets, including The Wall Street Journal, The Washington Post, US News, and NPR. A recognized expert in HEOR, Dr. Ayer has been at the forefront of applying generative AI to navigate healthcare systems and support better decision-making. He has contributed significantly to the development of advanced models for predicting healthcare outcomes and designing innovative cost-effectiveness analysis frameworks. Under his leadership, Value Analytics Labs has focused on the development of cutting-edge technologies, including ValueGen.AI, to enhance healthcare analytics and improve the efficiency of healthcare decision-making processes.
Applied Generative AI for HEOR: Introduction
Session Type: Short Course
Topics: Methodological & Statistical Research
Level: Introductory
Separate registration required.
The rapid advancement in generative artificial intelligence (GenAI) 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 transforming 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, retrieval-augmented generation (RAG) and agents to conduct scientific research and gain an understanding on issues pertaining to privacy and security when using GenAI for HEOR. Participants will further explore specific applications of these models for conducting robust scientific HEOR research in, for example, systematic literature reviews (SLR) 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.
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.
Speakers
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Sven L Klijn, MSc
Bristol Myers Squibb, Princeton, NJ, United States
Sven Klijn is Director at Bristol Myers Squibb in the Global HEOR Evidence Acceleration & Innovation group, where he leads the innovative modeling agenda in hematology and cell therapy. In addition, Sven has an active role in providing modeling and Generative AI education at international congresses. He has widely published on innovative methods, especially in the fields 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.
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William Rawlinson
Estima Scientific, London, United Kingdom
Will is a senior health economist at Estima Scientific holding a degree in Physics and Philosophy from the University of Oxford. Will has 4 years’ experience developing cost-utility models and has specialized in applications of generative AI to health economic modelling. Will has published on the automation of R modelling using large language models (LLMs), and more recently has focused on applications of LLMs to Excel modelling and model reporting.
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Timothy Reason, BSc, MSc
Estima Scientific, London, United Kingdom
Tim Reason is co-founder of Estima Scientific and specializes 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 specializes in the intersection of HEOR, software development and AI to drive better outcomes for patients. Tim is the lead author on 2 seminal papers in AI for HEOR, showing that AI can be used to automate health economic modelling and NMA.
Risk-Sharing/Performance-Based Arrangements in Developing Countries
Session Type: Short Course
Topics: Health Policy & Regulatory
Level: Intermediate
Separate registration required.
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.
Speakers
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Bertalan Németh, PhD
Syreon Research Institute, Budapest, Hungary
Bertalan Németh PhD graduated from Corvinus University of Budapest (MSc in Quantitative economics and Operation research), Eötvös Loránd University (Pharmaceutical economics and drug policies), and Semmelweis University School of PhD Studies (PhD in Pharmacoeconomics). Between 2010 and 2015 he was a Health Economist at the Hungarian HTA office. Since August 2015 Bertalan has been a Senior Health Economist, and since 2019 a Principal Researcher at Syreon research Institute. Bertalan was lead author or co-author of more than 50 peer reviewed publications. Bertalan was the President of the ISPOR Hungary Chapter and was the Chair of the ISPOR CEE Consortium. He was a participant in the international EUnetHTA project, the ISPOR HTA Roundtable Europe, and the Scientific Committee of the Annual Conference of the ISPOR Hungary Chapter. Bertalan is also a faculty member of ISPOR HTA Trainings and was the module leader of Health Technology Assessment for the MSc program at Eötvös Loránd University.
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Rok Hren, MSc, PhD
Syreon Research Institute, Budapest, Hungary
Rok Hren has more than 15 years of commercial experience in pharmaceutical industry and more than 12 years on a board level in both (1) line management, which has included full P&L responsibility for operations in Slovenia and Romania, and (2) leadership regional functions in Central and Eastern Europe. He regularly presents on the topic of pharmaceutical economics and policies at conferences in Europe and is well experienced in healthcare media business.
He received his PhD in Physiology and Biophysics from Dalhousie University, Canada and MSc in International Health Policy (Health Economics) with Distinction from London School of Economics and Political Science, UK while he was a post-Doctoral Fellow at Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah Medical School, USA. He is also an assistant professor at the University of Ljubljana and past president of the ISPOR Slovenia Regional Chapter. In total, his publications gathered 300/394 citations (excluding self-citations) in WoS/Scopus (as of September 10, 2016).
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Katarzyna Kolasa, PhD
Kozminski University, Warsaw, Poland
Driven with passion to improve healthcare, Katarzyna has focused her academic and business career on health economics.
She has been working with multiple pricing and reimbursement challenges worldwide for the last 25 years, while holding various regional and global leadership positions at Astra Zeneca, BMS, Biogen Idec, Lundbeck, GE Healthcare, Straub Medical, BD, and the Swedish County Council of Kalmar. Katarzyna is mentor and consultant to start ups involved in the development of innovative medical devices and digital health solutions from both Holland and Poland.
Since 2000, she has been an academic teacher and supervisor for over 30 MBA and PhD students. In partnership with the Polish Medical Research Agency, Deloitte Digital and the Polish Central Hospital of Ministry of Interior Affairs, she founded the first Digital Health 6 months educational program designed for digital transformation leaders working in the healthcare sector. Katarzyna developed an innovative Master Program Health Economics & Big Data (HEBDA) with the first edition being financed by EU Power Grant 2018 as well.
She is the founder of the Global Special Interest Group Digital Health and short courses “The Role of Digital Endpoints in the Value Generation for Health Technologies”, “Risk-Sharing/Performance-Based Arrangements in Developing Countries” for ISPOR, The Professional Society for Health Economics and Outcomes Research. She is currently a member of the ISPOR Education Council and a previous member of the ISPOR Health Science Policy Council as well.
Katarzyna has dedicated her academic research towards methodological advancements into the value assessment of pharmaceuticals, medical devices, and digital health solutions. Passionate about Big Data, she led the first project of machine learning adaptation for the optimal utilization of CT scanners granted by the Polish Ministry of Health. Since January 2022, she is the leader of AI special interest group at the Polish Chamber of Physicians. With the patronage of the Polish Parliamentary Commission for Innovation & Digitalization, she organized the first dialog about the societal preferences towards the adoption of AI in the healthcare in Poland.
Being a coauthor of more than 50 IF publications, she has presented her research at more than 60 international scientific conferences. As of 2022, Google Scholar reports over 730 citations to her work.
Health Economic Modeling in R for Decision Making: Assessment, Adaptation, and AI-Assisted Validation
Session Type: Short Course
Topics: Economic Evaluation
Level: Intermediate
Separate registration required.
Health economic models developed in R are playing an increasingly prominent role in reimbursement and health technology assessment decisions. While numerous resources exist for building models in R, comparatively little attention has been devoted to reviewing, validating, and adapting existing models. In practice, decision-makers and analysts frequently encounter complex R-based models that they must evaluate, verify, and modify, often with limited familiarity with the original codebase. This course aims to address that gap, drawing on the instructors' direct experience working with and advising HTA bodies including the UK National Institute for Health and Care Excellence (NICE), the Irish National Centre for Pharmacoeconomics (NCPE), and familiarity with the processes of the Dutch Zorginstituut Nederland (Zin) and the Canadian Drug Agency (CDA).
Using a Markov cost-effectiveness model in Atrial Fibrillation as the case study, based on a model developed for UK National Institute for Health and Care Excellence (NICE) guidelines, participants will work through a structured sequence covering model execution, code quality assessment, manual and AI-assisted validation, and practical modification for sensitivity and scenario analyses.
The course begins with an introduction to decision modeling in R, establishing foundational concepts and workflow conventions for reproducible health economic analyses. Participants will then examine the Atrial Fibrillation model's structure, inputs, and outputs before executing it in R to generate base case results.
With the model running, the course turns to coding practice. Participants will learn to recognize well-structured R code, including clear naming conventions, modular organization, documentation standards, and reproducibility safeguards. They will assess whether the case study model adheres to these standards.
Validation progresses through two complementary approaches. Participants will first perform manual extreme-value and unit tests, designing targeted checks that probe model behavior at boundary conditions and verify that individual components produce expected outputs.
The course then introduces an agentic artificial intelligence approach that automates the same structured validation process, demonstrating how AI can replicate and extend what participants learned to do manually. This pairing illustrates the progression from understanding validation principles to scaling them efficiently, while the analyst retains focus on substantive judgment. R's transparent and readable code provides a natural advantage for AI-assisted review compared to spreadsheet-based models where logic is dispersed across cells and tabs.
The course also covers NICE's position statement on the use of AI in evidence generation and reporting and shows it in action: as participants use AI to quality-control the case study model, they will see how to declare and describe AI use, keep the analyst accountable, and maintain transparency and reproducibility.
Finally, participants will modify the R model to implement sensitivity and scenario analyses, adjusting parameters, restructuring assumptions, and generating alternative results. This exercise consolidates skills from the full course, requiring participants to understand the model well enough to make targeted, purposeful modifications.
By the end of the course, participants will be equipped to independently assess, validate, and adapt R-based health economic models encountered in HTA submissions, academic review, and research collaboration. Participants who wish to gain hands-on experience must bring their laptops with R/R Studio installed. An online version of RStudio will be provided prior to the course as a backup.
PREREQUISITES: Basic R usage, Health economic decision modeling, modeling in health technology assessment, and Markov models.
Speakers
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Felicity Lamrock, BSc, PhD
Queen's University Belfast, Belfast, United Kingdom
Dr. Felicity Lamrock is a senior 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/pharmacoeconomic assessment.
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Eline Krijkamp, PhD, MSc
Erasmus School of Health Policy and Management, Rotterdam, Netherlands
Getting Closer to the Target: Practical Strategies to Hit the Mark in Trial Emulation
Session Type: Short Course
Topics: Methodological & Statistical Research, Epidemiology & Public Health
Level: Intermediate
Separate registration required.
Target trial emulation (TTE) has become a cornerstone of real-world evidence (RWE) generation, particularly for external control arms and hybrid trial designs. However, translating the conceptual framework into credible, decision-ready evidence requires careful alignment of key design elements—including eligibility criteria, index date selection, follow-up, and confounding control. Small deviations in these choices can introduce substantial bias, including immortal time bias, prevalent user bias/ left truncation, informative censoring, and residual confounding driven by disease trajectory.
This course provides a comprehensive, applied framework for designing fit-for-purpose TTE studies, with a focus on time-related design challenges. Drawing on recent methodological advances and real-world applications, we integrate three core components: (1) foundational TTE principles and sources of bias, (2) a structured decision framework for index date selection, and (3) practical strategies for addressing time-related biases, including time-varying confounding.
Participants will engage with a series of case-based exercises that simulate real-world study design decisions. Through live polling and interactive dashboards, attendees will evaluate tradeoffs across alternative design strategies—such as line-of-therapy selection, comparator definition, and confounding adjustment—and observe how these decisions influence study outputs in real time (e.g., survival curves, hazard ratios, covariate balance, and weighting diagnostics).
By integrating conceptual guidance with hands-on application, this course equips researchers, regulators, and decision-makers with practical tools to operationalize TTE principles and generate credible, transparent, and defensible RWE across therapeutic areas.
PREREQUISITES: Basic familiarity with observational research and real-world data. Prior exposure to causal inference concepts is helpful but not required.
Speakers
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Neisha Opper, MPH, PhD
Landmark Science, La Crescenta, CA, United States
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Shivani Aggarwal, MS, PhD
Landmark Science, Inc, Los Angeles, CA, United States
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Hoa Le, PhD, MD
The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
Experienced Leader with expertise in RWE, Biostatistics, Pharmacoepidemiology, Drug Safety, AI, GenAI, External Control Arms and Digital Twins
8:00 - 17:00
Reimbursement Systems for Pharmaceuticals in Europe
Session Type: Short Course
Topics: Health Policy & Regulatory
Level: Intermediate
Separate registration required.
Pharmaceutical reimbursement systems in Europe are complex, diverse, and heterogeneous, shaped by national policies, healthcare priorities, regulatory frameworks and underlying epistemological choices. This short course offers an in-depth exploration of these systems, focusing on the decision-making processes that determine whether and how new medicines are reimbursed or accessible across key European markets.
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 (monopsony), this situation provides a leading position for the public health insurer to set reimbursement conditions. On the other side, pharmaceutical companies may be in a monopoly situation with a single provider or very few for the same medicinal class. Therefore, based on each country’s set of regulations, processes, and values, wide variations exist in pricing and reimbursement decisions of pharmaceuticals driven by power positions and desirability of new products. This course is essential for professionals involved in market access, health economics, regulatory affairs, and policymaking, providing the tools and knowledge needed to navigate the evolving landscape of pharmaceutical reimbursement in Europe.
Speakers
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Mondher Toumi, MSc, PhD, MD
Aix-Marseille University, Marseille, France
Professor Mondher Toumi is an MD by training and holds 2 MSc in Biostatistics, and in Biological Sciences (option pharmacology) and a PhD in Economic Sciences. He is a professor of Public Health at Aix-Marseille University. After working for 12 years as a research manager in the Department of Pharmacology at the University of Marseille, he joined the Public Health Department in 1993. In 1995, he entered the pharmaceutical industry and worked there for 13 years.
Mondher Toumi was appointed global vice president at Lundbeck A/S in charge of health economics, outcome research, pricing, market access, epidemiology, risk management, governmental affairs, and competitive intelligence. In 2008, he founded Creativ-Ceutical, an international consulting firm dedicated to support health industries and authorities in strategic decision-making.
In February 2009, he was appointed professor at Lyon I University in the Department of Decision Sciences and Health Policies. He launched the first European University Diploma of Market Access (EMAUD), an international course already followed by more than 500 students. Additionally, he recently created the Market Access Society to promote research and scientific activities around market access, public health and health economic assessment. He is chief editor of the Journal of Market Access and Health Policy (JMAHP).
Since September 2014, he joined the research unit EA3279 of the Public Health Department, at Aix-Marseille University (France) as Full Professor. Mondher Toumi is also a visiting professor at Beijing University (Third Hospital).
In June 2022 Mondher Toumi founded InovIntell, an international venture dedicated to AI in life sciences.
He is a recognized expert in health economics and an authority on market access and risk management. He published more than 200 scientific publications and authored or co-authored several books predominantly in the fields of market access and health economics.
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Frank-Ulrich Fricke, PhD, MSc
Technische Hochschule Nürnberg, Nuremberg, Germany
Frank-Ulrich Fricke is a professor of health economics at the Technische Hochschule Nürnberg Georg Simon Ohm and an impartial member of the arbitration board on drug prices in the German healthcare system (Schiedsstelle nach § 130b SGB V) since 2011. He has served as a faculty dean since 2017. After studying business administration and a PhD in economics, Frank-Ulrich worked in industry and in consulting for several years. Main areas of interest have been market access, pricing and reimbursement, health policy and health economic evaluations. Frank-Ulrich is a member of several national as well as international professional associations.
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Keith H Tolley, BA, MPhil, MPP
Tolley Limited, Buxton, United Kingdom
Keith has over 35 years’ experience in health economics across academia (University of York - Centre for Health Economics and University of Nottingham, UK 1987-1997), for several pharmaceutical companies, including GSK, Pfizer, and Ortho Biotech (1997-2005), and in consultancy as a Director at Mapi (Adelphi) Values and now Tolley. He has direct experience of HTA as performed by NICE and SMC, and reimbursement and pricing issues around Europe. Keith has managed and strategically contributed to company submissions to NICE and SMC across a range of disease areas. He has also reviewed and been involved in the development of health economic models for NICE and SMC and other HTA bodies and has reviewed economic models for their suitability (eg, structure, data inputs) for drug reimbursement purposes.
Keith is also a health economics assessor with the SMC, a position he has held since 2005, having previously been an industry representative on the NDC. In 2013, Keith also became an assessor for the All Wales Medicine Strategy Group (AWMSG) and has provided expert advice as part of the NICE Early Scientific Advice Program.
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Krzysztof Kloc
Clever-Access, Kraków, Poland
Krzysztof Kloc is senior principal consultant in pricing, reimbursement, and market access (PRMA), vice head of the PRMA department, and co-founder of Clever-Access. Based in Krakow, Poland, he holds a master’s degree in applied biotechnology and has over 15 years of experience in market access and health technology assessment. Krzysztof has been engaged in consultancy projects in Poland related to the introduction and revisions of the Reimbursement Act, as well as in international projects, including stakeholder and pathway mapping, positioning and pricing strategy, evidence generation plans, and value communication. He was a speaker at the HTA Symposium in Krakow and is an active trainer for the International Market Access Upper Degree (IMAUD).
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Claudio Jommi, MSc
Universita del Piemonte Orientale, Milan, Italy
13:00 - 17:00
Causal Inference and Causal Estimands from Target Trial Emulations Using Evidence from Real-World Observational Studies and Clinical Trials
Session Type: Short Course
Topics: Real World Data & Information Systems
Level: Advanced
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 (eg, time-zero bias, immortal time bias), (2) provide an overview of causal methods for baseline confounding (multivariate regression, propensity scores) and time-varying confounding (eg, 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).
Speakers
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Uwe Siebert, MPH, MSc, ScD, MD
UMIT TIROL - University for Health Sciences and Technology; Harvard Chan School of Public Health, 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). 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.
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Felicitas Kuhne, MSc, PhD
Pfizer Pharma GmbH, Berlin, Germany
Felicitas Kühne is a senior scientist and deputy coordinator at the Institute of Public Health, Medical Decision Making and Health Technology Assessment at the Department of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Health Technology in Hall in Tirol, Austria. She is co-leading the Program on Causal Inference in Science and is the director of the HTADS course “Causal Inference for Assessing Effectiveness in Real-World Data and Clinical Trials: A Practical Hands-on Workshop”. Further, Felicitas Kühne is an outcomes research manager at Pfizer Pharma GmbH, Germany.
Felicitas Kühne holds a doctoral degree in Health Technology Assessment from UMIT TIROL as well as a master’s degree in health policy and management from the Harvard TH Chan School of Public Health, Boston, USA. She received her state approval as physiotherapist from the Georg-August-University of Göttingen, Germany and participated in Health-Economic Program of the University of Cologne, Germany. Before she started her position at UMIT TIROL in 2011, she worked as a consultant for pharmaceutical companies and healthcare providers, conducting several decision-analytic, real-world evidence, epidemiologic, and costing studies in a variety of disease areas.
Her research interests include evaluating public health interventions by applying advanced evidence-based quantitative methods from epidemiology, comparative effectiveness research, health services and outcomes research, economic evaluation, machine learning, and decision sciences. Her current substantive research focuses on identifying synergies of causal inference and decision science. The main disease areas are cardiovascular diseases, cancer, and infectious diseases including HIV/AIDS, hepatitis C, and pneumococcal disease. She teaches courses in decision-analytic modeling, economic evaluation, analysis of big data, and advanced causal epidemiologic methods at several universities and for industry in Europe and the USA.
She has authored several publications including textbook chapters and scientific articles and disseminated her finding at several conferences. She received financial support for her studies and research from several national and international organizations.
Felicitas Kühne is an active advisory board member for a NIMHD K01 award as well as a member of the editorial board of the journal of Medical Decision Making (MDM) and the journal of MDM Policy & Practice (MDM P&P). She is a member of the Working Group "Medical Decision Making" of the German Society for Medical Informatics, Biometry and Epidemiology (GMDS). She is also a member of the Professional Society for Health Economics and Outcomes Research (ISPOR), and the Society for Medical Decision Making (SMDM).
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Nicholas Latimer, MSc, PhD
SCHARR, University of Sheffield, Nottingham, United Kingdom
Nick joined the University of Sheffield in June 2008. Previously he worked as a research officer and analyst at NERA Economic Consulting, a Health Economics Fellow at Queen Mary, University of London (QMUL), and a Health Economist at Roche Products Ltd.
Nick has worked on several clinical trials, NICE Technology Appraisals and Public Health guidelines, and consultancy projects. Much of his work focuses on survival analysis and adjusting for treatment switching in clinical trials and he has completed NIHR Doctoral and Post-Doctoral Research Fellowships on these topics. In 2024, Nick completed a Senior Research Fellowship funded by Yorkshire Cancer Research in which he investigated the application of causal inference techniques to estimate comparative effectiveness from cancer registry datasets. He has authored NICE Decision Support Unit technical support documents on survival analysis (TSD14, 2011; TSD21, 2020), treatment switching (TSD16, 2014; TSD24, 2024), and partitioned survival analysis (TSD19, 2017), and was a member of Technology Appraisal Committee B for 5 years. He works part-time for Petauri Evidence.
https://www.sheffield.ac.uk/scharr/sections/heds/staff/latimer_n
Integrating Patient, Payer, and Investor Perspectives on Valuing Innovative Medicines for Orphan Diseases
Session Type: Short Course
Topics: Health Policy & Regulatory, Epidemiology & Public Health
Level: Intermediate
Separate registration required.
Explore the value assessment of innovative drugs from the perspectives of relevant stakeholders, their respective data requirements, and their methods and processes. Gain a better understanding of the value assessment from the investor perspective, with a focus on orphan drugs and advanced therapy medical products (ATMPs).
The value of medical innovation depends on a stakeholder's perspective in different decision contexts. Regulatory authorities (EMA, FDA) mainly consider the clinical value of medical innovation. In the context of coverage decisions, national health authorities may adopt a broader perspective by including clinical, economic criteria, and sometimes even other criteria such as equity and social values. For pricing and reimbursement, "value-based pricing" is the most widely accepted approach across countries, but it can vary from a narrow concept based on the incremental cost-effectiveness ratio (ICER) threshold to broader societal or holistic approaches.
Value-based pricing determines the maximum price from the national payer perspective. In the context of the investment decision, this price should exceed the minimum price for the investor acting in the international financial market to make a financial valuation. Furthermore, there are numerous other stakeholders, eg, patients, physicians, healthcare insurers, and employers--with their specific assessment of the value of medical innovation including, for example, patient and family quality of life, real-world effectiveness, budget impact, and the costs of lost productivity. Familiarity with health economic evaluation is desirable, but the course assumes little or no familiarity with economic valuation theory.
Speakers
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Lou Garrison, PhD
The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, Seattle, WA, United States
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.
For the first 13 years of his career, Dr. Garrison worked in non-profit health policy at Battelle and then the Project HOPE Center for Health Affairs, where he was the Director from 1989-1992. Following this, he worked as an economist in the pharmaceutical industry for 12 years. From 2002-2004, he was vice president and head of Health Economics & Strategic Pricing in Roche Pharmaceuticals, based in Basel, Switzerland.
Dr. Garrison received a BA in Economics from Indiana University, and a PhD in Economics from Stanford University. He has more than 150 publications in peer-reviewed journals. His research interests include national and international health policy issues related to personalized medicine, benefit-risk analysis, and other topics, as well as the economic evaluation of pharmaceuticals, diagnostics, and other technologies.
Dr. Garrison was elected as ISPOR President for July 2016-June 2017, following other leadership roles since 2005. He recently co-chaired the ISPOR Special Task Force on US Value Frameworks. He was selected in 2017 by PharmaVOICE as being among “100 of the Most Inspiring People” in the industry. He recently received the PhRMA Foundation and Personalized Medicine Coalition 2018 Value Assessment Challenge First-Prize Award as lead author on a paper on “A Strategy to Support the Efficient Development and Use of Innovations in Personalized and Precision Medicine.”
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Marlene Gyldmark, MPhil
BeiGene, Basel, Switzerland
In her current role, Marlene leads the EU HTA organizational readiness at BeiGene in the Global Value, Access, and Pricing group.
Marlene’s prior life science industry experience includes vice president global head Access Evidence at Idorsia, Switzerland; global head Health Policy and Outcomes Research at Roche Diabetes Care, Switzerland; global head Modelling, Outcomes Research, Statistics and Epidemiology, Roche Pharma, Switzerland; health economist at Pfizer Denmark, and Pricing and Economic Analyst at Novo Nordisk, Denmark. Before joining the life science industry, she worked as a researcher in the Danish Hospital Institute, Denmark and at University of Copenhagen, Denmark. Since 1996 Marlene has been an external lecturer at University of Copenhagen, Denmark.
Other work experiences include serving as a member of the board of directors (2000-2012) at EASE Consulting, Denmark and member of the board of the Institute of Neurodiversity (2021- 2025). She has been a long-term member of ISPOR and served as member of the Board of Directors between 2021-2024. Currently, Marlene also acts as a Copenhagen Goodwill ambassador.
She holds a master’s in economics and policy sciences from University of Copenhagen, Denmark, and a MPhil in health economics from York University, UK.
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Mark J Nuijten, MBA, PhD, MD
A2M, Amsterdam, Netherlands
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.
Applied Generative AI for HEOR: Robust Architectures
Session Type: Short Course
Topics: Methodological & Statistical Research
Level: Intermediate
Separate registration required.
Generative AI (GenAI) is rapidly transforming how HEOR and market access work is conducted, from literature reviews and evidence synthesis to dossier development and HTA submissions. As the field moves beyond experimentation, a new challenge emerges for subject matter experts: how to design and build AI tools that are rigorous enough to withstand scrutiny in regulatory, HTA, and payer engagement environments.
This intermediate-level course is designed for health economists, outcomes research professionals, market access specialists, and other HEOR subject matter experts who want to move beyond general-purpose chatbots and one-off pilots, and instead learn how to architect robust, purpose-built AI solutions. The emphasis throughout is on design: participants will learn how architectural choices determine whether an AI tool produces outputs that are reliable, traceable, and defensible.
The course is organized around three core themes, each illustrated with two applied examples that run throughout the course. These are complemented by ad hoc examples that showcase the diversity of possible architectures and applications across HEOR and market access.
Context Engineering
Large language models are only as good as the information they are given. Participants will learn how to design context: how external knowledge (eg, clinical data, published evidence, HTA guidance) is retrieved and incorporated into GenAI workflows. Retrieval-Augmented Generation (RAG) is treated as a cornerstone architecture, alongside complementary techniques such as tool use, that together determine factual accuracy, traceability, and domain fit.
Agentic AI
Participants will take an in-depth look at how autonomous and semi-autonomous AI agents can coordinate multi-step HEOR processes, such as structured data extraction and drafting workflows, while maintaining control, monitoring, and accountability. Faculty will discuss how to set boundaries for agents, orchestrate tasks, and design for human oversight.
Rigor for HTA
Building AI tools whose outputs will be scrutinized by HTA bodies, regulators, and payers demands a different standard than rapid prototyping. Faculty will address what separates "vibe-coded" solutions from HTA-ready tools and demonstrate how to evaluate and validate GenAI systems in terms of reliability, reproducibility, and regulatory alignment, drawing on frameworks such as ELEVATE-GenAI and guidance from NICE and the FDA. Ethical considerations around the application of AI are discussed in the same context. Participants will also learn how to set up a professional working environment, including effective use of an IDE, AI-assisted development, and reusable components such as skills.
The course provides full, runnable code for a worked example, discussed from a design and architecture perspective and highlighting a small number of key functions. Participants can run and adapt this example themselves after the course.
By the end of this course, participants will understand how to design AI architectures that live up to the evidentiary standards of HEOR and market access. They will leave with concrete design patterns, runnable reference implementations, and validation frameworks to build GenAI tools whose outputs can withstand scrutiny in HTA, regulatory, and payer settings. A basic understanding of Python or other similar scripting languages is recommended to get the most benefit from the provided worked examples.
PREREQUISITES: Attendance at “Applied Generative AI for HEOR: Introduction” or familiarity with concepts such as prompt engineering, APIs, and LLM workflows are necessary.
Speakers
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Sven L Klijn, MSc
Bristol Myers Squibb, Princeton, NJ, United States
Sven Klijn is Director at Bristol Myers Squibb in the Global HEOR Evidence Acceleration & Innovation group, where he leads the innovative modeling agenda in hematology and cell therapy. In addition, Sven has an active role in providing modeling and Generative AI education at international congresses. He has widely published on innovative methods, especially in the fields 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.
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Rajdeep Kaur, PhD
Pharmacoevidence Pvt. Ltd., Mohali, India
Dr. Rajdeep Kaur is the Lead of AI Sciences at Pharmacoevidence, with a Ph.D. in Computer Science and Engineering and 17+ years of expertise in advanced technologies. Her work focuses on Generative AI, machine learning, and cloud-enabled data systems, with a strong emphasis on real-world healthcare applications. She has successfully led multiple GenAI projects, combining deep technical expertise to deliver impactful AI-driven solutions.
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Ghayath Janoudi, PhD, MD
Loon, Ottawa, ON, Canada
Dr. Ghayath Janoudi, MBBS, MSc, PhD, is the Founder and CEO of Loon, an AI-driven clinical research and market access company developing scientifically validated AI agents for Health Economics and Outcomes Research (HEOR), Health Technology Assessment (HTA), and reimbursement strategy.
A medical doctor and health outcomes researcher by training, Dr. Janoudi holds a PhD in Clinical Epidemiology with a specialization in artificial intelligence for clinical research. He previously held senior leadership roles at Canada’s Drug Agency (formerly CADTH) and at clinical research organizations, where he led work on HTA, drug reimbursement policy, and value evidence evaluation.
A recognized thought leader in AI for clinical discovery, Dr. Janoudi is a well-published author in AI-enabled evidence synthesis, and was named Canada’s 2024 Emerging Healthcare Leader for his contributions to accelerating timely and equitable access to innovative therapies.
Practical Applications of Large Language Models for Real-World Evidence Generation and HEOR
Session Type: Short Course
Topics: Methodological & Statistical Research
Level: Intermediate
Separate registration required.
Examine large language models (LLMs) from industry leaders such as OpenAI, Anthropic AI, Google, and Meta, focusing on their application in real-world evidence generation and HEOR. The course covers technical LLMs, including their architecture, processing layers, attention mechanisms, embeddings, context window, hallucinations, risk-based frameworks, and current task-specific live benchmarks used for model assessment.
Participants will learn prompt engineering through hands-on, practical examples, empowering them to utilize commercially available LLMs. These examples include scientific literature retrieval, PICO extraction and processing, extracting and handling numerical data, summarizing tables and figures, automating captions, and generating code.
Upon completing this in-depth course, participants will gain the competencies needed to use LLMs responsibly for practical applications in RWE and HEOR, while remaining mindful of regulatory obligations. To participate in practical exercises, attendees are required to bring a personal laptop and have access to a personal or corporate LLM account with file upload functionality.
PREREQUISITE: General knowledge of chat-based LLMs (GPT, Claude, etc) is important. This is an intermediate course, and students should have prior knowledge of AI and have used chat based LLMs in a professional/work setting.
Speakers
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Manuel Cossio
Cytel Inc. Dubendord, Zürich, Switzerland; Universitat de Barcelona, Dubendorf, Switzerland
AI Engineer and Head of AI Solutions at Cytel with 13+ years of experience in HEOR. I lead the development of AI-driven solutions for evidence generation, economic modeling, and HTA landscaping—including EU JCA and market access. With expertise across both pharma and CRO consulting, I’m committed to advancing patient care through smarter, AI-enabled decision-making.
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Benjamin Bray, MD, MBChB, MSc, FFBCS
Lane Clark and Peacock, London, United Kingdom
Dr. Ben Bray is a medical doctor and epidemiologist and is Evidence Generation lead at LCP Health Analytics. He has been working in health data science and epidemiology for over 12 years and has extensive experience in the development and validation of machine learning models and in applications of AI using health data. He has authored over 60 publications including in The Lancet, BMJ and PLOS Medicine and has co-authored multiple reviews on the use of AI and machine learning in various therapy areas. He holds an Honorary Senior Clinical Lecturer post at King’s College London, focusing on research into machine learning analytics using large health databases.
Designing a Patient-Centered Strategy for Drug Development and Value
Session Type: Short Course
Topics: Patient-Centered Research
Level: Advanced
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.)
Speakers
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Ari Gnanasakthy, MBA, MSc
RTI Health Solutions, Research Triangle Park, NC, United States
Ari Gnanasakthy is head of Patient-Reported Outcomes at RTI-HS. Prior to RTI-HS.
Mr. Gnanasakthy was the executive director and head of the Patient-Reported Outcomes Center of Excellence at Novartis Pharmaceuticals. He has almost 25 years of experience in the pharmaceutical industry. At Novartis, he worked in several departments, including Biostatistics, Health Economics, Pricing, and Outcomes Research. After receiving his bachelor's degree in mathematics, statistics, and computing, Mr. Gnanasakthy joined Rothamsted Experimental Station (UK), where he was responsible for the statistical analysis of survey data of agricultural soil in England and Wales. He then joined the Milk Marketing Board (UK), where he was a part of the team responsible for modeling lactation curves of dairy cows. Mr. Gnanasakthy's extensive experience in the field of statistics and outcome research has resulted in numerous abstracts and almost 40 publications. Throughout his career, Mr. Gnanasakthy has developed and validated over a dozen patient-reported outcomes instruments and currently serves in the editorial board of Cancer Clinical Trials and a reviewer for many professional journals, including Value in Health.
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Lynda Doward, MSc
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.
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Shanshan Qin, PhD
RTI Health Solutions, Research Triangle Park, NC, United States
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 over 10 years of experience in leading, planning, and conducting psychometric analyses to evaluate measurement properties and interpretability of clinical outcome assessment (COA) scores; and supporting regulatory submission and publication of COA evidence. She has extensive experience with COAs in a variety of therapeutic areas, including mental and behavioral disorders, dermatology, oncology, gastroenterology, obesity, and ophthalmology.
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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.
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Nicholas J. Rockwood, PhD
RTI Health Solutions, Bend, OR, United States
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).
