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Cost
\nThis event is free of charge to both Members of PSI an d Non-Members.
\nRegistration
\nTo register for this event\, please click here. \;
\nOverview
\nTalks from speakers will cover an introduction to HTA and indirect comparisons for value assessment before f ocusing in more specifically on topics related to the use of indirect trea tment comparisons for patient access.
\nSpeaker details
\n| \n Speaker \n | \n \n Biography \n \n | \n < strong>Abstract \n | \n \n <
em> | \n \n Lara Wolfson\, PhD\, is the Associate V P and Head of HTA Statistics at MSD in Zurich\, Switzerland. Leading a tea m of 75+ statisticians\, she pioneers quantitative solutions for Health Te chnology Assessment (HTA) challenges. Lara's career spans roles at Merck\, Janssen\, and the World Health Organization\, focusing on biostatistics\, health technology assesment\, vaccines\, epidemiology\, and health econom ics. She also taught at Brigham Young University (US) and the University o f Waterloo (Canada). Dr. Wolfson co-leads the HTA ESIG (European Special I nterest Group) of EFSPI (European Federation of Statisticians in the Pharm aceutical Industry)\, and holds MS and PhD degrees in Statistics from Carn egie Mellon University\, with an undergraduate background from Simon Frase r University. \n | \n \n Introduction to HTA and indirect comparisons for value assessment \n | \n
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| \n \n I solve problems. Throughout more than 20 years as a statistician in the healthcare industry (public health\, device and dru g development\, and medical affairs and access)\, my goals are to ask the right questions and develop optimal solutions to get patients the best car e. I champion scientific curiosity\, use of diverse data sources\, effecti ve measurement\, and meticulous study design. As a leader and coach\, I ge t people to exceed themselves by leaving their comfort zones\, appreciatin g their strengths\, building strategic collaborations\, forming extensive personal networks\, and aiming for impact locally and globally. \nCurrently I work in Product Development Data Sciences at Hoffma nn La Roche\, leading a team of programmers and statistical scientists wit h projects spanning the non-oncology portfolio from early stage through en d of lifecycle. I also lead a community dedicated to creating and sharing best practices for collaboration and evidence generation supporting access and clinical practice. In addition\, I work externally with industry prof essional societies and special interest groups to identify and execute opp ortunities to advance the field. \nI received my M.S. a nd Ph.D. in the United States\, where I split my energy between theoretica l and applied statistics. In my free time I enjoy the exhilaration\, natur al beauty\, and rich history while racing my sons up the mountains of Swit zerland. \n | \n \n ITC is not for me: Why hallmark eviden ce may not influence HCPs \nDelivering new medi cines for patients is the goal of drug development. But the evidence needs of HTA / payer and clinical practice stakeholders often go beyond those r equired for regulators. And they are not always set in stone\, but morph a s the competitive landscape changes. There is a real need for product diff erentiation and demonstration of the relative value of new medicines compa red to their predecessors to enable optimal use in clinical practice. HTA bodies have often employed ITC to compare approved treatments and evaluate relative value instead of or in addition to direct head-to-head compariso ns within trials (which are not always available). But this tool is genera lly not something that resonates with healthcare providers (HCPs) and ther efore\, additional clinical studies are often required to address their qu estions. In this presentation\, I will discuss some of the reasons&ndash\; scientific\, statistical\, and pragmatic&ndash\; that ITCs are not necessa rily highly weighted in HCP clinical decision making in practice. \n | \n
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| \n Alex Simpson\, MSc\, is an epidemiologist by training and has been working in the pharma real-world data space for 7 years. In his current role at R oche as an Access RWE Strategy Lead\, he leads the development of RWD stud ies that are used to support reimbursement dossiers across the world which ultimately enable patients to access new therapies. Alex has a keen inter est in how RWD can be used to support patient access with a particular foc us in the rare disease space. \n | \n \n Can patient acces s be secured with single arm trials? \nSingle-a rm trials (SATs) are increasingly being reviewed by payers and Health Tech nology Assessment (HTA) agencies in situations where a randomised clinical trial is unfeasible due to ethical or operational reasons. However\, SATs pose a considerable challenge for HTA agencies when assessing new therape utic interventions as there is no direct comparison to contextualise the f indings seen from the SAT. This presentation will explore some of the alte rnative comparative methods that drug manufacturers may use to demonstrate treatment benefits during the HTA appraisal\, some of the pitfalls associ ated with such methods and what the future may hold for these types of val ue assessments. \n | \n
 \n Christian Rö\;ver | \n Christian Rö\;ver is a research associate a t the Department of Medical Statistics\, University Medical Center Gö\ ;ttingen\, Gö\;ttingen\, Germany. \; After studying Statistics at Dortmund University and Iowa State University\, he earned a PhD degree at The University of Auckland. \; While his masters thesis was on classif ication methods\, the PhD thesis was on computer intensive methods for Bay esian parameter estimation. \; After his PhD\, he went on to work most ly on signal detection and parameter estimation problems at the Max Planck Institute for Gravitational Physics (Albert Einstein Intitute) in Hannove r\, before moving to medical statistics at the University Medical Center G ö\;ttingen. His current methodological research focus is on Bayesian m ethods for meta-analysis\, their application in the common case of only fe w studies\, and their implementation in R. | \nBayesian random-effects meta-analysis u
sing empirically motivated heterogeneity priors. \n \n In Bayesian meta-analysis\, the specification of pr ior probabilities for the between-study heterogeneity is commonly required \, and is of particular benefit in situations where only few studies are i ncluded. \; Among the considerations in the set-up of such prior distributions\, the consultation of available empirical data on a set of relevant past analyses sometimes plays a role. \; Properly summarizing historical heterogeneity data\, however\, is tricky\; we extended the commonly used normal-normal hierarchical model fo r random-effects meta-analysis to infer a heterogeneity prior from previou s data (Rö\;ver et al.\, 2023). \; We use an \; example data s et to demonstrate how to fit a distribution to empirically observed hetero geneity data from a set of meta-analyses\, including considerations of par ametric distribution families and immediate applicability. We also outline the results from applying such an approach to "historical" HTA data from the Institute for Quality and Efficiency in Health Care (IQWiG\, Germany) (Lilienthal et al.\, 2023). | \n
