Cost-effectiveness estimates can be sensitive to the methods applied in modelling survival data. This is particularly true in oncology given the requirement to estimate lifetime costs and outcomes (ie, extrapolate) beyond the follow-up typically observed in clinical trials. Time-to-event (survival) analysis is an important element in many economic analyses of healthcare technologies. 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. 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. 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 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. 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. She has extensive experience in economic evaluation and health economic modeling having worked in the field for over 20 years. Liz provides scientific and strategic support to HE projects globally. OPEN Health Evidence & Access, Oxford, OXF, United KingdomĮlisabeth Fenwick is Deputy Chief Scientific Officer in Evidence & Access at Open Health, based in Oxford in the UK. To gain a more in-depth understanding of the impact of the choice for a specific method, participants will practice with several of the survival modeling techniques in hands-on exercises. 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. Newer techniques like response based landmark models, parametric mixture models, mixture cure models and Bayesian model averaging provide novel ways to capture these more complex survival patterns. 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. 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.
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