A Review on the Current Status of Health Economic Modeling in Personalized Medicine

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limited.  Objectives: The objective of this analysis is to present different alternatives for extrapolation of efficacy assumptions for treatments used in subsequent treatment lines in the context of sequence modelling.  Methods: A structured review of the literature and HTA agencies appraisals (i.e. the NICE), covering suggested approaches for efficacy assumptions in sequence modelling, has been conducted.  Results: Assumptions regarding efficacy and drop-out rates in sequence analyses are unavoidable, as evidence is often lacking. Some of the most commonly used approaches are to assume the same efficacy in all treatment lines as reported in first line or in the line that the products have been studied in clinical trials and to assume a proportional reduced efficacy in subsequent treatment lines. However, imposing some general principles and assumptions may be rather limiting and generate results that are sensitive to these assumptions and hard to support with clinical evidence.  Conclusions: The sequence modeling approach can be appropriate and very informative in some disease areas that decisions need to be taken for second or third line reimbursement. However, the users need to be conscious of the significant challenges that come along with the sequence modelling exercises when interpreting the results of such analyses. PRM110 A Review on the Current Status of Health Economic Modeling in Personalized Medicine Degeling K1, Koffijberg H2, IJzerman MJ2 1University of Twente, Enschede, The Netherlands, 2MIRA institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands

Objectives: With the advent of Personalized Medicine (PM), several researchers have argued that the field of Health Economic Modeling (HEM) is being challenged. In general, these challenges relate to the need for models to accurately represent the preference-sensitive, interactive, and dynamic clinical processes associated with PM. This study aims to provide insights into the current status of HEM in PM by identifying if and how challenges described in literature are being addressed and which modeling methods are being used to do so.  Methods: A literature review was performed on PubMed using primary search terms on PM combined with secondary search terms regarding modeling and well-known health economic terms. Modeling studies were included when patients were stratified for screening, treatment targeting, or response monitoring purposes. The sample was enriched by cross-referencing from the full text of the included publications. The final sample was analyzed to assess whether challenges were addressed or mentioned and which modeling methods were used.  Results: The final sample included 31 publications. Applicable disease areas included oncology (n= 17), cardiovascular disease (n= 5), HIV (n= 2), and the hepatitis C virus (n= 2). Decision-tree modeling (n= 15) and Markov modeling (n= 12) were the most observed modeling methods. Patients were most often stratified for treatment targeting (n= 19) or screening (n= 13) purposes, which were also combined. Challenges regarding companion diagnostics (n= 22), diagnostic performance (n= 20), and evidence gaps (n= 19) were most frequently addressed or mentioned. However, the extent to which challenges were addressed varied considerably between studies.  Conclusions: The results show that not all challenges for HEM in PM described in literature are yet frequently addressed or mentioned. This may indicate that either (1) the impact of the challenges is less severe than expected, (2) the challenges are hard to address, or (3) the health economic considerations in personalized medicine are still in an early stage. PRM111 Current Issues and Future Research Priorities for Health Economic Modelling of at Risk Population for Alzheimer’s Disease/Dementia Gustavsson A1, Green C2, Jones RW3, Simsek D4, de Reydet de Vulpillieres F5, Adlard N6, Bhattacharyya S7, Wimo A8, ForstlFörstl H9, Luthman S1 1Quantify Research, Stockholm, Sweden, 2Exeter University, Exeter, UK, 3The Research Institute for the Care of Older People, Bath, UK, 4Novartis Pharma AG, Basel, Switzerland, 5NOVARTIS PHARMA AG, Basel, Switzerland, 6Novartis Pharmaceuticals UK Ltd, Camberley, UK, 7Novartis Healthcare Pvt. Ltd., Hyderabad, India, 8Karolinska Institutet, Stockholm, Sweden, 9Technische Universität München, München, Germany

Objectives: Recent advances in biomarkers and genetics present opportunities to identify and target future treatment for those ‘at-risk’ of or at the very early stages of Alzheimer’s Disease (AD), including those with prodromal AD and preclinical AD who are most at risk of developing AD dementia. The objective of this research was to identify methodological issues and data gaps of relevance to the economic evaluation of early and pre-clinical treatment of AD with particular focus on modelling the full continuum of the disease.  Methods: A targeted literature review of published systematic review papers focusing on health economic modelling of any intervention type in the diagnosis and/or treatment of AD and/or dementia was conducted. A review of health technology assessment (HTA) reports was also completed. Identified papers and reports were reviewed, and considered within a deliberative process, to highlight and prioritize commonly discussed methodological issues and data gaps in early and pre-clinical AD health economic modelling.  Results: Fourteen review papers, 5 HTA reports and two additional papers identified from citations were retrieved for review. Key issues identified, and considered within a deliberative process, included the lack of established methods for economic evaluation in early and preclinical AD as well as limited availability of long-term large scale data required to appropriately understand and model the disease process. Methods for modelling any positive effects of disease modifying therapies (DMTs) on AD-related mortality differed across models, with results highly sensitive to the modelling assumptions made.  Conclusions: New data are needed covering the natural history of AD from preclinical throughout the disease progression process, which will further the development of appropriate economic modelling frameworks in early and preclinical AD, through to severe disease stages, institutional care, and death. Additional exploration of assumptions underlying the modelling of positive treatment effects on AD-related mortality is warranted.

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PRM112 Hyperbolic Discounting of Effects in Economic Evaluations Fókás L Numerus, Tübingen, Germany

Objectives: As the pharmaceutical industry increases its focus on treating chronic diseases, it is vitally important to obtain accurate estimates of future costs and benefits. To date, the standard discounted utility model (DU) has almost exclusively been used, even though many studies showed that time-inconsistent, hyperbolic discounting models better describe human behaviour. Such models are not currently being employed, mainly due to confusion and uncertainty about their underlying theory and applicability. The aim of this study is to highlight the differences between these two models when assessing health benefits over long time horizons.  Methods: The difference in discounting was demonstrated using test data over a 15-year time horizon. The DU model used a constant discount rate over time, whereas the hyperbolic model contained two parameters, one for perception of time and one for the departure from the traditional model (Loewenstein and Prelec,1992). The parameters used to calculate the discount functions were those proposed by Cairns and van der Pol (2000).  Results: Not only did the QALY within each treatment group drop significantly but the total QALY gain under the hyperbolic model was 61% lower compared with the DU model. As a result, the DU model overestimated the added value of the intervention, whereas the hyperbolic model, keeping all other variables constant, led to a lower ICER.  Conclusions: As average life expectancy increases so does the financial burden of long-term interventions for chronic diseases. This necessitates a more refined assessment for reimbursement that more accurately reflects people’s behaviour. We have demonstrated that, depending on the values of the parameters, using a more valid discounting approach can dramatically affect the estimate of ICER. We conclude that researchers should not rely on the DU model for treatments that achieve long-term health effects as it fails to account for people’s changing behaviour as they get older. PRM113 The Application of Population Models to Improve Access to Medical Innovations and Population Health – A Systematic Review Jahn B1, Conrads-Frank A2, Sroczynski G2, Rochau U3, Zauner G4, Bundo M2, Gyimesi M5, Endel G6, Popper N4, Siebert U7 1Department of Public Health, Health Services Research and Health Technology Assessment, Medical Informatics and Technology (UMIT), Hall i.T., Austria, 2UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, Hall i.T., Austria, 3UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and HTA, Department of Public Health, Health Services Research and HTA/ ONCOTYROL - Center for Personalized Medicine, Hall i.T./ Innsbruck, Austria, 4dwh GmbH, Vienna, Austria, 5Austrian Public Health Institute, Vienna, Austria, 6Main Association of Austrian Social Insurance Institutions, Vienna, Austria, 7Harvard Medical School, Institute for Technology Assessment & Department of Radiology, Hall i.T., Austria

Objectives: To support decision making for health or social care policies and to improve access to medical innovations, population models (PM) have become a common tool that explicitly considers population dynamics. PM are applied for economic evaluations of specific treatments or public health interventions, predictions of demand for care or burden of disease. In our project DEXHELPP (Decision Support for Health Policy and Planning), we reconsider definitions of PM and focus on modelling techniques and methodological challenges. The goal of this systematic review is to increase the insight of health policy researchers in PM.  Methods: We performed a systematic review on PM, focusing on the development and application for health policy questions. We identified existing models, systematically extracted and summarized information in evidence tables and standardized narrative comparisons. We present goals, modelling techniques, general model characteristics/specifications, validation and advantages and shortcomings of chosen approaches.  Results: The term PM is not used consistently. It refers to both models applied to study the dynamics of a population and models investigating the impact of interventions on population level. PM consider open (dynamic) rather than closed cohorts. In general, populations can be projected into the future using micro- or macrosimulations, continuous or discrete time, and a modular structure allows studying several diseases and applications. Comprehensive PM are applied for several research questions, for example, in Canada (POHEM), Sweden (SESIM), Australia (APPSIM) or Austria (GEPOC) or by OECD/WHO (CDP).  The identified models are often microsimulation models. Reported challenges are: data shortage, calibration, complexity and resource demands as well as quantifying uncertainty.  Conclusions: We identified several complex models with high quality, used for multiple research questions. The application of PM still requires better data, opportunities for data linkage and consistent reporting standards. Research should focus on continued methodological improvement for developing and applying complex population microsimulations. PRM114 Requirements for Economic Evidence Across Different HTA Bodies Vlachaki I, Wang-Silvanto J, Shephard C, Hirst A WG Access Ltd, London, UK

Objectives: Economic evidence is typically required by decision makers to inform their decisions and facilitate comparisons between technologies and disease areas. Economic modelling provides an important framework for synthesising available evidence and generating clinical- and cost-effectiveness estimates, and is usually required for most Health Technology Assessments (HTAs). Consequently, it is crucial for manufactures making submissions to different HTA bodies across the globe to understand the economic evidence requirements.  Methods: A review of the economic evidence requirements for eight HTA bodies was conducted via a comprehensive search of their websites. Six European and two non-European HTA bodies were included in our review: NICE (England), SMC (Scotland), NCPE (Ireland), HAS (France), IQWiG (Germany), TLV (Sweden), CADTH (Canada) and PBAC