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Komplexe Interventionen – Entwicklung durch Austausch: 13. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin

Deutsches Netzwerk Evidenzbasierte Medizin e. V.

15.03. - 17.03.2012, Hamburg

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The Application of Microsimulation Methods to Support HTA and EBM for Personalized Medicine

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  • corresponding author presenting/speaker Beate Jahn - Institute of Public Health, MDM and HTA, Dept. of Public Health & HTA, UMIT – Univ. for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, HTA & Health Economics, ONCOTYROL – Center for Personalized Cancer Medicine, Innsbruck, Austria, Hall i. T./ Innsbruck, Austria
  • author presenting/speaker Ursula Rochau - Institute of Public Health, MDM and HTA, Dept. of Public Health & HTA, UMIT – Univ. for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, HTA & Health Economics, ONCOTYROL – Center for Personalized Cancer Medicine, Innsbruck, Austria, Hall i. T./ Innsbruck, Austria
  • author Nikolai Mühlberger - Institute of Public Health, MDM and HTA, Dept. of Public Health & HTA, UMIT – Univ. for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, HTA & Health Economics, ONCOTYROL – Center for Personalized Cancer Medicine, Innsbruck, Austria, Hall i. T./ Innsbruck, Austria
  • author Gaby Sroczynski - Institute of Public Health, MDM and HTA, Dept. of Public Health & HTA, UMIT – Univ. for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, HTA & Health Economics, ONCOTYROL – Center for Personalized Cancer Medicine, Innsbruck, Austria, Hall i. T./ Innsbruck, Austria
  • author Annette Conrads-Frank - Institute of Public Health, MDM and HTA, Dept. of Public Health & HTA, UMIT – Univ. for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, HTA & Health Economics, ONCOTYROL – Center for Personalized Cancer Medicine, Innsbruck, Austria, Hall i. T./ Innsbruck, Austria
  • author Uwe Siebert - Institute of Public Health, MDM and HTA, Dept. of Public Health & HTA, UMIT – Univ. for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, HTA & Health Economics, ONCOTYROL – Center for Personalized Cancer Medicine, Innsbruck, Austria, Hall i. T./ Innsbruck, Austria

Komplexe Interventionen – Entwicklung durch Austausch. 13. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin. Hamburg, 15.-17.03.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. Doc12ebm021

doi: 10.3205/12ebm021, urn:nbn:de:0183-12ebm0212

Veröffentlicht: 5. März 2012

© 2012 Jahn et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.

Gliederung

Text

Purpose: Evidence based medicine aims to use the current best available evidence in making treatment decisions. Personalized medicine (PM) focuses on matching the appropriate treatment to a given individual by focusing on individual characteristics. Merging PM with health technology assessment requires methods that permit the incorporation of multiple characteristics and complex intervention decisions. Microsimulation is a technique to evaluate health technologies and policies based on individual characteristics. Our goal was to identify and contrast different microsimulation approaches using well known health policy models (e.g., POHEM, UKPDS) and discuss their applicability in the evaluation of PM.

Methods: We performed a review on microsimulation and applications in social sciences, health care and politics. Assessment criteria included the modeling of patient characteristics/history/prior events, continuous/discrete time, inclusion of life years/utilities/costs and open/closed cohort approach.

Results: Identified approaches range from state-transition models, discrete-event simulation models to equation-based models. Individual characteristics include risk factors, patient history, severity of disease, number of repeated events. Different approaches were used to link risk factors and predictors to prognosis and treatment decisions and success. E.g., POHEM is one of the leading comprehensive Canadian microsimulation models for health care policies. Applications range from cancer prevention and treatment to the evaluation of cardiovascular diseases. Overall microsimulation has been successfully applied e.g., in cancer research, for chronic diseases or screening and prevention.

Conclusion: Microsimulation techniques are broadly applied but still underrepresented in health sciences. They are a powerful tool for evaluating complex strategies as they can incorporate the genetic and clinical heterogeneity of individuals as well as personalized decision algorithms.