Article
The Application of Microsimulation Methods to Support HTA and EBM for Personalized Medicine
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Published: | March 5, 2012 |
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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.