Abstract
Objectives: The objective of this article is to describe the unique challenges and present potential solutions and approaches for economic evaluations of precision medicine (PM) interventions using simulation modeling methods. Methods: Given the large and growing number of PM interventions and applications, methods are needed for economic evaluation of PM that can handle the complexity of cascading decisions and patient-specific heterogeneity reflected in the myriad testing and treatment pathways. Traditional approaches (eg, Markov models) have limitations, and other modeling techniques may be required to overcome these challenges. Dynamic simulation models, such as discrete event simulation and agent-based models, are used to design and develop mathematical representations of complex systems and intervention scenarios to evaluate the consequence of interventions over time from a systems perspective. Results: Some of the methodological challenges of modeling PM can be addressed using dynamic simulation models. For example, issues regarding companion diagnostics, combining and sequencing of tests, and diagnostic performance of tests can be addressed by capturing patient-specific pathways in the context of care delivery. Issues regarding patient heterogeneity can be addressed by using patient-level simulation models. Conclusion: The economic evaluation of PM interventions poses unique methodological challenges that might require new solutions. Simulation models are well suited for economic evaluation in PM because they enable patient-level analyses and can capture the dynamics of interventions in complex systems specific to the context of healthcare service delivery.
| Original language | English |
|---|---|
| Pages (from-to) | 566-573 |
| Number of pages | 8 |
| Journal | Value in Health |
| Volume | 23 |
| Issue number | 5 |
| Early online date | 26 Mar 2020 |
| DOIs | |
| Publication status | Published - 1 May 2020 |
Bibliographical note
Funding Information:Source of financial support: This study was funded in part by grants from the Canadian Institutes for Health Research (381280), Genome Canada, Arthritis Society, ZonMw (Netherlands Organisation for Health Research and Development), and ReumaNederlands, through the Understanding Childhood Arthritis Network Canada-Netherlands (UCAN CAN-DU) project, the National Human Genome Research Institute ( U01 HG009599 ) and from the National Cancer Institutes ( R01 CA221870 ). D.A.M. is supported by the Arthur J. E. Child Chair in Rheumatology and a Canada Research Chair in Health Systems and Services Research (2008-2018). L.R.G. is funded as a doctoral trainee by the Arthritis Society ( TGP-18-0244 ) and UCAN CAN-DU project. J.B. and S.W. were partly funded by the National Institute for Health Research (NIHR) and Oxford Biomedical Research Centre (BRC). K.P. receives consulting fees from Illumina. M.I. received travel funding by Illumina to attend the 2019 International Summit on Population Health Genomics. D.A.R., K.P., J.B., S.W., and D.A.M. received travel funding by Illumina to attend the Global Economics and Evaluation of Clinical Genomics Sequencing Working Group (GEECS) meeting.
Publisher Copyright:
© 2020 ISPOR–The Professional Society for Health Economics and Outcomes Research
