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A Flexible Open-Source Decision Model for Value Assessment of Biologic Treatment for Rheumatoid Arthritis

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Abstract

Objective

The nature of model-based cost-effectiveness analysis can lead to disputes in the scientific community. We propose an iterative and collaborative approach to model development by presenting a flexible open-source simulation model for rheumatoid arthritis (RA), accessible to both technical and non-technical end-users.

Methods

The RA model is a discrete-time individual patient simulation with 6-month cycles. Model input parameters were estimated based on currently available evidence and treatment effects were obtained with Bayesian network meta-analysis techniques. The model contains 384 possible model structures informed by previously published models. The model consists of the following components: (i) modifiable R and C++ source code available in a GitHub repository; (ii) an R package to run the model for custom analyses; (iii) detailed model documentation; (iv) a web-based user interface for full control over the model without the need to be well-versed in the programming languages; and (v) a general audience web-application allowing those who are not experts in modeling or health economics to interact with the model and contribute to value assessment discussions.

Results

A primary function of the initial version of RA model is to help understand and quantify the impact of parameter uncertainty (with probabilistic sensitivity analysis), structural uncertainty (with multiple competing model structures), the decision framework (cost-effectiveness analysis or multi-criteria decision analysis), and perspective (healthcare or limited societal) on estimates of value.

Conclusion

In order for a decision model to remain relevant over time it needs to evolve along with its supporting body of clinical evidence and scientific insight. Multiple clinical and methodological experts can modify or contribute to the RA model at any time due to its open-source nature.

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Author Contributions

DI and JPJ designed the study, developed the model, and wrote the manuscript. JRC provided clinical input on model design and contributed to the writing of the manuscript; JS and DNL provided economic input on model design and contributed to writing of the manuscript.

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Authors and Affiliations

  1. Innovation and Value Initiative, 11100 Santa Monica Boulevard, Suite 500, Los Angeles, CA, 90025, USA

    Devin Incerti, Jason Shafrin & Jeroen P. Jansen

  2. Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA

    Jeffrey R. Curtis

  3. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA, USA

    Darius N. Lakdawalla

  4. Department of Health Research and Policy (Epidemiology), Stanford University School of Medicine, Stanford, CA, USA

    Jeroen P. Jansen

Authors
  1. Devin Incerti
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  2. Jeffrey R. Curtis
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  4. Darius N. Lakdawalla
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Corresponding author

Correspondence to Jeroen P. Jansen.

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Funding

This research was funded through the Innovation and Value Initiative (IVI), a multi-stakeholder research initiative.

Conflicts of interest

Devin Incerti, Jason Shafrin, and Jeroen Jansen are salaried employees of Precision Medicine Group. Darius Lakdawalla and Jeroen Jansen are shareholders of Precision Medicine Group, the parent company of Precision Health Economics (PHE), and Darius Lakdawalla is also a paid consultant to PHE. Jeffrey Curtis is a paid consultant to IVI. At the time of the current study, IVI was part of PHE and partly funded by different pharmaceutical companies.

Data Availability

Source code and data for the model are available at: https://github.com/InnovationValueInitiative/IVI-RA. A webpage with links to all components of the model (R package, tutorial, supplemental documentation, and web-interfaces) can be found at: https://innovationvalueinitiative.github.io/IVI-RA.

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Incerti, D., Curtis, J.R., Shafrin, J. et al. A Flexible Open-Source Decision Model for Value Assessment of Biologic Treatment for Rheumatoid Arthritis. PharmacoEconomics 37, 829–843 (2019). https://doi.org/10.1007/s40273-018-00765-2

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