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

Author

Listed:
  • Devin Incerti

    (Innovation and Value Initiative)

  • Jeffrey R. Curtis

    (University of Alabama at Birmingham)

  • Jason Shafrin

    (Innovation and Value Initiative)

  • Darius N. Lakdawalla

    (University of Southern California)

  • Jeroen P. Jansen

    (Innovation and Value Initiative
    Stanford University School of Medicine)

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.

Suggested Citation

  • Devin Incerti & Jeffrey R. Curtis & Jason Shafrin & Darius N. Lakdawalla & Jeroen P. Jansen, 2019. "A Flexible Open-Source Decision Model for Value Assessment of Biologic Treatment for Rheumatoid Arthritis," PharmacoEconomics, Springer, vol. 37(6), pages 829-843, June.
    • Handle: RePEc:spr:pharme:v:37:y:2019:i:6:d:10.1007_s40273-018-00765-2
    DOI: 10.1007/s40273-018-00765-2
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    References listed on IDEAS

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    1. Lakdawalla, Darius & Malani, Anup & Reif, Julian, 2017. "The insurance value of medical innovation," Journal of Public Economics, Elsevier, vol. 145(C), pages 94-102.
    2. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    3. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884.
    4. Alex Diamantopoulos & A. Finckh & T. Huizinga & D. Sungher & L. Sawyer & D. Neto & F. Dejonckheere, 2014. "Tocilizumab in the Treatment of Rheumatoid Arthritis: A Cost-Effectiveness Analysis in the UK," PharmacoEconomics, Springer, vol. 32(8), pages 775-787, August.
    5. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
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