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

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...

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Publicado en:PharmacoEconomics Vol. 37; no. 6; pp. 829 - 844
Autores principales: Incerti, Devin, Curtis, Jeffrey R., Shafrin, Jason, Lakdawalla, Darius N., Jansen, Jeroen P.
Formato: research tables/charts Journal Article
Publicado: Springer Nature Jun2019
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jun2019
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      pub: Springer Nature
      place: New York, New York
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        atl: A Flexible Open-Source Decision Model for Value Assessment of Biologic Treatment for Rheumatoid Arthritis.
      aug:
        au:
          Incerti, Devin
          Curtis, Jeffrey R.
          Shafrin, Jason
          Lakdawalla, Darius N.
          Jansen, Jeroen P.
        affil: Innovation and Value Initiative, 11100 Santa Monica Boulevard, Suite 500, 90025, Los Angeles, CA, USA
      sug:
        subj:
          Models, Statistical
          Biological Products Therapeutic Use
          Decision Making
          Arthritis, Rheumatoid Drug Therapy
          Uncertainty
          Human
          Cost Benefit Analysis
          Arthritis, Rheumatoid Mortality
          Probability
          Health Care Costs
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
      ab: 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.
      pubtype: Academic Journal
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      ougenre: Article
    language: English
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