, Volume 32, Issue 10, pp 1015–1028 | Cite as

Modelling Outcomes of Complex Treatment Strategies Following a Clinical Guideline for Treatment Decisions in Patients with Rheumatoid Arthritis

  • An Tran-DuyEmail author
  • Annelies Boonen
  • Wietske Kievit
  • Piet L. C. M. van Riel
  • Mart A. F. J. van de Laar
  • Johan L. Severens
Original Research Article



Management of rheumatoid arthritis (RA) is characterised by a sequence of disease-modifying antirheumatic drugs (DMARDs) and biological response modifiers (BRMs). In most of the Western countries, the drug sequences are determined based on disease activity and treatment history of the patients. A model for realistic patient outcomes should reflect the treatment pathways relevant for patients with specific characteristics.


This study aimed at developing a model that could simulate long-term patient outcomes and cost effectiveness of treatment strategies with and without inclusion of BRMs following a clinical guideline for treatment decisions.


Discrete event simulation taking into account patient characteristics and treatment history was used for model development. Treatment effect on disease activity, costs, health utilities and times to events were estimated using Dutch observational studies. Long-term progression of physical functioning was quantified using a linear mixed-effects model. Costs and health utilities were estimated using two-part models. The treatment strategy recommended by the Dutch Society for Rheumatology where both DMARDs and BRMs were available (Strategy 2) was compared with the treatment strategy without BRMs (Strategy 1). Ten thousand theoretical patients were tracked individually until death. In the probabilistic sensitivity analysis, Monte Carlo simulations were performed with 1,000 sets of parameters sampled from appropriate probability distributions.


The simulated changes over time in disease activity and physical functioning were plausible. The incremental cost per quality-adjusted life-year gained of Strategy 2 compared with Strategy 1 was €124,011. At a willingness-to-pay threshold higher than €119,167, Strategy 2 dominated Strategy 1 in terms of cost effectiveness but the probability that the Strategy 2 is cost effective never exceeded 0.87.


It is possible to model the outcomes of complex treatment strategies based on a clinical guideline for the management of RA. Following the Dutch guideline and using real-life data, inclusion of BRMs in the treatment strategy for RA appeared to be less favourable in our model than in most of the existing models that compared drug sequences independent of patient characteristics and used data from randomised controlled clinical trials. Despite complexity and demand for extensive data, our modelling approach can help to identify the knowledge gaps in clinical guidelines for RA management and priorities for future research.


Rheumatoid Arthritis Leflunomide Tocilizumab Abatacept Health Utility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Financial disclosure/conflict of interest

ATD, WK and PLCMvR declared no conflicts of interest. AB received research grants from Merck, AbbVie and Amgen, and honorarium from Pfizer and VCB. MAFJvdL received honorarium from Abbott, AbbVie, Bristol-Myers Squibb, MSD and Pfizer, fees for participation in review activities from Abbott, AbbVie, Bristol-Myers Squibb, Pfizer and UCB, and payment for lectures from Bristol-Myers Squibb. JLS received a research grant from Pfizer for conducting this study.

Role of the sponsors

Pfizer had no role in development of the model, interpretation of the simulated results or writing of the manuscript.

Author contributions

ATD designed and programmed the model, reviewed literature and performed statistical analyses for model inputs, ran the simulations, debugged the model, analysed the model outputs, designed and created the figures, and drafted the manuscript. AB assisted in drafting the manuscript. WK compiled the input data and estimated costs and utility. AB, WK, PLCMvR and MAFJvdL provided the first algorithm for treatment decisions. JLS assisted in conceptualising the model. All authors participated in discussion and refinement of the algorithm for treatment decisions, interpretation of the simulated results and review of the manuscript. ATD will serve as a guarantor for the entire contents of the manuscript.

Supplementary material

40273_2014_184_MOESM1_ESM.pdf (173 kb)
Supplementary material 1 (PDF 173 kb)


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • An Tran-Duy
    • 1
    • 2
    • 3
    Email author
  • Annelies Boonen
    • 2
    • 3
  • Wietske Kievit
    • 4
  • Piet L. C. M. van Riel
    • 4
  • Mart A. F. J. van de Laar
    • 5
  • Johan L. Severens
    • 6
  1. 1.Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA)Maastricht University Medical CenterMaastrichtThe Netherlands
  2. 2.Division of Rheumatology, Department of Internal MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
  3. 3.Caphri School for Public Health and Primary CareMaastricht UMC+MaastrichtThe Netherlands
  4. 4.Department of Rheumatic DiseasesRadboud University Nijmegen Medical CentreNijmegenThe Netherlands
  5. 5.Department of Rheumatology and Clinical ImmunologyUniversity Twente & Medisch Spectrum TwenteEnschedeThe Netherlands
  6. 6.Institute of Health Policy and ManagementErasmus University RotterdamRotterdamThe Netherlands

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