Modelling Outcomes of Complex Treatment Strategies Following a Clinical Guideline for Treatment Decisions in Patients with Rheumatoid Arthritis
- 387 Downloads
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.
KeywordsRheumatoid Arthritis Leflunomide Tocilizumab Abatacept Health Utility
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.
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.
- 1.Pincus T, Yazici Y, Sokka T, Aletaha D, Smolen JS. Methotrexate as the “anchor drug” for the treatment of early rheumatoid arthritis. Clin Exp Rheumatol. 2003;21(5 Suppl 31):179–85.Google Scholar
- 6.Prevoo MLL, Van’t Hof MA, Kuper HH, Van Leeuwen MA, Van de Putte LBA, Van Riel P. Modified disease activity scores that include twenty-eight-joint counts development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum. 1995;38(1):44–8.PubMedCrossRefGoogle Scholar
- 7.Fransen J, van Riel P. The Disease Activity Score and the EULAR response criteria. Clin Exp Rheumatol. 2005;23(5):93.Google Scholar
- 8.Barton P, Jobanputra P, Wilson J, Bryan S, Burls A. The use of modelling to evaluate new drugs for patients with a chronic condition: the case of antibodies against tumour necrosis factor in rheumatoid arthritis. Health Technol Assess. 2004;8:1–91.Google Scholar
- 12.Brennan A, Bansback N, Nixon R, Madan J, Harrison M, Watson K, et al. Modelling the cost effectiveness of TNF-alpha antagonists in the management of rheumatoid arthritis: results from the British Society for Rheumatology Biologics Registry. Rheumatology (Oxford). 2007;46(8):1345–54.CrossRefGoogle Scholar
- 13.Cimmino MA, Leardini G, Salaffi F, Intorcia M, Bellatreccia A, Dupont D, et al. Assessing the cost-effectiveness of biologic agents for the management of moderate-to-severe rheumatoid arthritis in anti-TNF inadequate responders in Italy: a modelling approach. Clin Exp Rheumatol. 2010;29(4):633–41.Google Scholar
- 15.Russell A, Beresniak A, Bessette L, Haraoui B, Rahman P, Thorne C, et al. Cost-effectiveness modeling of abatacept versus other biologic agents in DMARDS and anti-TNF inadequate responders for the management of moderate to severe rheumatoid arthritis. Clin Rheumatol. 2009;28(4):403–12.PubMedCrossRefGoogle Scholar
- 22.Welsing PMJ, van Riel PLCM. The Nijmegen inception cohort of early rheumatoid arthritis. J Rheumatol. 2004;69:14–21.Google Scholar
- 25.DREAM. Dutch RhEumatoid Arthritis Monitoring. 2014. http://www.dreamregistry.nl. Accessed 15 Jan 2014.
- 28.Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov EBN, Caski F, editors. Second international symposium on information theory. Budapest: Akademiai Kiado; 1973. p. 267–281.Google Scholar
- 31.Bruce B, Fries JF. The health assessment questionnaire (HAQ). Clin Exp Rheumatol. 2005;23(5):14.Google Scholar
- 32.Hakkaart-van Roijen L, Tan SS, Bouwmans CAM. Handleiding voor kostenonderzoek: methoden en standaard kostprijzen voor economische evaluaties in de gezondheidszorg. Diemen: College voor Zorgverzekeringen; 2010.Google Scholar
- 33.CBS. Statline-Centraal Bureau voor de Statistiek. 2014. http://statline.cbs.nl/statweb/. Accessed 1 Feb 2014.
- 37.Zorginstituut Nederland. Farmacotherapeutisch Kompas 2014. http://www.farmacotherapeutischkompas.nl. Accessed 1 Feb 2014.
- 39.Dolan PD, Gudex C, Kind P, Williams A. A Social tariff for EuroQoL: results from a UK general population survey. Discussion Paper No. 138. York: Centre for Health Economics, University of York; 1995.Google Scholar
- 40.Böhning D, Schlattmann P, Lindsay B. Computer-assisted analysis of mixtures (C.A.MAN): statistical algorithms. Biometrics. 1992;48(1):283–303.Google Scholar
- 41.Hernández Alava M, Wailoo AJ, Ara R. Tails from the peak district: adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values. Value Health. 2012;15(3):550–61.Google Scholar
- 42.College voor Zorgverzekeringen. Richtlijnen farmaco-economisch onderzoek, geactualiseerde. Versie 2006.Google Scholar
- 44.National Institute for Health and Clinical Excellence. Guide to the methods for technology appraisal. London: NICE; 2008. http://www.nice.org.uk/media/B52/A7/TAMethodsGuideUpdatedJune2008.pdf. Accessed 10 June 2014.
- 49.R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-02012.Google Scholar
- 50.Böhning D, Dietz E, Schlattmann P. Recent developments in computer-assisted analysis of mixtures. Biometrics. 1998;54(2):525–36.Google Scholar
- 52.Genz A. Numerical computation of multivariate normal probabilities. J Comput Graph Stat. 1992;1(2):141–9.Google Scholar
- 53.Press WH, Teukolsky SA, Vetterling W, Flannery BP. Numerical recipes-the art of scientific computing. 3rd ed. New York: Cambridge University Press; 2007.Google Scholar
- 54.Weinstein MC, O’Brien B, Hornberger J, Jackson J, Johannesson M, McCabe C, et al. Principles of good practice for decision analytic modeling in health care evaluation: report of the ISPOR Task Force on Good Research Practices—Modeling Studies. Value Health. 2003;6(1):9–17.PubMedCrossRefGoogle Scholar
- 55.Briggs A, Claxton K, Sculpher M. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006.Google Scholar
- 57.Selvin S. Survival analysis for epidemiologic and medical research: a practical guide. Cambridge: Cambridge University Press; 2008.Google Scholar