Modelling outcomes of complex treatment strategies following a clinical guideline for treatment decisions in patients with rheumatoid arthritis
SourcePharmacoeconomics, 32, 10, (2014), pp. 1015-1028
Article / Letter to editor
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Medical Technology Assessment
SubjectRadboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences; Radboudumc 5: Inflammatory diseases RIHS: Radboud Institute for Health Sciences
BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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 <euro>124,011. At a willingness-to-pay threshold higher than <euro>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. CONCLUSIONS: 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.
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