Disease Progression In Rheumatoid Arthritis: Key Element for Cost-Effectiveness Modelling.

grant agreement no [115546], resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’in kind contribution. www.imi.europa.eu frameworks: individual sampling models, discrete event simulation models, microsimulation models, and Markov cohort models. The reported individual sampling models and discrete event simulation models make assumptions about improvement of the HAQ when treated progression rates that can differ between treatments. A few of the Markov cohort models treat disease progression as separate states, depending on the patient’s type of response (e.g. remission, good, moderate or no response, measured by the American College of Rheumatology (ACR) response criteria or by the disease activity score 28 (DAS28)). Furthermore, they all assume a long-term deterioration in the HAQ score and a rebound effect when the treatment stops, i.e. for example a complete loss of the initial gain. Microsimulation and Markov cohort models use simpler structures with average annual HAQ tes of the DAS28 and estimate transition probabilities between such states over time. Disease progression of a radiographic score was modelled in one study, assuming a decreased deterioration of the radiographic score while being on treatment. No study modeled the impact of disease progression models on ACR response criteria. Finally, the two reviews did not include any cost-effectiveness analysis using decision trees that contained a disease progression model. See the table below for a summary of the identified models. PRM126: DISEASE PROGRESSION IN RHEUMATOID ARTHRITIS KEY ELEMENT FOR COST-EFFECTIVENESS MODELLING