PROs were also presented in the HTA submissions of 8 drugs with no EMA PRO data. In HTA submissions, PROs assessing QoL (51/57, 89.4%) and pain (18/57, 31.6%) were the most common. Statistical significance was observed in 59.6% of the 52 submissions with available results. In the final HTA decision, PROs were mentioned for 43.9% of submissions, not mentioned in 35.1% and were not reviewed by the HTA body in 21.0% of submissions due mainly to methodological issues. ConClusions: Demonstrating a statistically significant improvement in PROs does not increase the chance of a positive HTA recommendation (65.5% vs 74.1% for submissions without PROs). The value that PRO claims in oncology have to payers needs to be further clarified.
hospitals without rheumatology department which only use IV representing ,0,1% of DDD; 8/15 hospitals with rheumatology departments which kept IV for hospital but also started PHMEV representing 74,9% of DDD ; and 3/15 hospitals with rheumatology department which use both IV and SC at hospital also with PHMEV representing 25% of DDD. Conclusions: SC consumptions are increasing but IV still accounts for a main part. As SC and IV have close DCT and are both reimbursed in indications studied, price would not explain these differences. Different factors should be considered such as hospital policies encouraging ambulatory cares regarding day hospital capacities in every hospital and the imminent arrival of IV biosimilars.
insights on model constructs, key data elements/assumptions, and recent modeling advances. Results: Thirty-three HTAs comprising 60 CUAs were considered relevant and investigated further. Albeit individual sampling models and discrete event simulations have some advantages over Markov models, these three techniques may provide similar cost-effectiveness estimates and were all deemed appropriate for HTA submissions. At least ten different structural components were identified for which data sources and/or assumptions have evolved over time, several of which have a major bearing on model outcomes. The characteristics of patients entering the model (e.g. disease severity and prior treatments), assumptions about long-term disease progression whilst on treatment and the rebound effect upon treatment discontinuation, and mapping of Health Assessment Questionnaire and/or pain scores to Quality of Life utility values were repeatedly mentioned as key elements affecting the results. ConClusions: A wide variety of economic models for the evaluation of bDMARDS in RA have been developed and are continuously being refined. Despite recent initiatives to reach consensus on how RA models should be designed, substantial differences in the data sources and assumptions that are used still remain. This limits the comparability across and also generalizability of the various results obtained by using these models and poses problems to all stakeholders involved in HTAs.
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