KEY DRIVERS IN HEALTH TECHNOLOGY ASSESSMENT BY ANALYSING THE LEVEL OF IMPROVEMENT IN ACTUAL BENEFIT IN SOLID TUMOR ONCOLOGY DRUGS
Author(s)
Bouschon M1, Li J2, Barthelemy C3
1Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France, 2Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France, 3Assisance Publique des Hôpitaux de Paris (AP-HP), Paris, France
OBJECTIVES: The price level of drugs in France is based on their medical evaluation by the Transparency Committee. The aim of this study was to understand the rationale behind the evaluation of drugs in solid tumor oncology by determining the key drivers of the Improvement in Actual Benefit (IAB) levels. METHODS: We performed a retrospective analysis between March 2014 and October 2016 of the new products and the new indications in solid oncology. We searched quantitative and qualitative relevant criteria pertaining to the drug evaluation from the opinion of the Transparency Committee and we extracted the data for each product in an Excel® spreadsheet. RESULTS: In total, 28 drugs in 37 indications were evaluated including 1 important IAB (IAB II) (3%), 5 moderate IAB (IAB III) (13%), 14 minor IAB (IAB IV) (38%), 12 no clinical IAB (IAB V) (32%) and 5 (14%) insufficient Actual Benefit (insufficient AB). One quantitative criterion and 9 qualitative criteria were included: effectiveness, tolerance, methodological quality of the studies and type of comparator. The factors related to obtaining a good assessment (II and III) were the following: a statistically significant Overall Survival (OS) (100% of cases), an overall survival increase superior to 3 months (71% of cases) and a relevant active comparator (i.e: gold standard) (33% of cases). Conversely, the criteria that negatively impact the IAB (IV, V and insufficient AB) are: a statistically significant Progression Free Survival (PFS) alone without significant difference in OS (100% of cases), a decrease in tolerance as compared to the comparator (95%), a weak methodology (92%), a poor transposability (46%), an already covered medical need (insufficient AB in 100% of cases). Moreover, there was a linear relationship between the effect size and the IAB level (R² = 0.4628). CONCLUSIONS: The drivers influencing the IAB levels are: tolerance, quality of demonstration and data transposability.
Conference/Value in Health Info
2017-11, ISPOR Europe 2017, Glasgow, Scotland
Value in Health, Vol. 20, No. 9 (October 2017)
Code
PCN328
Topic
Health Technology Assessment
Topic Subcategory
Decision & Deliberative Processes
Disease
Multiple Diseases, Oncology