Expert review of pharmacoeconomics & outcomes research · 2025
Determinants of cancer drug prices in Germany and France: a multiple regression analysis.
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Subramanian Nethra, Subramanian Aadhav
Аннотация
BACKGROUND: Higher prices for drugs targeting rarer cancers may be justified, but the extent remains uncertain. This research analyses the determinants of cancer drug prices in Germany and France.
RESEARCH DESIGN AND METHODS: Cancer drugs approved in EU between 2011 and 2022 with available benefit assessments and prices were analyzed. Monthly treatment cost was the dependent variable; approval year, orphan status, benefit rating, number of comparators, and target population size were independent variables. Univariate and multiple regression analyses were conducted.
RESULTS: The analysis included 107 drugs in Germany and 70 in France. In Germany, univariate analysis showed significance for benefit magnitude, orphan status, number of comparators, target population, and approval year. In France, orphan status, number of comparators, target population, and approval year were significant. Regression models showed that log target population (p < 0.0001), approval year (p = 0.021), and major/considerable benefit (p < 0.0001) were significant in Germany; log target population (p = 0.001) and ASMR II/III (p = 0.017) were significant in France.
CONCLUSIONS: Target population and benefit rating are drivers of cancer drug prices in Germany and France. The analysis provides quantitative evidence on the association between cancer rarity and price, which could inform policy.
RESEARCH DESIGN AND METHODS: Cancer drugs approved in EU between 2011 and 2022 with available benefit assessments and prices were analyzed. Monthly treatment cost was the dependent variable; approval year, orphan status, benefit rating, number of comparators, and target population size were independent variables. Univariate and multiple regression analyses were conducted.
RESULTS: The analysis included 107 drugs in Germany and 70 in France. In Germany, univariate analysis showed significance for benefit magnitude, orphan status, number of comparators, target population, and approval year. In France, orphan status, number of comparators, target population, and approval year were significant. Regression models showed that log target population (p < 0.0001), approval year (p = 0.021), and major/considerable benefit (p < 0.0001) were significant in Germany; log target population (p = 0.001) and ASMR II/III (p = 0.017) were significant in France.
CONCLUSIONS: Target population and benefit rating are drivers of cancer drug prices in Germany and France. The analysis provides quantitative evidence on the association between cancer rarity and price, which could inform policy.