Genomic signature to guide adjuvant chemotherapy treatment decisions for early breast cancer patients in France: a cost-effectiveness analysis

IntroductionChemotherapy (CT) is commonly used as an adjuvant treatment for women with early breast cancer (BC). However, not all patients benefit from CT, while all are exposed to its short- and long-term toxicity. The Oncotype DX® test assesses cancer-related gene expression to estimate the risk of BC recurrence and predict the benefit of chemotherapy. The aim of this study was to estimate, from the French National Health Insurance (NHI) perspective, the cost-effectiveness of the Oncotype DX® test compared to standard of care (SoC; involving clinicopathological risk assessment only) among women with early, hormone receptor-positive, human epidermal growth factor receptor 2-negative BC considered at high clinicopathological risk of recurrence.MethodsClinical outcomes and costs were estimated over a lifetime horizon based on a two-component model that comprised a short-term decision tree representing the adjuvant treatment choice guided by the therapeutic decision support strategy (Oncotype DX® test or SoC) and a Markov model to capture long-term outcomes.ResultsIn the base case, the Oncotype DX® test reduced CT use by 55.2% and resulted in 0.337 incremental quality-adjusted life-years gained and cost savings of €3,412 per patient, compared with SoC. Being more effective and less costly than SoC, Oncotype DX® testing was the dominant strategy.DiscussionWidespread implementation of Oncotype DX® testing would improve patient care, provide equitable access to more personalized medicine, and bring cost savings to the health system

Key drivers for market penetration of biosimilars in Europe

Background & Objectives: Potential drivers and barriers of biosimilar uptake were mainly analysed through qualitative approaches. The study objective was to conduct a quantitative analysis and identify drivers of biosimilar uptake of all available biosimilars in the European Union (EU). Methods: A three-step process was established to identify key drivers for the uptake of biosimilars in the top 10 EU member states (MS) pharmaceutical markets (Belgium, France, Germany, Greece, Hungary, Italy, Poland, Spain, Sweden, and the UK): (1) literature review to identify incentive policies in place to enhance biosimilars adoption; (2) assessment of biosimilar market dynamics based on database analysis; (3) regression model analysis on price using the following explicative variables: incentive policies; price difference between the biosimilar and the originator product; distribution channel; generic uptake and generic price cut; pharmaceutical expenditure per capita; and market competition. Results: At the study cut-off date, 20 biosimilars were available on the market. Incentive policies applied to biosimilars were found to be heterogeneous across countries, and uptakes of biosimilars were also very heterogeneous between different therapeutic classes and countries. Results from the model demonstrated that incentive policies and the date of first biosimilar market entry were correlated to biosimilar uptake. Pharmaceutical expenditure per capita and the highest generic uptake were inversely correlated with biosimilar uptake. Average generic price discount over originator and the number of biosimilars showed a trend toward statistical significance for correlation with biosimilar uptake, but did not reach the significance threshold. Biosimilar price discount over original biologic price, the number of analogues, and the distribution channel were not correlated with the biosimilar uptake. Conclusions: Understanding drivers of biosimilar uptake becomes a critical issue to inform policy decision-makers. This study showed that incentive policies to enhance uptake remain an important driver of biosimilar penetration, while biosimilar price discounts have no impact. Future research is warranted when the biosimilar market gains maturity

A Comparison of Markov and Discrete-Time Microsimulation Approaches: ă Simulating the Avoidance of Alcohol-Attributable Harmful Events from ă Reduction of Alcohol Consumption Through Treatment of Alcohol Dependence

International audienceWhen modelling the pathophysiology of a disease, it is important to ă select a modelling approach that can adequately replicate its course. ă The objective of this paper was to compare the outcomes obtained by the ă Markov and discrete-time microsimulation modelling approaches using ă nalmefene clinical trial data. ă Markov and microsimulation modelling approaches assessing alcohol ă dependence treatment with psychosocial support with or without nalmefene ă were compared in terms of the modelled evolution of patients' alcohol ă consumption and the resulting occurrence of alcohol-attributable harmful ă events over 1 year. ă Comparison of the proportion of the modelled population at different ă levels of alcohol consumption over time revealed systematic differences ă arising from the different modelling techniques: a lower number of ă patients reaching abstinence, a higher number of patients at higher ă drinking levels, and, overall, a smoother evolution of alcohol ă consumption in the microsimulation. Reasons are discussed in the paper. ă While the models produced similar occurrences of alcohol-attributable ă harmful events as a whole, distinct results for the individual events ă were observed, explained by the specific pathophysiology of occurrence ă of these events and how their implementation was adapted to fit the ă limitations of the compared modelling approaches; however, these ă differences were only statistically significant for one of the eight ă events. ă For a general public health or health economic assessment of alcohol use ă disorders, it is possible to achieve similar results with the compared ă approaches. To assess a patients' disease course, taking into ă consideration alcohol-attributable harmful events, the microsimulation ă approach may provide more precise results. However, further external ă validation of the models is needed and this additional precision may be ă outweighed by the greater computational burden of a microsimulation ă approach