Early benefit assessment of innovative pharmaceuticals according to AMNOG in Germany: empirical analysis of the decision-making process

在许多工业化国家，已经建立了决定新的保健技术的补偿的决策程序。通过2010年引入《药品市场改革法》（简称：AMNOG），德国专利药品的监管框架发生了重大变化。制造商将提交一份档案，以证明新药的额外健康益处。反过来，在联邦联合委员会（G-BA）做出决定之前，由卫生保健质量和效率研究所（IQWiG）进行所谓的早期效益评估（EBA）。由于覆盖决策与卫生政策高度相关，因此有必要进行合理的实证分析，以了解AMNOG的功能，从而评估AMNOG的后果。目前，没有经验证据可以分析影响评估的因素，因此，本项目的目的是确定决定额外效益程度的因素（显著、相当大、微小、不可量化、没有效益证据、效益低于比较者）。我们还研究了解释制造商在随后的价格谈判中给予的回扣水平的影响的因素。这样做，我们区分（a）证据的质量，以证明额外的好处，（B）它的介绍，（c）技术特点的评价药物，（d）工艺相关的决策标准，（e）制药的预算影响。为此，将使用回归分析，如二项和多项logistic回归模型。在该项目之前的一项试点研究中，我们已经确定了要收集的变量。试点研究也证实了该项目的可行性。该项目使用EBA的文件作为数据源，从G-BA的网站和劳尔-Taxe数据库来计算退税授予。为了确保数据的有效性，所有变量都将由两名独立的审查员提取，然后进行比较。 研究结果提供了参与机构（特别是G-BA和IQWiG）的作用和决策过程的证据。

Across many industrialized countries, decision processes have established that determine reimbursement of new health technologies. By introducing the Act on the Reform of the Market for Medicinal Products (Arzneimittelmarktneuordnungsgesetz, short: AMNOG) in 2010, the regulatory framework for patented pharmaceuticals has changed substantially for Germany. Manufacturers are to submit a dossier to demonstrate additional health benefit of the new pharmaceutical. In turn, a so-called early benefit assessment (EBA) is then conducted by the Institute for Quality and Efficiency in Health Care (IQWiG) before the Federal Joint Committee (G-BA) makes a decision. As coverage decision-making is highly relevant for health policy, sound empirical analysis is necessary to understand the functioning of in order to assess the consequences of AMNOG. Currently, no empirical evidence exists that analyses what influences the appraisal.It is thus the aim of this project to identify factors that determine the degree of the additional benefit (remarkable, considerable, minor, not quantifiable, no evidence of benefit, less benefit than comparator). We also examine factors that explain the effect on the level of rebate granted by the manufacturer in the subsequent price negotiations. Doing so, we differentiate between (a) the quality of evidence presented to demonstrate additional benefit, (b) its presentation, (c) technological characteristics of the appraised pharmaceuticals, (d) process related criteria for decision-making, and (e) the pharmaceutical s budget impact. For this purpose, regression analysis such as binomial and multinomial logistic regression models will be used. In a pilot study that has preceded this project, we already defined the variables to be collected. The pilot study also confirmed the feasibility of the project.The project uses the documentation of the EBAs as data source from the G-BA s website and the Lauer-Taxe database to calculate the rebate granted. To ensure the validity of the data, all variables are to be extracted by two independent reviewers and compared afterwards. The results provide evidence about the roles of the participating institutions (especially G-BA and IQWiG) and, the decision processes.