Department of Health Policy and Medical Technology Research Group, LSE Health, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, United Kingdom.
Department of Health Policy and Medical Technology Research Group, LSE Health, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, United Kingdom.
Soc Sci Med. 2017 Sep;188:137-156. doi: 10.1016/j.socscimed.2017.06.024. Epub 2017 Jun 20.
Escalating drug prices have catalysed the generation of numerous "value frameworks" with the aim of informing payers, clinicians and patients on the assessment and appraisal process of new medicines for the purpose of coverage and treatment selection decisions. Although this is an important step towards a more inclusive Value Based Assessment (VBA) approach, aspects of these frameworks are based on weak methodologies and could potentially result in misleading recommendations or decisions. In this paper, a Multiple Criteria Decision Analysis (MCDA) methodological process, based on Multi Attribute Value Theory (MAVT), is adopted for building a multi-criteria evaluation model. A five-stage model-building process is followed, using a top-down "value-focused thinking" approach, involving literature reviews and expert consultations. A generic value tree is structured capturing decision-makers' concerns for assessing the value of new medicines in the context of Health Technology Assessment (HTA) and in alignment with decision theory. The resulting value tree (Advance Value Tree) consists of three levels of criteria (top level criteria clusters, mid-level criteria, bottom level sub-criteria or attributes) relating to five key domains that can be explicitly measured and assessed: (a) burden of disease, (b) therapeutic impact, (c) safety profile (d) innovation level and (e) socioeconomic impact. A number of MAVT modelling techniques are introduced for operationalising (i.e. estimating) the model, for scoring the alternative treatment options, assigning relative weights of importance to the criteria, and combining scores and weights. Overall, the combination of these MCDA modelling techniques for the elicitation and construction of value preferences across the generic value tree provides a new value framework (Advance Value Framework) enabling the comprehensive measurement of value in a structured and transparent way. Given its flexibility to meet diverse requirements and become readily adaptable across different settings, the Advance Value Framework could be offered as a decision-support tool for evaluators and payers to aid coverage and reimbursement of new medicines.
不断上涨的药价促使人们制定了许多“价值框架”，旨在为支付者、临床医生和患者提供新药评估和评估过程的信息，以便做出覆盖范围和治疗选择决策。虽然这是朝着更具包容性的基于价值的评估（VBA）方法迈出的重要一步，但这些框架的某些方面基于薄弱的方法，可能导致误导性的建议或决策。在本文中，采用基于多属性价值理论（MAVT）的多准则决策分析（MCDA）方法过程来构建多标准评价模型。遵循一个五阶段的模型构建过程，采用自上而下的“以价值为中心的思维”方法，涉及文献回顾和专家咨询。构建了一个通用的价值树，以捕获决策者在卫生技术评估（HTA）背景下评估新药价值的关注点，并与决策理论保持一致。生成的价值树（Advance Value Tree）由三个层次的标准（顶层标准集群、中层标准、底层子标准或属性）组成，涉及五个可以明确衡量和评估的关键领域：（a）疾病负担，（b）治疗效果，（c）安全概况，（d）创新水平和（e）社会经济影响。介绍了多种 MAVT 建模技术，用于对模型进行操作化（即估计）、对替代治疗方案进行评分、为标准分配相对重要性权重，以及对评分和权重进行组合。总体而言，这些 MCDA 建模技术的组合用于在通用价值树中引出和构建价值偏好，为以结构化和透明的方式全面衡量价值提供了一个新的价值框架（Advance Value Framework）。鉴于其灵活性可以满足各种需求并在不同环境中易于适应，Advance Value Framework 可以作为评估者和支付者的决策支持工具，帮助评估和报销新药。
