Determining minimal clinically important differences for neurodevelopmental outcome measures in Angelman syndrome

项目摘要/摘要 Angelman综合征(AS)是一种罕见的神经遗传学疾病，会导致一系列临床特征， 包括严重的发育迟缓和极其有限的功能能力。治疗性的 在过去几年中，发展努力大幅增加，有7个 目前正在进行或准备进行1期试验的制药公司。因此，有 迫切需要提高核心临床结果衡量标准的效用，以便能够 在未来的AS临床试验中有效部署。这项研究的总体目标是使用现有的 贝利婴儿量表这两个核心工具超过800次的管理数据 发展和Vinland适应行为量表，以建立有意义的变化 AS患者这两项指标的阈值。为了实现这一目标，我们将计算 对于每个测量(A)基于分布的最小临床重要差异(MCID)，以及(B) 基于锚点的MCID。基于锚的MCID与美国食品和药物管理局很好地结合在一起 美国食品和药物管理局关于建立“有意义的患者内部改变”的指导意见(见 Https://www.fda.gov/media/132505/download)，然而，即使是食品和药物管理局也承认这一经验数据 对于将临床结果评估的变化与背景联系起来是必要的。分配- 基于MCID的方法将在基于锚的方法上提供这种“现实检验”，确保 这种改变的阈值在统计上具有重要意义，在临床上也有意义。

Project Summary/Abstract Angelman syndrome (AS) is a rare neurogenetic condition that results in a host of clinical traits, including severe developmental delays and extremely limited functional abilities. Therapeutic development efforts have increased substantially in the last several years, with seven pharmaceutical companies currently conducting or preparing Phase 1 trials. As a result, there is a critical need to improve the utility of core clinical outcome measures so that they can be deployed effectively in future clinical trials in AS. The overall goal of this study is to use existing data on over 800 administrations of two core instruments, the Bayley Scales of Infant Development and the Vineland Adaptive Behavior Scales, to establish meaningful change thresholds for these two measures for individuals with AS. To achieve this goal, we will calculate for each measure (a) distribution-based minimal clinically important difference (MCID), and (b) anchor-based MCID. The anchor-based MCID aligns well with the U.S. Food and Drug Administration’s (FDA’s) guidance on establishing “meaningful within-patient change” (see https://www.fda.gov/media/132505/download), yet even the FDA recognizes that empirical data are necessary to contextualize changes on Clinical Outcome Assessments. The distribution- based MCID approach will provide this “reality check” on the anchor-based approach, ensuring that thresholds for change are both statistically significant and clinically meaningful.