Trigg Andrew, Ratitch Bohdana, Kruesmann Frank, Majumder Madhurima, Parfionovas Andrejus, Krahn Ulrike
Medical Affairs Statistics, Bayer plc, Reading, Berkshire, UK.
Statistics and Data Insights, Bayer Inc., Mississauga, ON, Canada.
Digit Biomark. 2025 Feb 3;9(1):52-66. doi: 10.1159/000543899. eCollection 2025 Jan-Dec.
Novel clinical measures assessed by a digital health technology tool require thresholds to interpret change over time, such as the minimal clinically important difference. Establishing such thresholds is a key component of clinical validation, facilitating understanding of relevant treatment effects.
Many of the approaches to derive interpretative thresholds for patient-reported outcomes can be applied to digital clinical measures. We present theoretical background to the use of interpretative thresholds, including the distinction between thresholds based on perceived importance versus measurement error, and thresholds for group- versus individual-level interpretations. We then review methods to estimate such thresholds, including anchor-based approaches. We illustrate the methods using data on cough frequency counts as measured by a wearable device in a clinical trial.
This paper provides an overview of statistical methodologies to estimate thresholds for the interpretation of change.
通过数字健康技术工具评估的新型临床指标需要阈值来解释随时间的变化，例如最小临床重要差异。确定此类阈值是临床验证的关键组成部分，有助于理解相关治疗效果。
许多用于得出患者报告结局的解释性阈值的方法可应用于数字临床指标。我们介绍了使用解释性阈值的理论背景，包括基于感知重要性与测量误差的阈值之间的区别，以及用于组水平与个体水平解释的阈值。然后我们回顾了估计此类阈值的方法，包括基于锚定的方法。我们使用一项临床试验中可穿戴设备测量的咳嗽频率计数数据来说明这些方法。
本文概述了用于估计变化解释阈值的统计方法。
