Supplementary Material for: Interpretation of change in novel digital measures: a statistical review and tutorial

Background
Novel clinical measures assessed by a digital health technology tool require thresholds to interpret change over time, such as the minimal clinically important difference (MCID). Establishing such thresholds is a key component of clinical validation, facilitating understanding of relevant treatment effects.

Summary
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.

Key Messages
This paper provides an overview of statistical methodologies to estimate thresholds for the interpretation of change.

背景
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（MCID）。确立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。

摘要
许多推导患者报告结局解释性阈值的方法可应用于数字化临床指标。本文介绍了解释性阈值使用的理论背景，包括基于感知重要性与测量误差的阈值差异，以及群体层面与个体层面解释的阈值差异。随后，我们综述了估算此类阈值的方法，包括锚定法。我们使用临床试验中可穿戴设备测量的咳嗽频率计数数据对这些方法进行了说明。

核心信息
本文概述了用于估算变化解释阈值的统计方法。