Data Sheet 1_Pain assessment on a numerical scale with uncertainty intervals: a proof-of-concept simulation study.docx

BackgroundReliable and validated scores assessing pain-related outcomes are an essential component of pain management. Point estimates, e.g., on the numeric rating scale (NRS), are widely used. Given the broad spectrum of physiological and psychological factors involved in a patient's pain experience, these point estimates entail inherent uncertainty. To account for this uncertainty, we propose a statistical framework featuring uncertainty intervals on a numerical scale assessing pain intensity.
MethodsWe describe a non-parametric statistical method to estimate the effectiveness of a pain intervention when patients provide an uncertainty interval of pain intensity rather than a single point estimate. We consider pain intensities on a generic numerical pain scale (NPS) ranging from 0 to 10 and illustrate the method's performance with proof-of-concept simulation studies and sensitivity analyses.
ResultsThe simulation studies demonstrate that the non-parametric method can derive correct estimates of the average treatment effects in idealized settings. Importantly, the method can represent the traditional pain assessment with point estimates when the widths of the uncertainty intervals are gradually decreased toward the mean of the uncertainty interval.
ConclusionWe proposed a new statistical framework to account for patient-specific uncertainties in pain intensity as measured on a numerical scale. The clinical importance of the method lies in its ability to reflect the large heterogeneity of individual pain experiences and the possibility of investigating pain-related aspects that go beyond a traditional pain assessment with point estimates. Future clinical studies are required to assess the method's clinical validity and utility.

背景：可靠且经过验证的疼痛相关结局评分是疼痛管理的必要组成部分。数值评定量表（Numeric Rating Scale, NRS）这类点估计值应用广泛。鉴于患者的疼痛体验涉及广泛的生理与心理因素，此类点估计值本身存在固有不确定性。为应对这一不确定性，我们提出一种统计框架，该框架在评估疼痛强度的数值量表上引入不确定性区间。
方法：我们描述了一种非参数统计方法，用于在患者提供疼痛强度的不确定性区间而非单点估计值时，估算疼痛干预的效果。我们以取值范围为0至10的通用疼痛数值量表（Generic Numerical Pain Scale, NPS）上的疼痛强度为研究对象，并通过概念验证模拟研究与敏感性分析验证该方法的性能。
结果：模拟研究表明，该非参数方法可在理想化场景下准确估算平均治疗效应。值得注意的是，当不确定性区间的宽度逐步向其均值收窄时，该方法可还原传统的单点估计疼痛评估方式。
结论：我们提出了一种全新的统计框架，用于考量数值量表测量的疼痛强度所对应的个体特异性不确定性。该方法的临床价值在于，其能够反映个体疼痛体验的显著异质性，且可用于探索超越传统单点估计疼痛评估的疼痛相关维度。未来仍需开展临床研究，以评估该方法的临床有效性与应用价值。