Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.

中文翻译：

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从生态瞬时评估获得的急性和慢性疼痛患者报告结果的统计模型。

生态瞬时评估 (EMA) 允许在正常环境中以高分辨率收集参与者报告的结果 (PRO)，包括疼痛，并减少回忆偏差。生态瞬时评估是疼痛研究的重要组成部分，提供有关个体疼痛的频率、强度和干扰程度的详细信息。然而，目前还没有普遍同意的标准来将使用 EMA 重复 PRO 评估的疼痛测量结果总结为单一的、有临床意义的疼痛测量结果。在这里，我们量化从 EMA 获得的疼痛结果摘要（例如平均值和中位数）的准确性以及对这些摘要进行阈值化以获得慢性疼痛状态的二元临床终点（是/否）的效果。数据应用和模拟表明二值化经验估计（例如样本均值、随机截距线性混合模型）可以表现良好。然而，考虑疼痛评分平均值和变异性之间非线性关系的线性混合效应建模估计器在量化真实平均疼痛方面表现更好，并将估计误差减少高达 50%，对于具有更多可变疼痛评分的个体来说，改进更大。我们还表明，二值化疼痛评分（例如，<3 和≥3）可能导致统计功效的大幅损失（40%-50%）。因此，当使用 EMA 检查疼痛结果时，使用整个量表 (0-10) 的线性混合模型优于将结果分为 2 组（<3 和 ≥3），提供更大的统计功效和敏感性。