为何在使用电子健康记录数据进行纵向分析时应提取推荐就诊间隔：考察就诊机制及对非随机评估的敏感性
Why recommended visit intervals should be extracted when conducting longitudinal analyses using electronic health record data: examining visit mechanism and sensitivity to assessment not at random
AI总结：
本文提出利用电子健康记录中的医生推荐就诊间隔来刻画非规则评估过程并考察非随机评估敏感性，通过在幼年皮肌炎队列中的应用验证了该方法能显著提高纵向分析严谨性。
摘要：
电子健康记录（EHRs）为生成丰富的纵向数据集提供了一种高效途径。然而，由于患者是按需就诊，评估时间通常是不规则的，并且可能与患者的健康状况相关。未能考虑这种信息性评估过程可能导致对疾病进程的估计存在偏差。在本文中，我们展示了如何通过利用患者EHR中常被忽视的信息——医生推荐的两次就诊之间的间隔——来改进对疾病轨迹的估计。具体而言，我们展示了推荐间隔如何用于刻画评估过程，以及如何用于考察结果对非随机评估（ANAR）的敏感性。我们在一项基于诊所的幼年皮肌炎（JDM）队列研究中说明了所提出的方法。在这项研究中，我们发现推荐间隔解释了评估时间中78%的变异性。在ANAR的一个特定情形下，我们假设疾病恶化导致患者比推荐时间更早就诊，估计的人群平均疾病活动轨迹相对于假设随机评估的轨迹向下偏移。这些结果表明，推荐间隔在提高分析严谨性方面发挥着关键作用，因为它们使我们能够评估随机评估（AAR）假设的合理性以及结果对该假设偏离的敏感性。因此，我们建议使用不规则纵向数据的研究应提取推荐就诊间隔，并遵循我们的流程将其纳入分析。
英文摘要：
Electronic health records (EHRs) provide an efficient approach to generating rich longitudinal datasets. However, since patients visit as needed, the assessment times are typically irregular and may be related to the patient's health. Failing to account for this informative assessment process could result in biased estimates of the disease course. In this paper, we show how estimation of the disease trajectory can be enhanced by leveraging an underutilized piece of information that is often in the patient's EHR: physician-recommended intervals between visits. Specifically, we demonstrate how recommended intervals can be used in characterizing the assessment process, and in investigating the sensitivity of the results to assessment not at random (ANAR). We illustrate our proposed approach in a clinic-based cohort study of juvenile dermatomyositis (JDM). In this study, we found that the recommended intervals explained 78% of the variability in the assessment times. Under a specific case of ANAR where we assumed that a worsening in disease led to patients visiting earlier than recommended, the estimated population average disease activity trajectory was shifted downward relative to the trajectory assuming assessment at random. These results demonstrate the crucial role recommended intervals play in improving the rigour of the analysis by allowing us to assess both the plausibility of the AAR assumption and the sensitivity of the results to departures from this assumption. Thus, we advise that studies using irregular longitudinal data should extract recommended visit intervals and follow our procedure for incorporating them into analyses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
