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.

翻译：电子健康记录（EHRs）为生成丰富的纵向数据集提供了高效途径。然而，由于患者按需就诊，评估时间通常不规则且可能与患者的健康状况相关。若未考虑这种信息性评估过程，可能导致对疾病进程的估计产生偏差。本文通过利用患者电子健康记录中常被忽视的一类信息——医生推荐的复诊间隔，展示了如何优化疾病轨迹的估计。具体而言，我们论证了推荐间隔如何用于刻画评估过程，以及如何探究结果对非随机评估（ANAR）的敏感性。我们在基于临床的幼年皮肌炎（JDM）队列研究中展示了所提出的方法。该研究发现，推荐间隔解释了78%的就诊时间变异性。在假设疾病恶化导致患者早于推荐时间就诊的特定非随机评估情境下，估计的群体平均疾病活动度轨迹相较于随机评估假设下的轨迹整体下移。这些结果表明，推荐间隔通过允许研究者评估随机评估假设的合理性及结果对该假设偏离的敏感性，对提升分析严谨性具有关键作用。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，并遵循本文提出的方法将其纳入分析流程。