Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Department of Biomedical Data Sciences, Section Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands.
Nephrol Dial Transplant. 2019 Apr 1;34(4):561-566. doi: 10.1093/ndt/gfy128.
Clinical epidemiological studies often focus on investigating the underlying causes of disease. For instance, a nephrologist may be interested in the association between blood pressure and the development of chronic kidney disease (CKD). However, instead of focusing on the mere occurrence of CKD, the decline of kidney function over time might be the outcome of interest. For examining this kidney function trajectory, patients are typically followed over time with their kidney function estimated at several time points. During follow-up, some patients may drop out earlier than others and for different reasons. Furthermore, some patients may have greater kidney function at study entry or faster kidney function decline than others. Also, a substantial heterogeneity may exist in the number of kidney function estimates available for each patient. This heterogeneity with respect to kidney function, dropout and number of kidney function estimates is important to take into account when estimating kidney function trajectories. In general, two methods are used in the literature to estimate kidney function trajectories over time: linear regression to estimate individual slopes and the linear mixed-effects model (LMM), i.e. repeated measures analysis. Importantly, the linear regression method does not properly take into account the above-mentioned heterogeneity, whereas the LMM is able to retain all information and variability in the data. However, the underlying concepts, use and interpretation of LMMs are not always straightforward. Therefore we illustrate this using a clinical example and offer a framework of how to model and interpret the LMM.
临床流行病学研究通常侧重于研究疾病的根本原因。例如，肾病学家可能对血压与慢性肾脏病（CKD）发展之间的关系感兴趣。然而，与其仅仅关注 CKD 的发生，更关注的可能是随着时间的推移肾功能的下降。为了检查这种肾功能轨迹，通常会随着时间的推移对患者进行随访，在几个时间点估计他们的肾功能。在随访期间，由于各种原因，一些患者可能比其他人更早退出。此外，一些患者在研究开始时可能具有更高的肾功能或更快的肾功能下降。而且，每位患者可用的肾功能估计数量也存在很大的异质性。在估计肾功能轨迹时，必须考虑到与肾功能、退出和肾功能估计数量有关的这种异质性。一般来说，文献中有两种方法用于随时间估计肾功能轨迹：线性回归估计个体斜率和线性混合效应模型（LMM），即重复测量分析。重要的是，线性回归方法没有正确考虑到上述异质性，而 LMM 能够保留数据中的所有信息和变异性。然而，LMM 的基本概念、用途和解释并不总是很清楚。因此，我们将使用一个临床示例来说明这一点，并提供一个如何对 LMM 进行建模和解释的框架。
