VA Eastern Colorado Health Care System, 13611 E. Colfax Ave, A151, Aurora, Denver, CO, 80045, USA.
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Box B119, 13001 E. 17th Place, Aurora, CO, 80045, USA.
BMC Med Res Methodol. 2018 Jul 6;18(1):74. doi: 10.1186/s12874-018-0517-7.
Multilevel models for non-normal outcomes are widely used in medical and health sciences research. While methods for interpreting fixed effects are well-developed, methods to quantify and interpret random cluster variation and compare it with other sources of variation are less established. Random cluster variation, sometimes referred to as general contextual effects (GCE), may be the main focus of a study; therefore, easily interpretable methods are needed to quantify GCE. We propose a Reference Effect Measure (REM) approach to 1) quantify GCE and compare it to individual subject and cluster covariate effects, and 2) quantify relative magnitudes of GCE and variation from sets of measured factors.
To illustrate REM, we consider a two-level mixed logistic model with patients clustered within hospitals and a random intercept for hospitals. We compare patients at hospitals at given percentiles of the estimated random effect distribution to patients at a median or 'reference' hospital. These estimates are then compared numerically and graphically to individual fixed effects to quantify GCE in the context of effects of other measured variables (aim 1). We then extend this approach by comparing variation from the random effect distribution to variation from sets of fixed effects to understand their magnitudes relative to overall outcome variation (aim 2).
Using an example of initiation of rhythm control treatment in atrial fibrillation (AF) patients within the Veterans Affairs (VA), we use REM to demonstrate that random variation across hospitals (GCE) in initiation of treatment is substantially greater than that due to most individual patient factors, and explains at least as much variation in treatment initiation as do all patient factors combined. These results are contrasted with a relatively small GCE compared with patient factors in 1 year mortality following hospitalization for AF patients.
REM provides a means of quantifying random effect variation (GCE) with multilevel data and can be used to explore drivers of outcome variation. This method is easily interpretable and can be presented visually. REM offers a simple, interpretable approach for evaluating questions of growing importance in the study of health care systems.
多水平模型常用于医学和健康科学研究中的非正态结果。虽然解释固定效应的方法已经很完善，但量化和解释随机聚类变异并将其与其他变异源进行比较的方法则不太成熟。随机聚类变异，有时也称为一般背景效应（GCE），可能是研究的主要关注点；因此，需要易于解释的方法来量化 GCE。我们提出了一种参考效应度量（REM）方法，用于 1）量化 GCE 并将其与个体受试者和聚类协变量效应进行比较，以及 2）量化 GCE 的相对大小和来自一组测量因素的变异。
为了说明 REM，我们考虑了一个两水平混合逻辑回归模型，其中患者按医院聚类，且医院有一个随机截距。我们将处于给定估计随机效应分布百分位数的医院的患者与处于中位数或“参考”医院的患者进行比较。然后，通过数值和图形比较这些估计值与个体固定效应，以量化其他测量变量（目标 1）背景下的 GCE。然后，我们通过比较随机效应分布的变异与固定效应集的变异来扩展这种方法，以了解它们相对于总体结果变异的大小（目标 2）。
我们使用退伍军人事务部（VA）中房颤（AF）患者节律控制治疗起始的示例，使用 REM 来证明，治疗起始方面医院之间的随机变异（GCE）远远大于大多数个体患者因素的变异，并且至少与所有患者因素的总和一样解释了治疗起始的变异。这些结果与 AF 患者住院后 1 年死亡率的 GCE 与患者因素相比相对较小形成对比。
REM 为使用多水平数据量化随机效应变异（GCE）提供了一种方法，并可用于探索结果变异的驱动因素。该方法易于解释，可通过直观方式呈现。REM 为评估医疗保健系统研究中日益重要的问题提供了一种简单、可解释的方法。
