Kondo Masahiro, Nagashima Kengo, Isono Shiroh, Sato Yasunori
Graduate School of Health Management, Keio University, Kanagawa, Japan.
Biostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, Tokyo, Japan.
Stat Med. 2025 May;44(10-12):e70046. doi: 10.1002/sim.70046.
The relationship between two variables measured multiple times per individual has often been evaluated in clinical studies. These data are not independent; therefore, the Pearson correlation coefficient is inappropriate, and some correlation coefficients for these data have been proposed. However, in the presence of missing data, the existing methods can be biased. In this article, we proposed a weighted repeated measures correlation coefficient that provides an accurate estimate, even with missing data, in a study in which participants ideally have the same number of measurements. We also provided a bootstrap confidence interval for the weighted repeated measures correlation coefficients. We evaluated the performance of the proposed and existing methods (i.e., simple Pearson correlation coefficient, the Pearson correlation coefficient for average, average of the Pearson correlation coefficient, correlation coefficient based on analysis of covariance, and correlation coefficient based on the linear mixed-effects model) through simulations and application to actual data. In numerical evaluations using simulations, the proposed method consistently outperformed existing methods. We recommend using a weighted repeated measures correlation coefficient to handle missing values in multiple-measurement data.
在临床研究中，常常会评估个体多次测量的两个变量之间的关系。这些数据并非相互独立；因此，皮尔逊相关系数并不适用，针对这些数据已经提出了一些相关系数。然而，在存在缺失数据的情况下，现有方法可能会产生偏差。在本文中，我们提出了一种加权重复测量相关系数，即使在存在缺失数据的情况下，在参与者理想情况下具有相同测量次数的研究中，它也能提供准确的估计。我们还为加权重复测量相关系数提供了一个自助置信区间。我们通过模拟以及对实际数据的应用，评估了所提出的方法和现有方法（即简单皮尔逊相关系数、平均皮尔逊相关系数、皮尔逊相关系数的平均值、基于协方差分析的相关系数以及基于线性混合效应模型的相关系数）的性能。在使用模拟进行的数值评估中，所提出的方法始终优于现有方法。我们建议使用加权重复测量相关系数来处理多次测量数据中的缺失值。
