Abstract

Generalized Estimating Equations (GEE) is used to analyze repeated measurements taken from subjects at equal time intervals and is applicable in presence of missing data. In this study, we aimed to introduce Quasi-Least Squares Regression (QLS), which is an extension of GEE and applicable when time intervals are unequal, and compare model performances under different scenarios in terms of efficiency using a comprehensive simulation study. The simulated datasets were analyzed using GEE and QLS, and the results were evaluated. In the simulation study, we produced 9 datasets with 1000 replicates using 3 correlation structures and 3 different correlation values . We obtained 36 scenarios by using 4 working correlation structures on these datasets. According to the results, in general, QLS has superiority over GEE in terms of the efficiency of estimations. In GEE method, a convergence problem was encountered for "Tri-diagonal" working correlation structure. However, in QLS method, there was no problem in convergence for this correlation structure. In order to make better comparisons of GEE and QLS results, Markov working correlation structure should be applicable to GEE as well as QLS. QLS gains an advantage over GEE when repeated measurements are collected at unequal time intervals and there are missing measurements.

摘要

广义估计方程 (GEE) 用于分析以相等时间间隔从受试者中获取的重复测量值，适用于存在缺失数据的情况。在本研究中，我们旨在引入准最小二乘回归 (QLS)，它是 GEE 的扩展，适用于时间间隔不相等的情况，并使用综合仿真研究在效率方面比较不同场景下的模型性能。使用 GEE 和 QLS 分析模拟数据集，并对结果进行评估。在模拟研究中，我们使用 3 种相关结构和 3 种不同的相关值生成了 9 个数据集，每个数据集重复 1000 次。我们通过在这些数据集上使用 4 个工作相关结构获得了 36 个场景。根据结果​​，一般来说，QLS 在估计效率方面优于 GEE。在GEE方法中，“三对角线”工作相关结构遇到了收敛性问题。然而，在 QLS 方法中，这种相关结构的收敛性没有问题。为了更好地比较 GEE 和 QLS 结果，马尔可夫工作相关结构应该适用于 GEE 和 QLS。当以不相等的时间间隔收集重复测量值并且缺少测量值时，QLS 比 GEE 更具优势。马尔可夫工作相关结构应该适用于 GEE 以及 QLS。当以不相等的时间间隔收集重复测量值并且缺少测量值时，QLS 比 GEE 更具优势。马尔可夫工作相关结构应该适用于 GEE 以及 QLS。当以不相等的时间间隔收集重复测量值并且缺少测量值时，QLS 比 GEE 更具优势。