BACKGROUNDS This study analyzes the effectiveness of different information criteria for the selection of covariance structures, extending it to different missing data mechanisms, the maintenance and adjustment of the mean structures, and matrices. METHOD The Monte Carlo method was used with 1,000 simulations, SAS 9.4 statistical software and a partially repeated measures design (p=2; q=5). The following variables were manipulated: a) the complexity of the model; b) sample size; c) matching of covariance matrices and sample size; d) dispersion matrices; e) the type of distribution of the variable; f) the non-response mechanism. RESULTS The results show that all information criteria worked well in Scenario 1 for normal and non-normal distributions with heterogeneity of variance. However, in Scenarios 2 and 3, all were accurate with the ARH matrix, whereas AIC, AICCR and HQICR worked better with TOEP and UN. When the distribution was not normal, AIC and AICCR were only accurate in Scenario 3, more heterogeneous and unstructured matrices, with complete cases, MAR and MCAR. CONCLUSIONS In order to correctly select the matrix it is advisible to analyze the heterogeneity, sample size and distribution of the data.

中文翻译：

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五个信息标准在重复测量设计中区分具有缺失数据的协方差结构的敏感性。

背景本研究分析了选择协方差结构的不同信息标准的有效性，将其扩展到不同的缺失数据机制、均值结构和矩阵的维护和调整。方法 蒙特卡罗方法与 1,000 次模拟、SAS 9.4 统计软件和部分重复测量设计（p=2；q=5）一起使用。操纵了以下变量： a) 模型的复杂性；b) 样本量；c) 协方差矩阵和样本大小的匹配；d) 色散矩阵；e) 变量的分布类型；f) 不回应机制。结果 结果表明，对于具有方差异质性的正态和非正态分布，所有信息标准在情景 1 中都运行良好。然而，在场景 2 和 3 中，所有的 ARH 矩阵都是准确的，而 AIC、AICCR 和 HQICR 与 TOEP 和 UN 合作得更好。当分布不正常时，AIC 和 AICCR 仅在场景 3 中准确，更多异构和非结构化矩阵，具有完整案例，MAR 和 MCAR。结论 为了正确选择矩阵，建议分析数据的异质性、样本大小和分布。