墨西哥健康与老龄化研究中认知变量的插补程序：评估排除数据缺失参与者所产生的偏差
Imputation Procedures for Cognitive Variables in the Mexican Health and Aging Study: Evaluating the Bias from Excluding Participants with Missing Data.
作者信息
Downer Brian, Avila Jaqueline, Chen Nai-Wei, Wong Rebeca
机构信息
University of Texas Medical Branch, School of Health Professions, Division of Rehabilitation Sciences, Galveston Texas, United States.
Brown University, School of Public Health, Department of Behavioral and Social Sciences, Center for Alcohol and Addiction Studies, Providence Rhode Island, United States.
出版信息
Real Datos Espacio. 2021 May-Aug;12(2):90-105.
Non-response of cognitive data in cohort studies is a barrier to cognitive aging research. We describe the procedures for the imputation of non-responses for cognitive data in the Mexican Health and Aging Study (MHAS). Data came from the 2001-2015 MHAS waves. We also describe the association of cognition with education, age, and other variables in 2015 with and without the imputed values. Between 12.3% and 37.9% of participants were missing data for at least one cognition variable. When we conducted the analysis with and without the imputed values, the relationships between education, age, and cognition were similar in direction and significance, but different in magnitude. Non-response of cognitive data is common and non-random in the MHAS. Investigators should use the data sets that include the imputed values, which are publicly available.
队列研究中认知数据的无应答是认知老化研究的一个障碍。我们描述了墨西哥健康与老龄化研究（MHAS）中认知数据无应答的插补程序。数据来自2001 - 2015年的MHAS各轮调查。我们还描述了在有和没有插补值的情况下，2015年认知与教育、年龄及其他变量之间的关联。12.3%至37.9%的参与者至少有一个认知变量的数据缺失。当我们在有和没有插补值的情况下进行分析时，教育、年龄和认知之间的关系在方向和显著性上相似，但在程度上不同。认知数据的无应答在MHAS中很常见且是非随机的。研究人员应使用包含插补值的数据集，这些数据集是公开可用的。
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