PDF(Pubmed)
Abstract：
UNASSIGNED: Missing data is prevalent in the Alzheimer\'s Disease Neuroimaging Initiative (ADNI). It is common to deal with missingness by removing subjects with missing entries prior to statistical analysis; however, this can lead to significant efficiency loss and sometimes bias. It has yet to be demonstrated that the imputation approach to handling this issue can be valuable in some longitudinal regression settings.
UNASSIGNED: The purpose of this study is to demonstrate the importance of imputation and how imputation is correctly done in ADNI by analyzing longitudinal Alzheimer\'s Disease Assessment Scale -Cognitive Subscale 13 (ADAS-Cog 13) scores and their association with baseline patient characteristics.
UNASSIGNED: We studied 1,063 subjects in ADNI with mild cognitive impairment. Longitudinal ADAS-Cog 13 scores were modeled with a linear mixed-effects model with baseline clinical and demographic characteristics as predictors. The model estimates obtained without imputation were compared with those obtained after imputation with Multiple Imputation by Chained Equations (MICE). We justify application of MICE by investigating the missing data mechanism and model assumptions. We also assess robustness of the results to the choice of imputation method.
UNASSIGNED: The fixed-effects estimates of the linear mixed-effects model after imputation with MICE yield valid, tighter confidence intervals, thus improving the efficiency of the analysis when compared to the analysis done without imputation.
UNASSIGNED: Our study demonstrates the importance of accounting for missing data in ADNI. When deciding to perform imputation, care should be taken in choosing the approach, as an invalid one can compromise the statistical analyses.
UNASSIGNED: The purpose of this study is to demonstrate the importance of imputation and how imputation is correctly done in ADNI by analyzing longitudinal Alzheimer\'s Disease Assessment Scale -Cognitive Subscale 13 (ADAS-Cog 13) scores and their association with baseline patient characteristics.
UNASSIGNED: We studied 1,063 subjects in ADNI with mild cognitive impairment. Longitudinal ADAS-Cog 13 scores were modeled with a linear mixed-effects model with baseline clinical and demographic characteristics as predictors. The model estimates obtained without imputation were compared with those obtained after imputation with Multiple Imputation by Chained Equations (MICE). We justify application of MICE by investigating the missing data mechanism and model assumptions. We also assess robustness of the results to the choice of imputation method.
UNASSIGNED: The fixed-effects estimates of the linear mixed-effects model after imputation with MICE yield valid, tighter confidence intervals, thus improving the efficiency of the analysis when compared to the analysis done without imputation.
UNASSIGNED: Our study demonstrates the importance of accounting for missing data in ADNI. When deciding to perform imputation, care should be taken in choosing the approach, as an invalid one can compromise the statistical analyses.
摘要：
■在阿尔茨海默病神经影像学计划（ADNI）中，数据缺失很普遍。通常在统计分析之前通过删除缺少条目的受试者来处理错误；但是，这可能会导致显著的效率损失，有时甚至会产生偏差。尚未证明，在某些纵向回归设置中，处理此问题的插补方法可能很有价值。
■这项研究的目的是通过分析纵向阿尔茨海默病评估量表-认知子量表13（ADAS-Cog13）得分及其与基线患者特征的关联，来证明填补的重要性以及如何在ADNI中正确进行填补。
■我们研究了1,063名患有轻度认知障碍的ADNI受试者。用线性混合效应模型对纵向ADAS-Cog13评分进行建模，以基线临床和人口统计学特征为预测因子。将未进行估算的模型估算值与通过链式方程（MICE）进行多次估算的估算值进行比较。我们通过调查缺失的数据机制和模型假设来证明MICE的应用是合理的。我们还评估了结果对插补方法选择的稳健性。
■在MICE产量有效的情况下，线性混合效应模型的固定效应估计，更严格的置信区间，从而提高了分析的效率相比，没有插补的分析。
■我们的研究证明了在ADNI中考虑缺失数据的重要性。当决定执行归因时，在选择方法时应该小心，作为一个无效的人可能会损害统计分析。
■这项研究的目的是通过分析纵向阿尔茨海默病评估量表-认知子量表13（ADAS-Cog13）得分及其与基线患者特征的关联，来证明填补的重要性以及如何在ADNI中正确进行填补。
■我们研究了1,063名患有轻度认知障碍的ADNI受试者。用线性混合效应模型对纵向ADAS-Cog13评分进行建模，以基线临床和人口统计学特征为预测因子。将未进行估算的模型估算值与通过链式方程（MICE）进行多次估算的估算值进行比较。我们通过调查缺失的数据机制和模型假设来证明MICE的应用是合理的。我们还评估了结果对插补方法选择的稳健性。
■在MICE产量有效的情况下，线性混合效应模型的固定效应估计，更严格的置信区间，从而提高了分析的效率相比，没有插补的分析。
■我们的研究证明了在ADNI中考虑缺失数据的重要性。当决定执行归因时，在选择方法时应该小心，作为一个无效的人可能会损害统计分析。
