含缺失值的重复测量资料分析在SPSS和SAS中的实现

    Data Analysis for Repeated Measurements with Missing Valuesin SPSS and SAS

    • 摘要: 目的 探讨含缺失值的重复测量资料混合线性模型分析在SPSS和SAS中的实现。 方法 应用重复测量资料的方差分析和混合线性模型分别对抑郁治疗的资料进行分析,以治疗后抑郁量表得分为分析指标,评价不同治疗方法的效果。 结果 当大量个体的重复测量数据存在缺失值时,方差分析将这些个体排除在外,而混合线性模型将存在测量数据的个体全部纳入分析,二者得出不同的统计推断。 结论 混合线性模型可以灵活处理含有缺失值的重复测量资料,能够充分利用数据信息,结果可靠。SPSS和SAS的MIXED模块都可以实现其统计分析,且在随机缺失情况下结果一致。

       

      Abstract: Objective To discuss the realization of linear mixed model in SPSS and SAS for repeated measurements with missing values. Methods Data from a depression therapy with missing values were analyzed using GLM and MIXED procedure, so as to evaluate the therapy effect. The measurements are depression levels after treatment. Results When missing values exist, analysis with GLM procedure will exclude the subjects from further calculation, while analysis with MIXED procedure will include the subjects in further calculation. They lead to different statistical results. Conclusion MIXED extends repeated measures models in GLM to allow an unequal number of repetitions and it will still be efficient under more complex situations where data units are nested in a hierarchy. SPSS and SAS both can get efficient estimators for either balanced or unbalanced data.

       

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