Chang Chung-Chou H, Yang Hsiao-Ching, Tang Gong, Ganguli Mary
Department of Medicine, University of Pittsburgh, PA 15213, USA.
Int Psychogeriatr. 2009 Oct;21(5):869-78. doi: 10.1017/S104161020900876X. Epub 2009 Mar 17.
Attrition from mortality is common in longitudinal studies of the elderly. Ignoring the resulting non-response or missing data can bias study results.
1260 elderly participants underwent biennial follow-up assessments over 10 years. Many missed one or more assessments over this period. We compared three statistical models to evaluate the impact of missing data on an analysis of depressive symptoms over time. The first analytic model (generalized mixed model) treated non-response as data missing at random. The other two models used shared parameter methods; each had different specifications for dropout but both jointly modeled both outcome and dropout through a common random effect.
The presence of depressive symptoms was associated with being female, having less education, functional impairment, using more prescription drugs, and taking antidepressant drugs. In all three models, the same variables were significantly associated with depression and in the same direction. However, the strength of the associations differed widely between the generalized mixed model and the shared parameter models. Although the two shared parameter models had different assumptions about the dropout process, they yielded similar estimates for the outcome. One model fitted the data better, and the other was computationally faster.
Dropout does not occur randomly in longitudinal studies of the elderly. Thus, simply ignoring it can yield biased results. Shared parameter models are a powerful, flexible, and easily implemented tool for analyzing longitudinal data while minimizing bias due to nonrandom attrition.
在对老年人的纵向研究中，因死亡导致的失访很常见。忽略由此产生的无应答或缺失数据会使研究结果产生偏差。
1260名老年参与者在10年中接受了每两年一次的随访评估。在此期间，许多人错过了一次或多次评估。我们比较了三种统计模型，以评估缺失数据对随时间变化的抑郁症状分析的影响。第一个分析模型（广义混合模型）将无应答视为随机缺失的数据。另外两个模型使用共享参数方法；每个模型对失访有不同的设定，但都通过一个共同的随机效应联合对结果和失访进行建模。
抑郁症状的存在与女性、受教育程度较低、功能障碍、使用更多处方药以及服用抗抑郁药有关。在所有三个模型中，相同的变量与抑郁显著相关且方向相同。然而，广义混合模型和共享参数模型之间关联的强度差异很大。尽管两个共享参数模型对失访过程有不同的假设，但它们对结果的估计相似。一个模型对数据的拟合更好，另一个在计算上更快。
在对老年人的纵向研究中，失访并非随机发生。因此，简单地忽略它会产生有偏差的结果。共享参数模型是一种强大、灵活且易于实施的工具，用于分析纵向数据，同时将因非随机失访导致的偏差降至最低。
