Comparison of methods for analyzing longitudinal binary outcomes: cognitive status as an example
Comparison of methods for analyzing longitudinal binary outcomes: cognitive status as an example
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DOI：
10.1080/13607860310001594727
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发表时间：
2003-11-01
影响因子：
3.4
通讯作者：
Fillenbaum, GG
中科院分区：
文献类型：
--
作者：
Kuchibhatla, M;Fillenbaum, GG
纵向数据生成相关观察结果。忽略相关性可能会导致标准误差的错误估计，从而导致参数的错误推断。在此使用的示例中，使用标准逻辑回归、使用广义估计方程 (GEE) 拟合的总体平均 (PA) 模型以及随机截距模型来对三年和六年后基线的二元结果进行建模。结果表明，社区居住老年人样本中存在认知障碍与无认知障碍。这些模型包括时不变（年龄、性别）和时变（时间、与时间的相互作用）协变量。随机截取模型的绝对估计值大于标准 Logistic 模型和 GEE 模型的绝对估计值。与使用考虑时间依赖性的 GEE 拟合的模型相比，标准逻辑回归模型高估了时变协变量（例如时间、日常生活活动问题所导致的时间）的标准误，并低估了时变协变量（例如年龄和性别）的标准误。随机截距模型的标准误差大于逻辑回归和 GEE 模型的标准误差。模型（GEE 或随机截距）的选择取决于研究问题和协变量的性质。当对受试者间效应感兴趣时，群体平均方法是合适的，当受试者特定效应很重要时，随机效应是有用的。
Longitudinal data generate correlated observations. Ignoring correlation can lead to incorrect estimation of standard errors, resulting in incorrect inferences of parameters. In the example used here, standard logistic regression, a population-averaged (PA) model fit using generalized estimating equations (GEE), and random-intercept models are used to model binary outcomes at baseline, three and six years later. The outcomes indicate cognitive impairment versus no cognitive impairment in a sample of community dwelling elders. The models include both time-invariant (age, gender) and time-varying (time, interactions with time) covariates. The absolute estimates from random-intercept models are larger than those of both standard logistic and GEE models. Compared to the model fit using GEE that accounts for time dependency, standard logistic regression models overestimate standard errors of time-varying covariates (such as time, and time by problems with activities of daily living), and underestimate the standard errors of time-invariant covariates (such as age and gender). The standard errors from the random-intercept model are larger than those from logistic regression and GEE models. The choice of models, GEE or random-intercept, depends on the research question and the nature of the covariates. Population-averaged methods are appropriate when between-subjects effects are of interest, and random-effects are useful when subject-specific effects are important.