GEE estimation of a misspecified time-varying covariate: an example with the effect of alcoholism treatment on medical utilization.
GEE estimation of a misspecified time-varying covariate: an example with the effect of alcoholism treatment on medical utilization.
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错误指定的时变协变量的 GEE 估计：酒精中毒治疗对医疗利用影响的示例。
DOI：
10.1002/sim.1966
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Eberly,LynnE
中科院分区：
文献类型：
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作者：
Wall,MelanieM;Dai,Yu;Eberly,LynnE
广义估计方程（GEE）方法广泛用于纵向数据分析，特别是当结果变量为非高斯分布时。在温和的监管条件下，参数估计是一致的，其渐近方差是有效的。在一项针对酒精中毒患者的观察性研究中，我们将GEE方法应用于一家大型国家管理式医疗机构医疗利用记录的纵向计数数据。卫生服务研究的问题是，与治疗前相比，参与酒精中毒治疗后患者的医疗利用是否发生了变化。因此，关注的主要效应是一个时变协变量，表明患者是否接受过治疗。GEE在五个不同的工作相关性和混合结果的意义上的治疗effects.Because的大样本量，即8485例患者的平均46个重复测量每例患者，不同的工作相关性结构产生的估计值之间的差异是可疑的。结果表明，这些差异可能是由于边际均值模型中的时变协变量被错误指定而引起的。进行模拟研究，以证明在边际均值结构中时变协变量的错误设定可能导致GEE结果在各种工作相关结构选择中的差异。版权所有© 2004年约翰威利父子有限公司。
The generalized estimation equation (GEE) method is widely used in longitudinal data analysis, particularly when the outcome variable is non‐Gaussian distributed. Under mild regulatory conditions, the parameter estimates are consistent and their asymptotic variances are efficient. In an observational study focusing on alcoholism patients, we applied the GEE method to longitudinal count data from medical utilization records from a large national managed care organization. The health services research question was whether there was a change in medical utilization for patients after engaging in alcoholism treatment as compared to before treatment. Thus, the main effect of interest was a time‐varying covariate indicating whether the patient had undergone treatment yet or not. GEE under five different working correlations was employed and mixed results regarding the significance of the treatment effect were found. Because of the large sample size, i.e. 8485 patients with an average of 46 repeated measurements per patient, differences across the estimates produced by the different working correlation structures was suspicious. It is shown that these differences are maybe caused by the fact that the time‐varying covariate in the marginal mean model is misspecified. A simulation study is performed to demonstrate that misspecification of the time‐varying covariate in the marginal mean structure can cause differences in GEE results across various choices of working correlation structure. Copyright © 2004 John Wiley & Sons, Ltd.