THE EFFECTS OF MIXTURE DISTRIBUTION MISSPECIFICATION WHEN FITTING MIXED-EFFECTS LOGISTIC-MODELS
THE EFFECTS OF MIXTURE DISTRIBUTION MISSPECIFICATION WHEN FITTING MIXED-EFFECTS LOGISTIC-MODELS
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DOI：
10.1093/biomet/79.4.755
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发表时间：
1992-12-01
期刊：
影响因子：
2.7
通讯作者：
KALBFLEISCH, JD
中科院分区：
文献类型：
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作者：
NEUHAUS, JM;HAUCK, WW;KALBFLEISCH, JD
混合效应逻辑模型通常用于分析以集群或群组形式收集的二元响应数据。假设响应在聚类内遵循逻辑模型，截距根据指定的概率分布G在聚类间变化。在本文中，我们研究了混合效应逻辑回归分析的性能时，模型的主要组成部分，混合分布，是错误的。我们表明，当混合分布是错误的，模型参数的估计，包括协变量的影响，通常是渐近偏置，即不一致的。然而，我们提出了一些近似值，这表明估计协变量效应的偏倚幅度通常很小。这些研究结果证实了一组模拟，这也表明，有效的方差估计协变量的影响时，混合分布是错误的。
Mixed-effects logistic models are often used to analyze binary response data which have been gathered in clusters or groups. Responses are assumed to follow a logistic model within clusters, with an intercept which varies across clusters according to a specified probability distribution G. In this paper we examine the performance of mixed-effects logistic regression analysis when a main component of the model, the mixture distribution, is misspecified. We show that, when the mixture distribution is misspecified, estimates of model parameters, including the effects of covariates, typically are asymptotically biased, i.e. inconsistent. However, we present some approximations which suggest that the magnitude of the bias in the estimated covariate effects is typically small. These findings are corroborated by a set of simulations which also suggest that valid variance estimates of estimated covariate effects can be obtained when the mixture distribution is misspecified.