The impact of a misspecified random-effects distribution on the estimation and the performance of inferential procedures in generalized linear mixed models
The impact of a misspecified random-effects distribution on the estimation and the performance of inferential procedures in generalized linear mixed models
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DOI：
10.1002/sim.3157
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发表时间：
2008-07-20
影响因子：
2
通讯作者：
Molenberghs, G.
中科院分区：
文献类型：
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作者：
Litiere, S.;Alonso, A.;Molenberghs, G.
广义线性混合模型（GLMM）中的估计通常基于最大似然理论，假设基本概率模型被正确指定。然而，这种假设的有效性有时很难验证。在本文中，我们研究，通过模拟，误指定的随机效应分布的估计和假设检验的GLS-T的影响。结果表明，极大似然估计是不一致的存在误指定。在平均结构参数中引起的偏置一般是小的，只要潜在的随机效应分布的可变性也是小的。然而，对这种变异性的估计总是有严重的偏差。鉴于方差分量是研究真实分布变异性的唯一工具，因此很难评估均值结构估计是否存在问题。第一类错误率和常用的推理程序的力量也受到严重影响。如果模型中包含一个以上的随机效应，则情况会恶化。此外，我们建议通过敏感性分析，考虑几个随机效应分布来处理可能的误指定。所有的结果都是用精神分裂症临床试验的数据来说明的。版权所有（C）2007约翰威利父子有限公司
Estimation in generalized linear mixed models (GLMMs) is often based on maximum likelihood theory, assuming that the underlying probability model is correctly specified. However, the validity of this assumption is sometimes difficult to verify. In this paper we study, through simulations, the impact of misspecifying the random-effects distribution on the estimation and hypothesis testing in GLMMs. It is shown that the maximum likelihood estimators are inconsistent in the presence of misspecification. The bias induced in the mean-structure parameters is generally small, as far as the variability of the underlying random-effects distribution is small as well. However, the estimates of this variability are always severely biased. Given that the variance components are the only tool to study the variability of the true distribution, it is difficult to assess whether problems in the estimation of the mean structure occur. The type I error rate and the power of the commonly used inferential procedures are also severely affected. The situation is aggravated if more than one random effect is included in the model. Further, we propose to deal with possible misspecification by way of sensitivity analysis, considering several random-effects distributions. All the results are illustrated using data from a clinical trial in schizophrenia. Copyright (C) 2007 John Wiley & Sons, Ltd.