A consistent estimator for logistic mixed effect models.
A consistent estimator for logistic mixed effect models.
复制标题
逻辑混合效应模型的一致估计量。
DOI：
10.1002/cjs.11482
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Sinha,Samiran
中科院分区：
文献类型：
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作者：
Wei,Yizheng;Ma,Yanyuan;Garcia,TanyaP;Sinha,Samiran
我们提出了一种具有随机斜率的logistic混合效应模型参数估计的一致和局部有效的方法。我们的方法放松了两个典型的假设：随机效应是正态分布的，协变量和随机效应相互独立。在健康研究中，坚持这些假设是特别困难的，在许多情况下，我们在长期研究中设计实验和收集数据的资源有限，而其他领域的新发现可能会出现，表明违反这些假设。因此，有一个对这种违反具有鲁棒性的估计器是至关重要的；然后我们可以更好地利用利用各种有价值的资源收集到的现有数据。我们的方法推广了Garcia & Ma（2016）中提出的框架，该框架也处理逻辑混合效应模型，但只考虑随机截取。仿真研究表明，即使在独立性和正态性假设被违反的情况下，我们提出的估计量仍然保持一致。这与传统的极大似然估计量形成了有利的对比，当协变量和随机效应之间存在依赖性时，极大似然估计量可能不一致。这项工作在亨廷顿氏病研究中的应用表明，通过对认知表现的评估，疾病诊断可以得到加强。加拿大统计杂志47:140-156；2019©2019加拿大统计学会
We propose a consistent and locally efficient method of estimating the model parameters of a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where, in many cases, we have limited resources to design experiments and gather data in long‐term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial to have an estimator that is robust to such violations; then we could make better use of current data harvested using various valuable resources. Our method generalizes the framework presented in Garcia & Ma (2016) which also deals with a logistic mixed effect model but only considers a random intercept. A simulation study reveals that our proposed estimator remains consistent even when the independence and normality assumptions are violated. This contrasts favourably with the traditional maximum likelihood estimator which is likely to be inconsistent when there is dependence between the covariates and random effects. Application of this work to a study of Huntington's disease reveals that disease diagnosis can be enhanced using assessments of cognitive performance.The Canadian Journal of Statistics47: 140–156; 2019 © 2019 Statistical Society of Canada