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Misspecifying the covariance structure in a linear mixed model under MAR drop-out.
Statistics in Medicine ( IF 2 ) Pub Date : 2020-05-25 , DOI: 10.1002/sim.8589
Christos Thomadakis 1 , Loukia Meligkotsidou 2 , Nikos Pantazis 1 , Giota Touloumi 1
Affiliation  

Misspecification of the covariance structure in a linear mixed model (LMM) can lead to biased population parameters' estimates under MAR drop‐out. In our motivating example of modeling CD4 cell counts during untreated HIV infection, random intercept and slope LMMs are frequently used. In this article, we evaluate the performance of LMMs with specific covariance structures, in terms of bias in the fixed effects estimates, under specific MAR drop‐out mechanisms, and adopt a Bayesian model comparison criterion to discriminate between the examined approaches in real‐data applications. We analytically show that using a random intercept and slope structure when the true one is more complex can lead to seriously biased estimates, with the degree of bias depending on the magnitude of the MAR drop‐out. Under misspecified covariance structure, we compare in terms of induced bias the approach of adding a fractional Brownian motion (BM) process on top of random intercepts and slopes with the approach of using splines for the random effects. In general, the performance of both approaches was satisfactory, with the BM model leading to smaller bias in most cases. A simulation study is carried out to evaluate the performance of the proposed Bayesian criterion in identifying the model with the correct covariance structure. Overall, the proposed method performs better than the AIC and BIC criteria under our specific simulation setting. The models under consideration are applied to real data from the CASCADE study; the most plausible model is identified by all examined criteria.

中文翻译:

在MAR丢失下,线性混合模型中的协方差结构有误。

线性混合模型(LMM)中协方差结构的错误指定会导致MAR缺失下总体参数估计值的偏差。在我们未经处理的HIV感染过程中模拟CD4细胞计数的激励性例子中,经常使用随机拦截和斜率LMM。在本文中,我们根据特定的MAR退出机制,根据固定效应估计中的偏倚来评估具有特定协方差结构的LMM的性能,并采用贝叶斯模型比较标准来区分实际数据中所检查的方法应用程序。我们的分析表明,当真实截距更复杂时,使用随机截距和斜率结构会导致估计严重偏倚,其偏倚程度取决于MAR下降的幅度。在错误指定的协方差结构下,我们在诱导偏差方面比较了在随机截距和斜率之上添加分数布朗运动(BM)过程的方法与在随机效果中使用样条曲线的方法。通常,两种方法的性能都令人满意,而BM模型在大多数情况下会导致较小的偏差。进行了仿真研究,以评估提出的贝叶斯准则在识别具有正确协方差结构的模型中的性能。总体而言,在我们的特定仿真设置下,所提出的方法的性能优于AIC和BIC标准。所考虑的模型已应用于CASCADE研究的真实数据;所有检查的标准都可以确定最合理的模型。
更新日期:2020-05-25
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