Effect of Smoothing in Generalized Linear Mixed Models on the Estimation of Covariance Parameters for Longitudinal Data

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2016-11-01 DOI:10.1515/ijb-2015-0026

M. Mullah, A. Benedetti

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引用次数: 4

Abstract

Abstract Besides being mainly used for analyzing clustered or longitudinal data, generalized linear mixed models can also be used for smoothing via restricting changes in the fit at the knots in regression splines. The resulting models are usually called semiparametric mixed models (SPMMs). We investigate the effect of smoothing using SPMMs on the correlation and variance parameter estimates for serially correlated longitudinal normal, Poisson and binary data. Through simulations, we compare the performance of SPMMs to other simpler methods for estimating the nonlinear association such as fractional polynomials, and using a parametric nonlinear function. Simulation results suggest that, in general, the SPMMs recover the true curves very well and yield reasonable estimates of the correlation and variance parameters. However, for binary outcomes, SPMMs produce biased estimates of the variance parameters for high serially correlated data. We apply these methods to a dataset investigating the association between CD4 cell count and time since seroconversion for HIV infected men enrolled in the Multicenter AIDS Cohort Study.

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广义线性混合模型中平滑对纵向数据协方差参数估计的影响

广义线性混合模型除了主要用于分析聚类或纵向数据外，还可以通过限制回归样条结点处拟合的变化来实现平滑。所得到的模型通常称为半参数混合模型(spmm)。我们研究了使用spmm平滑对纵向正态、泊松和二值数据的相关和方差参数估计的影响。通过仿真，我们将spmm的性能与其他更简单的估计非线性关联的方法(如分数多项式和使用参数非线性函数)进行了比较。仿真结果表明，总体而言，spmm可以很好地恢复真实曲线，并对相关参数和方差参数给出合理的估计。然而，对于二元结果，spmm对高序列相关数据的方差参数产生偏倚估计。我们将这些方法应用于一个数据集，该数据集调查了在多中心艾滋病队列研究中登记的HIV感染男性的CD4细胞计数与血清转化时间之间的关系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.