Assessing the Use of GEE Methods for Analyzing Continuous Outcomes from Family Studies: Strong Heart Family Study

Q3 Nursing
X. Chen, Ying Zhang, A. Fretts, T. Ali, J. Umans, R. B. Devereux, Elisa Lee, S. Cole, Yan D. Zhao
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Abstract

Background: Because of its convenience and robustness, the generalized estimating equations (GEE) method has been commonly used to fit marginal models of continuous outcomes in family studies. However, unbalanced family sizes and complex pedigree structures within each family may challenge the GEE method, which treats families as clusters with the same correlation structure. The appropriateness of using the GEE method to analyze continuous outcomes in family studies remains unclear. In this paper, we performed simulation studies to evaluate the performance of GEE in the analysis of family study data. Methods: In simulation studies, we generated data from a linear mixed effects model with individual random effects. The random effects covariance matrix is specified as twice that of the pedigree matrix from the Strong Heart Family Study (SHFS) and other hypothetical pedigree structures. A Bayesian approach that utilizes the pedigree matrix was also conducted as a benchmark to compare with GEE methods with either independent or exchangeable correlation structures. Finally, analysis with a real data example was included. Results: Our simulation results showed that GEE with independent correlation structure worked well for family data with continuous outcomes. Real data analysis revealed that all GEE and Bayesian approaches produced similar results. Conclusion: GEE model performs well on continuous outcome in family studies, and it yields estimated coefficients similar to a Bayesian model, which takes genetic relationship into account. Overall, GEE is robust to misspecification of genetic relationships among family members.
评估使用GEE方法分析家庭研究的连续结果:强心脏家庭研究
背景:广义估计方程(GEE)方法由于其方便性和稳健性,在家庭研究中被广泛用于拟合连续结局的边际模型。然而,不平衡的家庭规模和每个家庭内部复杂的谱系结构可能会挑战GEE方法,该方法将家庭视为具有相同相关结构的集群。使用GEE方法分析家庭研究中连续结果的适宜性尚不清楚。在本文中,我们进行了模拟研究,以评估GEE在分析家庭研究数据中的性能。方法:在模拟研究中,我们从具有单个随机效应的线性混合效应模型中生成数据。随机效应协方差矩阵指定为强心脏家族研究(SHFS)和其他假设谱系结构的家系矩阵的两倍。利用系谱矩阵的贝叶斯方法也作为基准,与具有独立或可交换相关结构的GEE方法进行比较。最后,结合一个实际数据实例进行了分析。结果:我们的模拟结果表明,具有独立相关结构的GEE可以很好地处理具有连续结果的家庭数据。真实的数据分析表明,所有的GEE和贝叶斯方法都产生了相似的结果。结论:GEE模型对家庭研究的连续结果表现良好,其估计系数与考虑了遗传关系的贝叶斯模型相似。总体而言,GEE对家庭成员之间遗传关系的错误描述是稳健的。
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来源期刊
Epidemiology Biostatistics and Public Health
Epidemiology Biostatistics and Public Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
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期刊介绍: Epidemiology, Biostatistics, and Public Health (EBPH) is a multidisciplinary journal that has two broad aims: -To support the international public health community with publications on health service research, health care management, health policy, and health economics. -To strengthen the evidences on effective preventive interventions. -To advance public health methods, including biostatistics and epidemiology. EBPH welcomes submissions on all public health issues (including topics like eHealth, big data, personalized prevention, epidemiology and risk factors of chronic and infectious diseases); on basic and applied research in epidemiology; and in biostatistics methodology. Primary studies, systematic reviews, and meta-analyses are all welcome, as are research protocols for observational and experimental studies. EBPH aims to be a cross-discipline, international forum for scientific integration and evidence-based policymaking, combining the methodological aspects of epidemiology, biostatistics, and public health research with their practical applications.
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