Optimal design of longitudinal data analysis using generalized estimating equation models.
Optimal design of longitudinal data analysis using generalized estimating equation models.
复制标题
使用通用估计方程模型的纵向数据分析的最佳设计。
DOI：
10.1002/bimj.201600107
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发表时间：
2017-03
期刊：
影响因子：
--
通讯作者：
Colditz GA
中科院分区：
文献类型：
--
作者：
Liu J;Colditz GA
在生物医学研究和临床试验中，纵向研究经常被用来评价治疗效果。在样本量计算和分析中必须考虑受试者内的关联模式。Liang和Zeger提出的广义估计方程（GEE）是分析这类研究的重要方法之一，该方程引入了“工作相关结构”，并且被试体内的关联模式依赖于一个由ρ表示的关联参数向量。基于Liu和Liang的GEE方法，得到了线性和Logistic回归模型中两组比较的显式样本容量公式。对于集群随机试验（CRT），研究人员提出了在集群和个人水平的最佳样本量作为抽样成本和集群内相关系数（ICC）的函数。在这些方法中，最佳样本量强烈依赖于ICC。然而，对于随机分组和多中心试验，ICC通常是未知的。为了克服这一缺点，货车Breukelen等人考虑了一系列可能的ICC值，并根据相对效率（RE）和预算和成本约束下的效率提出了最大最小设计（MMD）。本文提出了在固定预算条件下，工作相关矩阵可交换的GEE模型的最优样本容量和重复测量次数，其中“最优”是指在给定的抽样预算条件下的最大功效。对于已知的参数值ρ，导出了样本容量与重复测量次数的关系式，对于未知的参数值ρ，给出了一种简单的算法。在实践中的应用进行了讨论。我们还讨论了当工作相关矩阵为AR（1）时最优设计的存在性。我们提出的方法可以扩展的情况下，当真实和工作的相关矩阵是不同的。
Longitudinal studies are often applied in biomedical research and clinical trials to evaluate the treatment effect. The association pattern within the subject must be considered in both sample size calculation and the analysis. One of the most important approaches to analyze such a study is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which “working correlation structure” is introduced and the association pattern within the subject depends on a vector of association parameters denoted by ρ. The explicit sample size formulas for two-group comparison in linear and logistic regression models are obtained based on the GEE method by Liu and Liang. For cluster randomized trials (CRTs), researchers proposed the optimal sample sizes at both the cluster and individual level as a function of sampling costs and the intracluster correlation coefficient (ICC). In these approaches, the optimal sample sizes depend strongly on the ICC. However, the ICC is usually unknown for cluster randomized and multicenter trials. To overcome this shortcoming, Van Breukelen et al consider a range of possible ICC values identified from literature reviews and present Maximin designs (MMDs) based on relative efficiency (RE) and efficiency under budget and cost constraints. In this paper, the optimal sample size and number of repeated measurements using GEE models with an exchangeable working correlation matrix is proposed under the considerations of fixed budget, where “optimal” refers to maximum power for a given sampling budget. The equations of sample size and number of repeated measurements for a known parameter value ρ are derived and a straightforward algorithm for unknown ρ is developed. Applications in practice are discussed. We also discuss the existence of the optimal design when an AR(1) working correlation matrix is assumed. Our proposed method can be extended under the scenarios when the true and working correlation matrix are different.
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