Unbiased Estimating Equations From Working Correlation Models for Irregularly Timed Repeated Measures
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不规则时点重复测量中由工作相关模型构造无偏估计方程
- 研究问题
- 对不规则时点的重复测量，如何从灵活工作相关模型构造无偏估计方程，以改善 GEE 的求解可行性和效率？
- 方法与思路
- 作者分别利用工作相关矩阵 Cholesky 分解的导数与解耦高斯伪似然得分构造补充方程，并详细分析广义 Markov 模型。
- 材料陈述的贡献
- 摘要称计算量与一阶 GEE 相当，推导相关参数的三明治标准误并通过模拟评估，还应用于儿童肺功能重复测量。
- 阅读关注点
- 应核对方程无偏所需条件、相关模型误设下的性质，以及不规则时点处理、求解可行性和三明治标准误的表现。
- 阅读边界
- 仅据版本未注明的 OpenAlex 摘要；无偏性及稳健性的完整条件、效率改善幅度和模拟范围未说明，不能推断刊本变化。
原文摘要
The method of generalized estimating equations (GEEs) has been criticized recently for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. However, the feasibility and efficiency of GEE methods can be enhanced considerably by using flexible families of working correlation models. We propose two ways of constructing unbiased estimating equations from general correlation models for irregularly timed repeated measures to supplement and enhance GEE. The supplementary estimating equations are obtained by differentiation of the Cholesky decomposition of the working correlation, or as score equations for decoupled Gaussian pseudolikelihood. The estimating equations are solved with computational effort equivalent to that required for a first-order GEE. Full details and analytic expressions are developed for a generalized Markovian model that was evaluated through simulation. Large-sample “sandwich” standard errors for working correlation parameter estimates are derived and shown to have good performance. The proposed estimating functions are further illustrated in an analysis of repeated measures of pulmonary function in children.