Unbiased estimating equations from working correlation models for irregularly timed repeated measures

• 成果类型: Article
• 署名作者: Wang, YG; Carey, VJ
• 署名单位: Harvard University; National University of Singapore; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard Medical School
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000001178
• 发表日期: 2004
• 页码: 845-853
• 关键词: GENERALIZED ESTIMATING EQUATIONS; longitudinal data; linear-models; binary data; quasi-likelihood; parameters

摘要

The method of generalized estimating equation-, (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.