Model diagnostic tests for selecting informative correlation structure in correlated data

• 成果类型: Article
• 署名作者: Qu, Annie; Lee, J. Jack; Lindsay, Bruce G.
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Texas System; UTMD Anderson Cancer Center; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asn051
• 发表日期: 2008
• 页码: 891905
• 关键词: GENERALIZED ESTIMATING EQUATIONS; quadratic inference functions; maximum-likelihood-estimation; working correlation structure; longitudinal data; COVARIANCE-STRUCTURES; performance; matrix

摘要

In the generalized method of moments approach to longitudinal data analysis, unbiased estimating functions can be constructed to incorporate both the marginal mean and the correlation structure of the data. Increasing the number of parameters in the correlation structure corresponds to increasing the number of estimating functions. Thus, building a correlation model is equivalent to selecting estimating functions. This paper proposes a chi-squared test to choose informative unbiased estimating functions. We show that this methodology is useful for identifying which source of correlation it is important to incorporate when there are multiple possible sources of correlation. This method can also be applied to determine the optimal working correlation for the generalized estimating equation approach.