Analysis of partially observed clustered data using generalized estimating equations and multiple imputation

作者：Kathryn M. Aloisio, Micali, Nadia, Sonja Alsemgeest Swanson, Alison E. Field, Nicholas J. Horton, Aloisio, Kathryn M., Micali, Nadia, Sonja Alsemgeest Swanson, Alison E. Field, Horton, Nicholas J. · 发表于：PubMed · 年份：2014 · DOI：10.22004/ag.econ.267105 · 被引用次数：101 · 研究领域：Health, Environment, Cognitive Aging、Nutritional Studies and Diet、Air Quality and Health Impacts

Clustered data arise in many settings, particularly within the social and biomedical sciences. As an example, multiple-source reports are commonly collected in child and adolescent psychiatric epidemiologic studies where researchers use various informants (e.g. parent and adolescent) to provide a holistic view of a subject's symptomatology. Fitzmaurice et al. (1995) have described estimation of multiple source models using a standard generalized estimating equation (GEE) framework. However, these studies often have missing data due to additional stages of consent and assent required. The usual GEE is unbiased when missingness is Missing Completely at Random (MCAR) in the sense of Little and Rubin (2002). This is a strong assumption that may not be tenable. Other options such as weighted generalized estimating equations (WEEs) are computationally challenging when missingness is non-monotone. Multiple imputation is an attractive method to fit incomplete data models while only requiring the less restrictive Missing at Random (MAR) assumption. Previously estimation of partially observed clustered data was computationally challenging however recent developments in Stata have facilitated their use in practice. We demonstrate how to utilize multiple imputation in conjunction with a GEE to investigate the prevalence of disordered eating symptoms in adolescents reported by parents and adolescents as well as factors associated with concordance and prevalence. The methods are motivated ...