Analysis of partially observed clustered data using generalized estimating equations and multiple imputation
Abstract. Clustered data arise in many settings, particularly within the social and
biomedical sciences. For example, multiple-source reports are commonly collected
in child and adolescent psychiatric epidemiologic studies where researchers use various
informants (for instance, parents and adolescents) to provide a holistic view of
a subject’s symptoms. Fitzmaurice et al. (1995, American Journal of Epidemiology
142: 1194–1203) have described estimation of multiple-source models using a
standard generalized estimating equation (GEE) framework. However, these studies
often have missing data because additional stages of consent and assent are
required. The usual GEE is unbiased when data are missing completely at random
in the context of Little and Rubin (2002, Statistical Analysis with Missing Data
[Wiley]). This is a strong assumption that may not be tenable. Other options,
such as the weighted GEE, are computationally challenging when missingness is
nonmonotone. Multiple imputation is an attractive method to fit incomplete data
models while requiring only the less restrictive missing-at-random assumption.
Previously, estimation of partially observed clustered data was computationally
challenging. However, recent developments in Stata have facilitated using them in
practice. We demonstrate how to use multiple imputation in conjunction with a
GEE to investigate the prevalence of eating disorder symptoms in adolescents as
reported by parents and adolescents and to determine the factors associated with
concordance and prevalence. The methods are motivated by the Avon Longitudinal
Study of Parents and their Children, a cohort study that enrolled more than
14,000 pregnant mothers in 1991–92 and has followed the health and development
of their children at regular intervals. While point estimates for the missing-at-random
model were fairly similar to those for the GEE under missing completely
at random, the missing-at-random model had smaller standard errors and required
less stringent assumptions regarding missingness.
View all articles by these authors:
Kathryn M. Aloisio, Nadia Micali, Sonja A. Swanson, Alison Field, Nicholas J. Horton
View all articles with these keywords:
ALSPAC study, eating disorders, multiple informants, weighted estimating equations, generalized estimating equations, multiple imputation, missing data, missing at random, missing completely at random
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