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Publication Date

2013

Document Type

Honors Project

Department

Mathematics and Statistics

Keywords

Generalized estimating equations, Multiple imputations (Statistics), Missing observations (Statistics), Eating disorders-Statistical methods, Missing data, Multiple informants, Eating disorders, Missing at random, Missing completely at random

Abstract

Clustered data arise in many settings, particularly within the social and biomedical sciences, and re- quire sophisticated analytical methods. 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 child) to provide a holistic view of a subject's symptomatology. Fitzmaurice et al. (1995) have described estimation of multiple source models using a generalized estimating equation (GEE) framework. However, these studies often have missing data due to multiple stages of consent and willingness to participate. 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. Multiple imputation is an attractive method to t incomplete data models while only requiring the less restrictive Missing at Random (MAR) assumption. In this thesis, I 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 by the Avon Longitudinal Study of Parents and their Children (ALSPAC), 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 were fairly similar to the GEE under MCAR, the MAR model had smaller standard errors, while requiring less stringent assumptions regarding missingness. This approach is available within general purpose statistical software, and is recommended as a principled analytic approach.

Language

English

Comments

80 p. : ill. Honors project-Smith College, 2013. Includes bibliographical references (p. 78-80)

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