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

Kathryn M Aloisio1, Sonja A Swanson2, Nadia Micali3

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Summary

This study addresses missing data in child psychiatric research using multiple imputation with generalized estimating equations (GEE). This method is more flexible than standard GEE, offering smaller standard errors under less restrictive assumptions.

Keywords:
ALSPAC studyeating disordersgeneralized estimating equationsmissing at randommissing completely at randommissing datamultiple imputationmultiple informantsweighted estimating equations

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Area of Science:

  • Biomedical Sciences
  • Social Sciences
  • Epidemiology

Background:

  • Clustered data are common in child and adolescent psychiatric epidemiology.
  • Missing data is a frequent challenge in these studies, often violating the Missing Completely at Random (MCAR) assumption required by standard generalized estimating equations (GEE).
  • Existing methods like weighted GEE (WEEs) can be computationally intensive with non-monotone missingness.

Purpose of the Study:

  • To demonstrate the application of multiple imputation with GEE for analyzing partially observed clustered data.
  • To investigate the prevalence of disordered eating symptoms in adolescents using parent and adolescent reports.
  • To identify factors associated with symptom concordance and prevalence in the Avon Longitudinal Study of Parents and their Children (ALSPAC).

Main Methods:

  • Utilized multiple imputation, a method accommodating the less restrictive Missing at Random (MAR) assumption.
  • Employed generalized estimating equations (GEE) in conjunction with multiple imputation for statistical modeling.
  • Applied these methods to analyze data from the ALSPAC cohort study.

Main Results:

  • Multiple imputation with GEE provided estimates similar to standard GEE under MCAR.
  • The MAR model using multiple imputation yielded smaller standard errors compared to standard GEE.
  • The approach requires less stringent assumptions about missing data mechanisms.

Conclusions:

  • Multiple imputation in conjunction with GEE is a practical and statistically sound approach for handling missing data in clustered observational studies.
  • This method offers advantages over traditional GEE by relaxing assumptions about missingness.
  • The findings contribute to a better understanding of disordered eating symptoms in adolescents within the ALSPAC cohort.