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Extended generalized estimating equations for binary familial data with incomplete families.

Patrick E B FitzGerald1

  • 1Department of Public Health, University of Western Australia, Nedlands, Western Australia 6907, Australia. pfitzgerald@m-tag.net

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This study introduces a novel method for analyzing incomplete familial data in generalized estimating equations (GEE2) for binary outcomes. The new approach improves upon naive methods by not requiring the missing data process to be ignorable.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Incomplete familial data presents challenges in statistical analyses.
  • Standard methods for handling missing data in generalized estimating equations (GEE2) may be inadequate.
  • Binary outcomes are common in epidemiological and genetic studies.

Purpose of the Study:

  • To evaluate naive methods for handling incomplete familial data in GEE2 analyses with binary outcomes.
  • To propose and validate a new GEE2 method for discrete explanatory variables and incomplete familial data.
  • To address limitations of naive methods that assume ignorable missing data.

Main Methods:

  • Performance assessment of two standard naive methods for incomplete familial data in GEE2.
  • Development of a novel GEE2 method for discrete explanatory variables and incomplete familial data.
  • Illustration of the proposed method using a case study on familial obesity aggregation.

Main Results:

  • Naive methods may yield biased results when data are incomplete.
  • The proposed method effectively analyzes incomplete familial data without assuming ignorable missingness.
  • The familial aggregation of obesity was examined using the new methodology.

Conclusions:

  • The proposed GEE2 method offers a robust alternative for analyzing incomplete familial data.
  • This approach enhances the analysis of binary outcomes in the presence of missing familial information.
  • The method is particularly useful when the missing data mechanism is not ignorable.