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Identifying influential families using regression diagnostics for generalized estimating equations

A Ziegler1, M Blettner, C Kastner

  • 1Medical Center for Methodology and Health Research, Institute of Medical Biometry and Epidemiology, Philipps-University of Marburg, Germany. ziegler@mailer.uni-marburg.de

Genetic Epidemiology
|July 22, 1998
PubMed
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Generalized Estimating Equations (GEE) analysis revealed familial aggregation in oesophageal cancer clusters. Specific family characteristics, like early disease onset, influenced the cancer risk model. Regression diagnostics identified these influential clusters.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Familial aggregation of oesophageal cancer is a concern in high-incidence areas.
  • Understanding familial risk factors is crucial for targeted prevention strategies.

Purpose of the Study:

  • To investigate familial aggregation of oesophageal cancer using Generalized Estimating Equations (GEE).
  • To identify specific families and their characteristics that influence epidemiological models of oesophageal cancer.

Main Methods:

  • Application of Generalized Estimating Equations (GEE) to analyse correlated data from an epidemiological study.
  • Utilisation of regression diagnostics to assess the influence of families on mean and association structures.
  • Analysis of oesophageal cancer data from a high-incidence region in China.

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Main Results:

  • Regression diagnostics identified influential families for both mean and association structures.
  • Families with a common early age of disease onset significantly influenced the mean structure.
  • Identified families primarily impacted the parent correlation within the association structure.

Conclusions:

  • Generalized Estimating Equations (GEE) combined with regression diagnostics effectively identify familial clusters influencing epidemiological models.
  • Early age of onset is a key familial characteristic associated with increased oesophageal cancer risk.
  • Regression diagnostics are valuable tools for refining models of familial disease aggregation.