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Second-Order Estimating Equations for Clustered Current Status Data from Family Studies Using Response-Dependent

Yujie Zhong1, Richard J Cook2

  • 11MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge, CB2 0SR UK.

Statistics in Biosciences
|August 28, 2018
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Summary
This summary is machine-generated.

This study introduces new statistical methods to analyze genetic influences on diseases within families. The approach accounts for how family members are selected and the type of data collected, improving the understanding of genetic disease patterns.

Keywords:
Current status dataFamily studyGaussian copulaRelative efficiencyResponse-dependent samplingRobustnessSecond-order estimating equations

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Area of Science:

  • Biostatistics
  • Genetic Epidemiology
  • Statistical Genetics

Background:

  • Family studies are crucial for investigating the genetic basis of diseases.
  • Traditional methods often use probands from disease registries, leading to biased sampling.
  • Non-probands typically provide limited disease status data, complicating analysis.

Purpose of the Study:

  • To develop statistical methods for analyzing within-family dependence in disease.
  • To address biased sampling schemes and current status data from non-probands.
  • To accurately assess the genetic contribution to disease based on family data.

Main Methods:

  • Development of conditional second-order estimating equations.
  • Incorporation of biased sampling and current status data into statistical models.
  • Simulation studies to evaluate method performance and relative efficiency.

Main Results:

  • The proposed methods effectively analyze within-family dependence in genetic studies.
  • Simulation results demonstrate the performance of different estimating functions.
  • The study quantifies the empirical relative efficiency of the developed methods.

Conclusions:

  • The novel statistical approach enhances the study of genetic disease under realistic family sampling conditions.
  • The methods provide a robust framework for understanding genetic influences on disease.
  • Application to a psoriatic arthritis family study illustrates the practical utility.