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Penalized joint generalized estimating equations for longitudinal binary data.

Youjun Huang1, Jianxin Pan2

  • 1Mathematical College, Sichuan University, Chengdu, P. R. China.

Biometrical Journal. Biometrische Zeitschrift
|September 29, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces penalized joint generalized estimating equation (PJGEE) methods for longitudinal binary data, improving variable selection and parameter accuracy by simultaneously modeling mean and correlations.

Keywords:
correlation matrixjoint mean and correlation modelslongitudinal binary datapenalized generalized estimating equationsvariable selection

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Variable selection is crucial in statistical research but challenging for longitudinal data due to within-subject correlations.
  • Standard methods fail with discrete longitudinal responses as likelihood functions lack closed forms.
  • Penalized generalized estimating equations (PGEE) can err if the correlation structure is misspecified.

Purpose of the Study:

  • To develop novel methods for simultaneous modeling of mean and correlations in longitudinal binary data.
  • To perform variable selection in the mean model while respecting correlation structure constraints.
  • To address the challenges posed by the Fréchet-Hoeffding upper bound for correlation coefficients.

Main Methods:

  • Proposed smoothly clipped absolute deviation (SCAD)-based and least absolute shrinkage and selection operator (LASSO)-based penalized joint generalized estimating equation (PJGEE) methods.
  • Simultaneously modeled the mean and correlations for longitudinal binary data.
  • Ensured estimated correlation coefficients adhere to upper bound constraints.

Main Results:

  • The proposed PJGEE methods demonstrated superior performance in simulation studies.
  • Achieved better variable selection consistency compared to existing PGEE methods.
  • Showed improved parameter estimation accuracy.

Conclusions:

  • The PJGEE approach effectively models mean and correlations for longitudinal binary data.
  • It offers a robust solution for variable selection in complex longitudinal settings.
  • The method was successfully illustrated using a real-world clinical dataset.