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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

A generalized linear mixed model for longitudinal binary data with a marginal logit link function.

Michael Parzen1, Souparno Ghosh, Stuart Lipsitz

  • 1Goizueta Business School, Emory University.

The Annals of Applied Statistics
|May 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for longitudinal binary data, improving upon existing methods by allowing correlated random intercepts. This enhanced model better captures complex correlations in health and behavioral research.

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies with binary outcomes are prevalent across health, social, and behavioral sciences.
  • Standard random effects logistic regression models for longitudinal binary data often lose the logistic form in their marginal models.
  • Previous models, like Wang and Louis (2003), addressed this but were limited to a single random effect per cluster.

Purpose of the Study:

  • To propose a modified random effects logistic regression model for longitudinal binary data.
  • To allow for separate, correlated random intercepts at each measurement occasion, overcoming limitations of prior models.
  • To enable flexible correlation structures among random intercepts, interpretable via Kendall's τ.

Main Methods:

  • Developed a modified random effects logistic regression model.
  • Incorporated separate, correlated random intercepts for each measurement occasion.
  • Utilized Kendall's τ to characterize the correlation structure among random intercepts.

Main Results:

  • The proposed model maintains matching conditional and marginal logit link functions.
  • It allows marginal correlations among repeated binary outcomes to decline with increasing time separation.
  • The model was successfully applied to analyze longitudinal data on cardiac abnormalities in children of HIV-infected women.

Conclusions:

  • The proposed model offers a flexible and statistically sound approach for analyzing longitudinal binary data.
  • It effectively models complex correlation structures, providing a more nuanced understanding of repeated binary outcomes.
  • This method has practical applications in epidemiological and clinical research, as demonstrated by the HIV-exposed children cohort study.