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A semivarying joint model for longitudinal binary and continuous outcomes.

Esra Kürüm1, John Hughes2, Runze Li3

  • 1Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06520, USA.

The Canadian Journal of Statistics = Revue Canadienne De Statistique
|September 27, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a semivarying joint modeling framework to analyze time-varying associations between continuous and binary longitudinal data. The novel approach uses a Gaussian latent variable and a two-stage estimation for robust analysis of complex health data.

Keywords:
Generalized varying coefficient modelHIVlocal linear regressionprofile least squares

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Jointly modeling continuous and binary longitudinal data presents challenges due to the lack of natural multivariate distributions.
  • Semivarying models offer a flexible extension to varying coefficient models by incorporating both constant and covariate-dependent coefficients.

Purpose of the Study:

  • To develop a semivarying joint modeling framework for estimating time-varying associations between continuous and binary longitudinal responses.
  • To address the challenge of jointly modeling these diverse data types by introducing a latent variable approach.

Main Methods:

  • A Gaussian latent variable is introduced to bridge the continuous and binary responses.
  • The model is decomposed into a marginal model for the continuous response and a conditional model for the binary response.
  • A two-stage estimation procedure is developed, and its asymptotic normality is analyzed.

Main Results:

  • The proposed semivarying joint modeling framework effectively estimates time-varying associations.
  • Simulation studies confirm the finite-sample performance of the developed estimation procedure.
  • The method is successfully illustrated using real-world data from the Women's Interagency HIV Study.

Conclusions:

  • The semivarying joint modeling framework provides a robust method for analyzing complex longitudinal data with both continuous and binary outcomes.
  • This approach offers a valuable tool for researchers investigating time-dependent relationships in health studies.
  • The latent variable technique effectively handles the distributional challenges in joint modeling.