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Mixed binary-continuous copula regression models with application to adverse birth outcomes.

Nadja Klein1, Thomas Kneib2, Giampiero Marra3

  • 1Applied Statistics, Humboldt University of Berlin, Berlin, Germany.

Statistics in Medicine
|October 19, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new copula regression model for mixed binary-continuous outcomes, like gestational age and low birth weight. The findings reveal significant racial and spatial inequalities in infant mortality risk factors.

Keywords:
adverse birth outcomescopulalatent variablemixed discrete-continuous distributionspenalized maximum likelihoodpenalized splines

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Adverse birth outcomes are influenced by various risk factors, many of which are dichotomous.
  • Existing bivariate regression models often struggle with mixed binary-continuous response variables.

Purpose of the Study:

  • To develop and apply a flexible copula regression framework for mixed binary-continuous responses.
  • To model gestational age and low birth weight, considering complex regression effects.

Main Methods:

  • Utilized latent continuous representations of binary regression models.
  • Implemented a penalized likelihood-based approach for copula regression.
  • Applied the model to analyze gestational age and low birth weight data.

Main Results:

  • The proposed model effectively handles mixed binary-continuous outcomes.
  • Demonstrated the advantage of flexible regression specifications, including nonlinear and spatial effects.
  • Identified significant racial and spatial inequalities in infant mortality risk factors.

Conclusions:

  • The novel copula regression approach offers a flexible tool for analyzing mixed-type outcome data.
  • Results highlight the critical impact of race and location on infant mortality risks, underscoring existing disparities.