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

Nadja Klein, Thomas Kneib, Giampiero Marra, Rosalba Radice, Slawa Rokicki, Mark E. McGovern

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
139 Downloads (Pure)


Bivariate copula regression allows for the flexible combination of two arbitrary, continuous marginal distributions with regression effects being placed on potentially all parameters of the resulting bivariate joint response distribution. Motivated by the risk factors for adverse birth outcomes, many of which are dichotomous, we consider mixed binary-continuous responses that extend the bivariate continuous framework to the situation where one response variable is discrete (more precisely binary) while the other response remains continuous. Utilizing the latent continuous representation of binary regression models, we implement a penalized likelihood based approach for the resulting class of copula regression models and employ it in the context of modelling gestational age and the presence/absence of low birth weight. The analysis demonstrates the advantage of the flexible specification of regression impacts including nonlinear effects of continuous covariates and spatial effects. Our results imply that racial and spatial inequalities in the risk factors for infant mortality are even greater than previously suggested.
Original languageEnglish
Pages (from-to)413-436
Number of pages24
JournalStatistics in Medicine
Issue number3
Early online date17 Oct 2018
Publication statusPublished - 10 Feb 2019


  • Adverse birth outcomes
  • Copula
  • Latent variable
  • Mixed discrete- continuous distributions
  • Penalised maximum likelihood
  • Penalised splines

ASJC Scopus subject areas

  • Economics and Econometrics
  • Health(social science)

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