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Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes

Author

Listed:
  • Nadja Klein
  • Thomas Kneib
  • Giampiero Marra
  • Rosalba Radice
  • Slawa Rokicki
  • Mark E. McGovern

Abstract

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.

Suggested Citation

  • Nadja Klein & Thomas Kneib & Giampiero Marra & Rosalba Radice & Slawa Rokicki & Mark E. McGovern, 2018. "Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes," CHaRMS Working Papers 18-06, Centre for HeAlth Research at the Management School (CHaRMS).
  • Handle: RePEc:qub:charms:1806
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    References listed on IDEAS

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    Cited by:

    1. Dorn, Franziska & Maxand, Simone & Kneib, Thomas, 2021. "The dependence between income inequality and carbon emissions: A distributional copula analysis," University of Göttingen Working Papers in Economics 413, University of Goettingen, Department of Economics.
    2. Elizabeth D. Schifano & Himchan Jeong & Ved Deshpande & Dipak K. Dey, 2021. "Fully and empirical Bayes approaches to estimating copula-based models for bivariate mixed outcomes using Hamiltonian Monte Carlo," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 133-152, March.
    3. Giampiero Marra & Rosalba Radice & David Zimmer, 2021. "Did the ACA's “guaranteed issue” provision cause adverse selection into nongroup insurance? Analysis using a copula‐based hurdle model," Health Economics, John Wiley & Sons, Ltd., vol. 30(9), pages 2246-2263, September.

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    More about this item

    Keywords

    Adverse Birth Outcomes; Copula; Latent Variable; Mixed Discrete-continuous Distributions; Penalised Maximum Likelihood; Penalised Splines;
    All these keywords.

    JEL classification:

    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • I10 - Health, Education, and Welfare - - Health - - - General

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