Bivariate analysis of birth weight and gestational age by Bayesian distributional regression with copulas

dc.contributor.authorRathjens, Jonathan
dc.contributor.authorKolbe, Arthur
dc.contributor.authorHölzer, Jürgen
dc.contributor.authorIckstadt, Katja
dc.contributor.authorKlein, Nadja
dc.date.accessioned2025-03-14T10:12:39Z
dc.date.available2025-03-14T10:12:39Z
dc.date.issued2023-10-27
dc.description.abstractWe analyze perinatal data including biometric and obstetric information as well as data on maternal smoking, among others. Birth weight is the primarily interesting response variable. Gestational age is usually an important covariate and included in polynomial form. However, in opposition to this univariate regression, bivariate modeling of birth weight and gestational age is recommended to distinguish effects on each, on both, and between them. Rather than a parametric bivariate distribution, we apply conditional copula regression, where the marginal distributions of birth weight and gestational age (not necessarily of the same form) and the dependence structure are modeled conditionally on covariates. In the resulting distributional regression model, all parameters of the two marginals and the copula parameter are observation specific. While the Gaussian distribution is suitable for birth weight, the skewed gestational age data are better modeled by the three-parameter Dagum distribution. The Clayton copula performs better than the Gumbel and the symmetric Gaussian copula, indicating lower tail dependence (stronger dependence when both variables are low), although this non-linear dependence between birth weight and gestational age is surprisingly weak and only influenced by Cesarean section. A non-linear trend of birth weight on gestational age is detected by a univariate model that is polynomial with respect to the effect of gestational age. Covariate effects on the expected birth weight are similar in our copula regression model and a univariate regression model, while distributional copula regression reveals further insights, such as effects of covariates on the association between birth weight and gestational age.en
dc.identifier.urihttp://hdl.handle.net/2003/43546
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25379
dc.language.isoen
dc.relation.ispartofseriesStatistics in biosciences; 16(1)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectConditional copula regressionen
dc.subjectDagum distributionen
dc.subjectPerinatal dataen
dc.subjectSecondary dataen
dc.subjectTail dependenceen
dc.subject.ddc310
dc.titleBivariate analysis of birth weight and gestational age by Bayesian distributional regression with copulasen
dc.typeText
dc.type.publicationtypeArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationRathjens, J. et al. (2024) ‘Bivariate analysis of birth weight and gestational age by Bayesian distributional regression with copulas’, Statistics in biosciences, 16(1), pp. 290–317. Available at: https://doi.org/10.1007/s12561-023-09396-4
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1007/s12561-023-09396-4

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