Exposure to PFOA and birth outcome in North Rhine-Westphalia, Germany

Kolbe, Arthur; Rathjens, Jonathan; Becker, Eva‐Maria; Bücker-Nott, Hans-Joachim; Olthoff, Katharina; Wilhelm, Michael; Ickstadt, Katja; Hölzer, Jürgen

doi:10.1289/isee.2016.4396

Cited by 2 publications

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“…Individual relevant covariates are pre-selected from the perinatal registry data. This is done in accordance with the literature (e.g., Gardosi et al 1995;Schwartz et al 2010;Frederick et al 2008;Thompson et al 2001), and with previous findings within our larger PerSpat project (Kolbe et al 2016). The specific variables are: child's sex, number of previous pregnancies of the mother, whether the child has been delivered by Cesarean section (sectio), whether the birth has been induced, mother's age, mother's height, mother's body mass index (BMI) at the beginning of pregnancy, gain of weight of the mother during pregnancy, number of cigarettes the mother reports to smoke per day, whether the mother is single and whether the mother is employed.…”

Section: Research Questions and Data Situationsupporting

confidence: 90%

Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas

Rathjens¹,

Kolbe²,

Hölzer³

et al. 2021

Preprint

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In this article, we analyze perinatal data with birth weight (BW) as primarily interesting response variable. Gestational age (GA) is usually an important covariate and included in polynomial form. However, in opposition to this univariate regression, bivariate modeling of BW and GA is recommended to distinguish effects on each, on both, and between them. Rather than a parametric bivariate distribution, we apply conditional copula regression, where marginal distributions of BW and GA (not necessarily of the same form) can be estimated independently, and where the dependence structure is modeled conditional on the covariates separately from these marginals. In the resulting distributional regression models, all parameters of the two marginals and the copula parameter are observation-specific. Besides biometric and obstetric information, data on drinking water contamination and maternal smoking are included as environmental covariates. While the Gaussian distribution is suitable for BW, the skewed GA data are better modeled by the three-parametric 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 nonlinear dependence between BW and GA is surprisingly weak and only influenced by Cesarean section. A non-linear trend of BW on GA is detected by a classical univariate model that is polynomial with respect to the effect of GA. Linear effects on BW mean are similar in both models, while our distributional copula regression also reveals covariates' effects on all other parameters.

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Section: Research Questions and Data Situationsupporting

confidence: 90%

Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas

Rathjens¹,

Kolbe²,

Hölzer³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Bivariate Analysis of Birth Weight and Gestational Age by Bayesian Distributional Regression with Copulas

Rathjens,

Kolbe,

Hölzer

et al. 2023

Stat Biosci

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We 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.

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Exposure to PFOA and birth outcome in North Rhine-Westphalia, Germany

Cited by 2 publications

References 0 publications

Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas

Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas

Bivariate Analysis of Birth Weight and Gestational Age by Bayesian Distributional Regression with Copulas

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