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[Preprint]. 2023 Oct 13:arXiv:2310.09185v1.

Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications

Qing Yin  1 , Jong-Hyeon Jeong  1 , Xu Qin  2 , Shyamal D Peddada  3 , Jennifer J Adibi  4
Affiliations

Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications

Qing Yin et al. ArXiv. .

Update in

Abstract

Often linear regression is used to perform mediation analysis. However, in many instances, the underlying relationships may not be linear, as in the case of placentalfetal hormones and fetal development. Although, the exact functional form of the relationship may be unknown, one may hypothesize the general shape of the relationship. For these reasons, we develop a novel shape-restricted inference-based methodology for conducting mediation analysis. This work is motivated by an application in fetal endocrinology where researchers are interested in understanding the effects of pesticide application on birth weight, with human chorionic gonadotropin (hCG) as the mediator. We assume a practically plausible set of nonlinear effects of hCG on the birth weight and a linear relationship between pesticide exposure and hCG, with both exposure-outcome and exposure-mediator models being linear in the confounding factors. Using the proposed methodology on a population-level prenatal screening program data, with hCG as the mediator, we discovered that, while the natural direct effects suggest a positive association between pesticide application and birth weight, the natural indirect effects were negative.

Keywords: Birth-weight; Constrained inference; Human chorionic gonadotropin (hCG); Mediation analysis; Pesticides exposure; Placental-fetal hormones; Regression spline; Shape-restricted inference.

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Figures

Figure 1:
Figure 1:
The relationship between a placental hormone (expressed as a gestational age multiple of median) and infant birth weight.
Figure 2:
Figure 2:
Plots of hormone vs. birth weight under each pattern, and simulation results of coverage probability for each pattern
Figure 3:
Figure 3:
The directed acyclic graph (DAG) for the analysis
See this image and copyright information in PMC

References

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