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

Qing Yin, Jong-Hyeon Jeong, Xu Qin, Shyamal D Peddada () and Jennifer J Adibi
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Qing Yin: University of Pittsburgh
Jong-Hyeon Jeong: University of Pittsburgh
Xu Qin: University of Pittsburgh
Shyamal D Peddada: National Institute of Environmental Health Sciences (NIEHS), NIH
Jennifer J Adibi: University of Pittsburgh

Sankhya B: The Indian Journal of Statistics, 2024, vol. 86, issue 2, No 11, 669-689

Abstract: Abstract Often linear regression is used to estimate mediation effects. In many instances the underlying relationships may not be linear. Although, the exact functional form of the relationship may be unknown, based on the underlying science, one may hypothesize the 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. 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; Placental-fetal hormones; Pesticides exposure; Regression spline; Shape-restricted inference; Primary: 62D20; Secondary: 62F30 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13571-024-00336-w

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