A Bayesian multi-dimensional couple-based latent risk model with an application to infertility.

Area of Science:

• Environmental epidemiology
• Reproductive health
• Statistical modeling

Background:

• The Longitudinal Investigation of Fertility and the Environment (LIFE) Study examined environmental pollutant exposure and human reproductive outcomes.
• Understanding the joint impact of male and female exposures on infertility is crucial for reproductive health research.

Purpose of the Study:

• To propose a joint latent risk class modeling framework to analyze the interplay of chemical exposures between partners and infertility risk.
• To investigate the dependence structure between couples' chemical patterns and their infertility risk.

Main Methods:

• Developed a joint latent risk class model incorporating an interaction term for female and male chemical patterns.
• Employed a Bayesian inference approach using Markov chain Monte Carlo (MCMC) algorithms.
• Validated the model through simulations and applied it to the LIFE Study dataset.

Main Results:

• Confirmed the role of female polychlorinated biphenyl (PCB) exposures in infertility risk.
• Demonstrated that male partners' PCB exposures significantly influence infertility risk.
• Identified a subadditive risk relationship, suggesting a ceiling effect when both partners have high exposure levels.

Conclusions:

• The joint modeling framework effectively captures the complex interactions between couple-specific chemical exposures and infertility.
• Both male and female PCB exposures are critical factors in determining infertility risk.
• The findings highlight the importance of considering joint partner exposures and suggest potential thresholds for infertility risk.