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Biometrics. 2019 Mar;75(1):315-325. doi: 10.1111/biom.12972. Epub 2019 Mar 8.

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

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Department of Applied Statistics, Chung-Ang University, Seoul, Korea.
Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Bethesda, Maryland.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.


Motivated by the Longitudinal Investigation of Fertility and the Environment (LIFE) Study that investigated the association between exposure to a large number of environmental pollutants and human reproductive outcomes, we propose a joint latent risk class modeling framework with an interaction between female and male partners of a couple. This formulation introduces a dependence structure between the chemical patterns within a couple and between the chemical patterns and the risk of infertility. The specification of an interaction enables the interplay between the female and male's chemical patterns on the risk of infertility in a parsimonious way. We took a Bayesian perspective to inference and used Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. We conducted simulations to examine the performance of the estimation approach. Using the LIFE Study dataset, we found that in addition to the effect of PCB exposures on females, the male partners' PCB exposures play an important role in determining risk of infertility. Further, this risk is subadditive in the sense that there is likely a ceiling effect which limits the probability of infertility when both partners of the couple are at high risk.


Couple-based design; chemical mixture models; latent class model; low dose additivity; subadditivity effect


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