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A Bayesian latent class model for effect of environmental pollutants on female infertility
Journal of the Korean Data & Information Science Society 2018;29:1257-68
Published online September 30, 2018
© 2018 Korean Data and Information Science Society.

Yoon Kyung Choi1 · Beom Seuk Hwang2

12Department of Applied Statistics, Chung-Ang University
Correspondence to: Assistant professor, Department of Applied Statistics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea. E-mail: bshwang@cau.ac.kr
This research was supported by the Chung-Ang University Graduate Research Scholarship in 2016,
and supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B03933334).
Received August 20, 2018; Revised September 6, 2018; Accepted September 10, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
We proposed a latent class model to examine the association between an environmental pollutant (PCB) and female infertility in the LIFE study. We assumed there exist latent risk groups of subjects and linked the PCB exposure and logit model for female infertility through the latent class variable. Also, semicontinuous PCB exposure was analyzed through a mixture of a degenerate distribution at zero and a continuous distribution for nonzero values. We took a Bayesian perspective to inference and used Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. We calculated and compared DICs for all comparable models to find the most appropriate model for LIFE study data. As a result, we found that the risk of infertility was affected by latent risk groups of PCB exposure.
Keywords : Latent class model, LIFE study, Markov chain Monte Carlo, Metropolis algorithm, semicontinuous data.