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

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  • Beom Seuk Hwang
  • Zhen Chen
  • Germaine M. Buck Louis
  • Paul S. Albert

Abstract

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.

Suggested Citation

  • Beom Seuk Hwang & Zhen Chen & Germaine M. Buck Louis & Paul S. Albert, 2019. "A Bayesian multi‐dimensional couple‐based latent risk model with an application to infertility," Biometrics, The International Biometric Society, vol. 75(1), pages 315-325, March.
    • Handle: RePEc:bla:biomet:v:75:y:2019:i:1:p:315-325
    DOI: 10.1111/biom.12972
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    References listed on IDEAS

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    4. Liu, Yue & Liu, Lei & Zhou, Jianhui, 2015. "Joint latent class model of survival and longitudinal data: An application to CPCRA study," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 40-50.
    5. Peter Lenk & Wayne DeSarbo, 2000. "Bayesian inference for finite mixtures of generalized linear models with random effects," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 93-119, March.
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    Cited by:

    1. Debamita Kundu & Sungduk Kim & Mary H. Ward & Paul S. Albert, 2024. "A Comparison of Statistical Methods for Studying Interactions of Chemical Mixtures," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 503-519, July.
    2. Shelley H. Liu & Yitong Chen & Jordan R. Kuiper & Emily Ho & Jessie P. Buckley & Leah Feuerstahler, 2024. "Applying Latent Variable Models to Estimate Cumulative Exposure Burden to Chemical Mixtures and Identify Latent Exposure Subgroups: A Critical Review and Future Directions," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 482-502, July.

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