A Model-Based Approach to Detection Limits in Studying Environmental Exposures and Human Fecundity

Sungduk Kim, Zhen Chen (), Neil J. Perkins, Enrique F. Schisterman and Germaine M. Buck Louis
Additional contact information
Sungduk Kim: National Cancer Institute
Zhen Chen: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Neil J. Perkins: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Enrique F. Schisterman: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Germaine M. Buck Louis: George Mason University

Statistics in Biosciences, 2019, vol. 11, issue 3, No 3, 524-547

Abstract: Abstract Human exposure to persistent environmental pollutants often results in concentrations with a range of values below the laboratory detection limits. Growing evidence suggests that inadequate handling of concentrations below the limit of detection (LOD) may bias assessment of health effects in relation to environmental exposures. We seek to quantify such bias in models focusing on the day-specific probability of pregnancy during the fertile window and propose a model-based approach to reduce such bias. A multivariate skewed generalized t-distribution constrained by the LOD is assumed for the chemical concentrations, which realistically represents the underlying distribution. A latent variable-based framework is used to model fecundibility, which nonlinearly relates conception probability to chemical concentrations, daily intercourses, and other important covariates. The advantages of the proposed approach include the use of multiple chemical concentrations to aid the estimation of left censored chemical exposures, as well as the model-based feedback mechanism for fecundibility outcome to inform the estimations, and an adequate handling of model uncertainty through a joint modeling framework. A Markov chain Monte Carlo sampling algorithm is developed for implementing the Bayesian computations and the logarithm of pseudo-marginal likelihood measure is used for model choices. We conduct simulation studies to demonstrate the performance of the proposed approach and apply the framework to the Longitudinal Investigation of Fertility and the Environment study which evaluates the effects of exposures to environmental pollutants on the probability of pregnancy. We found that $$p,p^{\prime }$$ p , p ′ -DDT is negatively associated with the day-specific probability of pregnancy.

Keywords: Fecundity; Generalized t-distribution; Markov chain Monte Carlo; Menstrual cycle; Pregnancy; Posterior distribution (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-019-09243-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:11:y:2019:i:3:d:10.1007_s12561-019-09243-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-019-09243-5

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().