TY - JOUR
T1 - A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions
AU - Lee, Min Jae
AU - Rahbar, Mohammad H.
AU - Samms-Vaughan, Maureen
AU - Bressler, Jan
AU - Bach, MacKinsey A.
AU - Hessabi, Manouchehr
AU - Grove, Megan L.
AU - Shakespeare-Pellington, Sydonnie
AU - Coore Desai, Charlene
AU - Reece, Jody Ann
AU - Loveland, Katherine A.
AU - Boerwinkle, Eric
N1 - Funding Information:
The National Institutes of Health Fogarty International Center (NIH-FIC), Grant/Award Number: R21HD057808; Translational Science Award (NIH CTSA), Grant/Award Number: UL1 RR024148; National Institute of Environmental Health Sciences (NIEHS), Grant/Award Number: R01ES022165; Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD); the National Center for Advancing Translational Sciences (NCATS), Grant/Award Number: UL1 TR000371
Funding Information:
This research is co-funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the National Institutes of Health Fogarty International Center (NIH-FIC) by a grant (R21HD057808) as well as National Institute of Environmental Health Sciences (NIEHS) by a grant (R01ES022165) awarded to University of Texas Health Science Center at Houston. We also acknowledge the support provided by the Biostatistics/Epidemiology/Research Design (BERD) component of the Center for Clinical and Translational Sciences (CCTS) for this project. CCTS is mainly funded by the NIH Centers for Translational Science Award (NIH CTSA) grant (UL1 RR024148), awarded to University of Texas Health Science Center at Houston in 2006 by the National Center for Research Resources (NCRR) and its renewal (UL1 TR000371) by the National Center for Advancing Translational Sciences (NCATS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NICHD or the NIH-FIC or NIEHS or the NCRR or the NCATS. The authors would like to acknowledge that the public release of real data from ERAJ study that were used to apply the proposed methods was not allowed at the time when the manuscript was accepted, and these ERAJ study data will be publicly available in National Database for Autism Research (NDAR) after May 1, 2020.
PY - 2019/7
Y1 - 2019/7
N2 - A weighted quantile sum (WQS) regression has been used to assess the associations between environmental exposures and health outcomes. However, the currently available WQS approach, which is based on additive effects, does not allow exploring for potential interactions of exposures with other covariates in relation to a health outcome. In addition, the current WQS cannot account for clustering, thus it may not be valid for analysis of clustered data. We propose a generalized WQS approach that can assess interactions by estimating stratum-specific weights of exposures in a mixture, while accounting for potential clustering effect of matched pairs of cases and controls as well as censored exposure data due to being below the limits of detection. The performance of the proposed method in identifying interactions is evaluated through simulations based on various scenarios of correlation structures among the exposures and with an outcome. We also assess how well the proposed method performs in the presence of the varying levels of censoring in exposures. Our findings from the simulation study show that the proposed method outperforms the traditional WQS, as indicated by higher power of detecting interactions. We also find no strong evidence that the proposed method falsely identifies interactions when there are no true interactive effects. We demonstrate application of the proposed method to real data from the Epidemiological Research on Autism Spectrum Disorder (ASD) in Jamaica (ERAJ) by examining interactions between exposure to manganese and glutathione S-transferase family gene, GSTP1 in relation to ASD.
AB - A weighted quantile sum (WQS) regression has been used to assess the associations between environmental exposures and health outcomes. However, the currently available WQS approach, which is based on additive effects, does not allow exploring for potential interactions of exposures with other covariates in relation to a health outcome. In addition, the current WQS cannot account for clustering, thus it may not be valid for analysis of clustered data. We propose a generalized WQS approach that can assess interactions by estimating stratum-specific weights of exposures in a mixture, while accounting for potential clustering effect of matched pairs of cases and controls as well as censored exposure data due to being below the limits of detection. The performance of the proposed method in identifying interactions is evaluated through simulations based on various scenarios of correlation structures among the exposures and with an outcome. We also assess how well the proposed method performs in the presence of the varying levels of censoring in exposures. Our findings from the simulation study show that the proposed method outperforms the traditional WQS, as indicated by higher power of detecting interactions. We also find no strong evidence that the proposed method falsely identifies interactions when there are no true interactive effects. We demonstrate application of the proposed method to real data from the Epidemiological Research on Autism Spectrum Disorder (ASD) in Jamaica (ERAJ) by examining interactions between exposure to manganese and glutathione S-transferase family gene, GSTP1 in relation to ASD.
KW - autism spectrum disorder (ASD)
KW - correlated environmental exposures
KW - interactions
KW - limits of detection
KW - matched-pair data
KW - weighted quantile sum
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U2 - 10.1002/bimj.201800259
DO - 10.1002/bimj.201800259
M3 - Article
C2 - 31058353
AN - SCOPUS:85065408982
VL - 61
SP - 934
EP - 954
JO - Biometrical Journal
JF - Biometrical Journal
SN - 0323-3847
IS - 4
ER -