A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

Min Jae Lee, Mohammad H. Rahbar, Maureen Samms-Vaughan, Jan Bressler, MacKinsey A. Bach, Manouchehr Hessabi, Megan L. Grove, Sydonnie Shakespeare-Pellington, Charlene Coore Desai, Jody Ann Reece, Katherine A. Loveland, Eric Boerwinkle

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)934-954
Number of pages21
JournalBiometrical Journal
Volume61
Issue number4
DOIs
StatePublished - Jul 2019
Externally publishedYes

Keywords

  • autism spectrum disorder (ASD)
  • correlated environmental exposures
  • interactions
  • limits of detection
  • matched-pair data
  • weighted quantile sum

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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