TY - JOUR
T1 - A random subset implementation of weighted quantile sum (WQSRS) regression for analysis of high-dimensional mixtures
AU - Curtin, Paul
AU - Kellogg, Joshua
AU - Cech, Nadja
AU - Gennings, Chris
N1 - Funding Information:
This study was supported by funding from NIEHS (U2C ES026555-01) for the CHEAR Data Center.
Publisher Copyright:
© 2019 Taylor & Francis Group, LLC.
PY - 2021
Y1 - 2021
N2 - Here we introduce a novel implementation of weighted quantile sum (WQS) regression, a modeling strategy for mixtures analyses, which integrates a random subset algorithm in the estimation of mixture effects. We demonstrate the application of this method (WQSRS) in three case examples, with mixtures varying in size from 34 to 472 variables. In evaluating each case, we provide detailed simulation studies to characterize the sensitivity and specificity of WQSRS in varying contexts. Our results emphasize that WQSRS is robustly effective in evaluating mixture effects in diverse high-dimensional contexts, yielding sensitivity and specificity in empirical contexts of approximately 73–75% and 73–89%, respectively.
AB - Here we introduce a novel implementation of weighted quantile sum (WQS) regression, a modeling strategy for mixtures analyses, which integrates a random subset algorithm in the estimation of mixture effects. We demonstrate the application of this method (WQSRS) in three case examples, with mixtures varying in size from 34 to 472 variables. In evaluating each case, we provide detailed simulation studies to characterize the sensitivity and specificity of WQSRS in varying contexts. Our results emphasize that WQSRS is robustly effective in evaluating mixture effects in diverse high-dimensional contexts, yielding sensitivity and specificity in empirical contexts of approximately 73–75% and 73–89%, respectively.
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U2 - 10.1080/03610918.2019.1577971
DO - 10.1080/03610918.2019.1577971
M3 - Article
AN - SCOPUS:85071303733
SN - 0361-0918
VL - 50
SP - 1099
EP - 1114
JO - Communications in Statistics: Simulation and Computation
JF - Communications in Statistics: Simulation and Computation
IS - 4
ER -