miWQS: Multiple Imputation Using Weighted Quantile Sum Regression

Authors

Paul M. Hargarten, Virginia Commonwealth University
David C. Wheeler, Virginia Commonwealth University

Date of this Version

12-2020

Document Type

Article

Citation

The R Journal (December 2020) 12(2); Editor: Michael J. Kane

Comments

Copyright 2020, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

The miWQS package in the Comprehensive R Archive Network (CRAN) utilizes weighted quantile sum regression (WQS) in the multiple imputation (MI) framework. The data analyzed is a set/mixture of continuous and correlated components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, which may differ among the components. This type of data is found in areas such as chemical epidemiological studies, sociology, and genomics. The miWQS package can be run using complete or incomplete data, which may be placed in the first quantile, or imputed using bootstrap or Bayesian approach. This article provides a stepwise and hands-on approach to handle uncertainty due to values below the detection limit in correlated component mixture problems.