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Weighted Quantile Sum (WQS) regression is a method commonly used in environmental epidemiology to assess the impact of chemical mixtures in relation to a health outcome of interest. Data are partitioned into a single training and test set to reduce sample-specific chemical weights.

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We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation.

The gWQS package extends WQS regression to applications with continuous and categorical outcomes and implements the random subset WQS and the repeated holdout WQS.

Nov 1, 2019 · Weighted Quantile Sum (WQS) regression is a method commonly used in environmental epidemiology to assess the impact of chemical mixtures in relation to a health outcome of interest. Data...

We propose a revised algorithm that may be useful for larger mixtures by combining time-specific WQS (t) indices in a reverse DLM.

In this section, we’ll delve into the fundamental aspects and key features of the package. The gWQS package provides functions for generalized weighted quantile sum regression, a method for assessing the combined effects of multiple exposures.