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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Repeated holdout validation for weighted quantile sum regression.

Eva M Tanner1, Carl-Gustaf Bornehag1,2, Chris Gennings1

  • 1Icahn School of Medicine at Mount Sinai, New York, NY, United States.

Methodsx
|December 25, 2019
PubMed
Summary
This summary is machine-generated.

Repeated holdout validation enhances the stability of Weighted Quantile Sum (WQS) regression estimates for chemical mixtures in environmental epidemiology. This method stabilizes results and clarifies uncertainty in identifying concerning chemicals, especially in studies with limited sample sizes.

Keywords:
BootstrapChemical mixturesChemical of concernCross-validationEnvironmental epidemiologyRepeated holdout validation for weighted quantile sum regressionUncertainty plot

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Area of Science:

  • Environmental Epidemiology
  • Toxicology
  • Biostatistics

Background:

  • Weighted Quantile Sum (WQS) regression is a standard method for assessing chemical mixture impacts on health outcomes.
  • Traditional WQS implementation with single data partitioning can yield unstable chemical weights and index estimates, particularly in smaller epidemiological studies.
  • Researchers sometimes forgo data partitioning, potentially compromising the validity of WQS results.

Purpose of the Study:

  • To introduce and evaluate repeated holdout validation as a method to improve the stability and reliability of WQS regression estimates.
  • To address the challenge of unstable chemical weights and index estimates in WQS regression when applied to datasets with limited sample sizes.
  • To enhance the characterization of chemical weight variability for more accurate identification of chemicals of concern.

Main Methods:

  • Proposed repeated holdout validation, involving 100 random data partitions to generate a distribution of WQS results.
  • Calculated the mean of validated results as the final WQS estimate and derived confidence estimates for inference.
  • Applied the method to assess prenatal exposure to 26 endocrine-disrupting chemicals and child Intelligence Quotient (IQ) in the SELMA study (718 mother-child pairs).

Main Results:

  • Single data partition results for WQS regression were unstable and sensitive to the random seed.
  • WQS index estimates showed significant association when no data partitioning was used, but attenuated and became nonsignificant with repeated holdout validation.
  • Repeated holdout validation demonstrated improved stability and provided a more reliable estimate (β = -0.82, CI = -2.11, 0.45) compared to single partition or no partition methods.

Conclusions:

  • Repeated holdout validation offers a viable and robust alternative to single or no data partitioning in WQS regression for epidemiological studies with finite sample sizes.
  • This approach stabilizes WQS estimates and effectively characterizes uncertainty in identifying chemicals of concern, aiding targeted future research.
  • The SELMA study data illustrated that repeated holdout validation provides more reliable insights into chemical mixture effects on child IQ than traditional methods.