Defense Date


Document Type


Degree Name

Doctor of Philosophy



First Advisor

Chris Gennings


In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) are unsuitable. We extend and characterize a weighted quantile score (WQS) approach to estimating an index for a set of highly correlated components. In the case with environmental chemicals, we use the WQS to identify “bad actors” and estimate body burden. The accuracy of the WQS was evaluated through extensive simulation studies in terms of validity (ability of the WQS to select the correct components) and reliability (the variability of the estimated weights across bootstrap samples). The WQS demonstrated high validity and reliability in scenarios with relatively high correlations with an outcome and moderate breakdown in cases where the correlation with the outcome was relatively small compared to the pairwise correlations. In cases where components are independent, weights can be interpreted as association with the outcome relative to the other components. In cases with complex correlation patterns, weights are influenced by both importance with the outcome and the correlation structure. The WQS also showed improvements over ordinary regression and LASSO in the simulations performed. To conclude, an application of this method on the association between environmental chemicals, nutrition and liver toxicity, as measured by ALT (alanine amino-transferase) is presented. The application identifies environmental chemicals (PCBs, dioxins, furans and heavy metals) that are associated with an increase in ALT and a set of nutrients that are identified as non-chemical stressors due to an association with an increase in ALT.


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Is Part Of

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Date of Submission

May 2013

Included in

Biostatistics Commons