Adaptive-mixture-categorization (AMC)-based G-computation and Its Application to Trace Element Mixtures and Bladder Cancer Risk

Several new statistical methods have been developed to identify the overall impact of an exposure mixture on health outcomes. Weighted quantile sum (WQS) regression assigns the joint mixture effect weights to indicate the overall association of multiple exposures, and quantile-based g-computation is a generalized version of WQS without the restriction of directional homogeneity. This paper proposes an adaptive-mixture-categorization (AMC)-based g-computation approach that combines g-computation with an optimal exposure categorization search using the F statistic. AMC-based g-computation reduces variance within each category and retains the variance between categories to build more powerful predictors. In a simulation study, the performance of association analysis was improved using categorizing by AMC compared with quantiles. We applied this method to assess the association between a mixture of 12 trace element concentrations measured from toenails and the risk of non-muscle invasive bladder cancer. Our findings suggested that medium-level (116.7-145.5 μg/g) vs. low-level (39.5-116.2 μg/g) of toenail zinc had a statistically significant positive association with bladder cancer risk.