Adaptive-mixture-categorization (AMC)-based g-computation and its application to trace element mixtures and bladder cancer risk

ABSTRACT: 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 categor

SUBMITTER: Li S 

PROVIDER: S-EPMC9596719 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature