Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures.

ABSTRACT: Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points a

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10718172 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature