A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes.

ABSTRACT:

Background

Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility.

Methods

We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI ge

SUBMITTER: Wang Y 

PROVIDER: S-EPMC7488560 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature