Interaction Analysis Based on Shapley Values and Extreme Gradient Boosting: A Realistic Simulation and Application to a Large Epidemiological Prospective Study

ABSTRACT:

Background

SHapley Additive exPlanations (SHAP) based on tree-based machine learning methods have been proposed to interpret interactions between exposures in observational studies, but their performance in realistic simulations is seldom evaluated.

Methods

Data from population-based cohorts in Sweden of 47,770 men and women with complete baseline information on diet and lifestyles were used to inform a realistic simulation in 3 scenarios of small (ORM = 0.75 vs. ORW = 0.70), moderate (ORM = 0.75 vs. ORW = 0.65), and large (ORM = 0.75 vs. ORW = 0.60) discrepancies in the adjusted mortality odds ratios conferred by a healthy diet among men and among women. Estimates were obtained with logistic regression (L-ORM; L-ORW) and derived from SHAP values (S-ORM; S-ORW).

Resul

SUBMITTER: Orsini N 

PROVIDER: S-EPMC9340268 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature