Comparative efficacy of three Bayesian variable selection methods in the context of weight loss in obese women.

ABSTRACT: The use of high-dimensional data has expanded in many fields, including in clinical research, thus making variable selection methods increasingly important compared to traditional statistical approaches. The work aims to compare the performance of three supervised Bayesian variable selection methods to detect the most important predictors among a high-dimensional set of variables and to provide useful and practical guidelines of their use. We assessed the variable selection ability of: (1) Bayesian Kernel Machine Regression (BKMR), (2) Bayesian Semiparametric Regression (BSR), and (3) Bayesian Least Absolute Shrinkage and Selection Operator (BLASSO) regression on simulated data of different dimensions and under three scenarios with disparate predictor-response relationships and correlation

SUBMITTER: Pesenti N 

PROVIDER: S-EPMC10390836 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature