Bayesian regularization to predict neuropsychiatric adverse events in smoking cessation with pharmacotherapy.

ABSTRACT:

Background

Research on risk factors for neuropsychiatric adverse events (NAEs) in smoking cessation with pharmacotherapy is scarce. We aimed to identify predictors and develop a prediction model for risk of NAEs in smoking cessation with medications using Bayesian regularization.

Methods

Bayesian regularization was implemented by applying two shrinkage priors, Horseshoe and Laplace, to generalized linear mixed models on data from 1203 patients treated with nicotine patch, varenicline or placebo. Two predictor models were considered to separate summary scores and item scores in the psychosocial instruments. The summary score model had 19 predictors or 26 dummy variables and the item score model 51 predictors or 58 dummy variables. A total of 18 models were investigated.

Re

SUBMITTER: Truong VTT 

PROVIDER: S-EPMC10148544 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature