A Novel Method for Identifying a Parsimonious and Accurate Predictive Model for Multiple Clinical Outcomes.

ABSTRACT:

Background and objective

Most methods for developing clinical prognostic models focus on identifying parsimonious and accurate models to predict a single outcome; however, patients and providers often want to predict multiple outcomes simultaneously. As an example, for older adults one is often interested in predicting nursing home admission as well as mortality. We propose and evaluate a novel predictor-selection computing method for multiple outcomes and provide the code for its implementation.

Methods

Our proposed algorithm selected the best subset of common predictors based on the minimum average normalized Bayesian Information Criterion (BIC) across outcomes: the Best Average BIC (baBIC) method. We compared the predictive accuracy (Harrell's C-statistic) and parsimony (

SUBMITTER: Diaz-Ramirez LG 

PROVIDER: S-EPMC8098121 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature