Evaluating variable selection methods for multivariable regression models: A simulation study protocol.

ABSTRACT: Researchers often perform data-driven variable selection when modeling the associations between an outcome and multiple independent variables in regression analysis. Variable selection may improve the interpretability, parsimony and/or predictive accuracy of a model. Yet variable selection can also have negative consequences, such as false exclusion of important variables or inclusion of noise variables, biased estimation of regression coefficients, underestimated standard errors and invalid confidence intervals, as well as model instability. While the potential advantages and disadvantages of variable selection have been discussed in the literature for decades, few large-scale simulation studies have neutrally compared data-driven variable selection methods with respect to their consequen

SUBMITTER: Ullmann T 

PROVIDER: S-EPMC11315300 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature