Leave-one-out cross-validation, penalization, and differential bias of some prediction model performance measures-a simulation study.

ABSTRACT:

Background

The performance of models for binary outcomes can be described by measures such as the concordance statistic (c-statistic, area under the curve), the discrimination slope, or the Brier score. At internal validation, data resampling techniques, e.g., cross-validation, are frequently employed to correct for optimism in these model performance criteria. Especially with small samples or rare events, leave-one-out cross-validation is a popular choice.

Methods

Using simulations and a real data example, we compared the effect of different resampling techniques on the estimation of c-statistics, discrimination slopes, and Brier scores for three estimators of logistic regression models, including the maximum likelihood and two maximum penalized likelihood estimators.

Re

SUBMITTER: Geroldinger A 

PROVIDER: S-EPMC10152625 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature