Re-evaluation of the comparative effectiveness of bootstrap-based optimism correction methods in the development of multivariable clinical prediction models.

ABSTRACT:

Background

Multivariable prediction models are important statistical tools for providing synthetic diagnosis and prognostic algorithms based on patients' multiple characteristics. Their apparent measures for predictive accuracy usually have overestimation biases (known as 'optimism') relative to the actual performances for external populations. Existing statistical evidence and guidelines suggest that three bootstrap-based bias correction methods are preferable in practice, namely Harrell's bias correction and the .632 and .632+ estimators. Although Harrell's method has been widely adopted in clinical studies, simulation-based evidence indicates that the .632+ estimator may perform better than the other two methods. However, these methods' actual comparative effectiveness is still

SUBMITTER: Iba K 

PROVIDER: S-EPMC7789544 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature