Regularization approaches in clinical biostatistics: A review of methods and their applications.

ABSTRACT: A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of

SUBMITTER: Friedrich S 

PROVIDER: S-EPMC9896544 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature