Biostatistics Sample Work

Techniques and applications for regularization methods in clinical biostatistics

Abstract

In the data sciences, a variety of regularization algorithms have been proposed to overcome overfitting, leverage sparsity, or enhance prediction. We discuss a variety of techniques within this framework, including penalization, early halting, ensembling, and model averaging, using a wide definition of regularization, which involves regulating model complexity by adding information in order to solve ill-posed problems or prevent overfitting. Aspects of their actual implementation are explored, as well as accessible R-packages and examples. We surveyed three general medical publications to determine the extent to which these techniques are employed in medicine. With the exception of random effects models, it demonstrated that regularization procedures are rarely used in real clinical applications. As a result, we propose that regularization procedures be used more frequently in medical research. The sole disadvantage of regularization procedures in instances when other approaches work well is increased complexity in the conduct of the biostatistics studies, which might provide obstacles in terms of computer resources and skill on the part of the data analyst. Both can and should, in our opinion, be addressed by investing in proper computing infrastructure and instructional resources.

Keywords

Penalization, Bayesian inference, ensembling, model averaging, early stopping, evidence synthesis

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