Accounting for clustering in automated variable selection using hospital data: a comparison of different LASSO approaches.

ABSTRACT:

Background

Automated feature selection methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) have recently gained importance in the prediction of quality-related outcomes as well as the risk-adjustment of quality indicators in healthcare. The methods that have been used so far, however, do not account for the fact that patient data are typically nested within hospitals.

Methods

Therefore, we aimed to demonstrate how to account for the multilevel structure of hospital data with LASSO and compare the results of this procedure with a LASSO variant that ignores the multilevel structure of the data. We used three different data sets (from acute myocardial infarcation, COPD, and stroke patients) with two dependent variables (one numeric and one binary), on wh

SUBMITTER: Bollmann S 

PROVIDER: S-EPMC10675967 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature