Publications by authors named "Stella Bollmann"

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.

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Diagnosis-related group (DRG) hospital reimbursement systems differentiate cases into cost-homogenous groups based on patient characteristics. However, exogenous organizational and regional factors can influence hospital costs beyond case-mix differences. Therefore, most countries using DRG systems incorporate adjustments for such factors into their reimbursement structure.

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Structural factors can influence hospital costs beyond case-mix differences. However, accepted measures on how to distinguish hospitals with regard to cost-related organizational and regional differences are lacking in Switzerland. Therefore, the objective of this study was to identify and assess a comprehensive set of hospital attributes in relation to average case-mix adjusted costs of hospitals.

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Questionable research practices (QRPs) can occur whenever one result is favored over another, and tests of mediation are no exception. Given mediation's ubiquity and importance to both theory and practice, QRPs in tests of mediation pose a serious threat to the advancement of psychology. We investigate this issue through the introduction of a straightforward means of detecting the presence and magnitude of QRPs in tests of mediation and validate this methodology with a series of sensitivity tests and simulations.

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Various methods to detect differential item functioning (DIF) in item response models are available. However, most of these methods assume that the responses are binary, and so for ordered response categories available methods are scarce. In the present article, DIF in the widely used partial credit model is investigated.

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