Dataset Information


Combining multiple biomarker models in logistic regression.

ABSTRACT: In medical research, there is great interest in developing methods for combining biomarkers. We argue that selection of markers should also be considered in the process. Traditional model/variable selection procedures ignore the underlying uncertainty after model selection. In this work, we propose a novel model-combining algorithm for classification in biomarker studies. It works by considering weighted combinations of various logistic regression models; five different weighting schemes are considered in the article. The weights and algorithm are justified using decision theory and risk-bound results. Simulation studies are performed to assess the finite-sample properties of the proposed model-combining method. It is illustrated with an application to data from an immunohistochemical study in prostate cancer.

PROVIDER: S-EPMC7092376 | BioStudies |

REPOSITORIES: biostudies

Similar Datasets

| S-EPMC2633005 | BioStudies
| S-EPMC8058550 | BioStudies
| S-EPMC5096953 | BioStudies
2017-01-01 | S-EPMC5099121 | BioStudies
| S-EPMC4046566 | BioStudies
| S-EPMC1950820 | BioStudies
| S-EPMC7530206 | BioStudies
| S-EPMC5459674 | BioStudies
| S-EPMC6156907 | BioStudies
| S-EPMC3439973 | BioStudies