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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.

SUBMITTER: Yuan Z 

PROVIDER: S-EPMC7092376 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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Combining multiple biomarker models in logistic regression.

Yuan Zheng Z   Ghosh Debashis D  

Biometrics 20080305 2


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 con  ...[more]

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