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Combining multiple biomarker models in logistic regression.
Biometrics (2008)
  • Debashis Ghosh, Penn State University
  • Zheng Yuan

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.

  • classification,
  • diagnostic test,
  • generalized degrees of fredom,
  • model selection,
  • receiver operating characteristic curve
Publication Date
Spring March, 2008
Citation Information
Debashis Ghosh and Zheng Yuan. "Combining multiple biomarker models in logistic regression." Biometrics Vol. 64 Iss. 2 (2008)
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