title: 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.
authors.affiliation: Eli Lilly and Company, Indianapolis, Indiana 46285, USA.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Yuan
authors.forename: Debashis
authors.initials: D
authors.lastname: Ghosh
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000032:analysis*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D012044:Regression Analysis
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins