title: Flexible regression models for ROC and risk analysis, with or without a gold standard.
abstract: A novel semiparametric regression model is developed for evaluating the covariate-specific accuracy of a continuous medical test or biomarker. Ideally, studies designed to estimate or compare medical test accuracy will use a separate, flawless gold-standard procedure to determine the true disease status of sampled individuals. We treat this as a special case of the more complicated and increasingly common scenario in which disease status is unknown because a gold-standard procedure does not exist or is too costly or invasive for widespread use. To compensate for missing data on disease status, covariate information is used to discriminate between diseased and healthy units. We thus model the probability of disease as a function of 'disease covariates'. In addition, we model test/biomarker outcome data to depend on 'test covariates', which provides researchers the opportunity to quantify the impact of covariates on the accuracy of a medical test. We further model the distributions of test outcomes using flexible semiparametric classes. An important new theoretical result demonstrating model identifiability under mild conditions is presented. The modeling framework can be used to obtain inferences about covariate-specific test accuracy and the probability of disease based on subject-specific disease and test covariate information. The value of the model is illustrated using multiple simulation studies and data on the age-adjusted ability of soluble epidermal growth factor receptor - a ubiquitous serum protein - to serve as a biomarker of lung cancer in men. SAS code for fitting the model is provided. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: Biostatistics Program, Oregon State University, Corvallis, 97331, Oregon, U.S.A.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Branscum
authors.affiliation: Department of Statistics, University of California, Irvine, CA, U.S.A.
authors.forename: Wesley O
authors.initials: WO
authors.lastname: Johnson
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, SC, U.S.A.
authors.forename: Timothy E
authors.initials: TE
authors.lastname: Hanson
authors.affiliation: Loring Life Sciences, Inc., Lexington, KY, U.S.A.
authors.forename: Andre T
authors.initials: AT
authors.lastname: Baron
mesh_terms: D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000097:blood; D056808:Biostatistics; D003198:Computer Simulation; D066246:ErbB Receptors / Q000097:blood; D006801:Humans; D008175:Lung Neoplasms / Q000097:blood / Q000175:diagnosis; D008297:Male; D015233:Models, Statistical; D012372:ROC Curve; D012044:Regression Analysis; D018570:Risk Assessment / Q000706:statistics & numerical data
chemical_list: D014408:Biomarkers, Tumor; D066246:ErbB Receptors
keywords: Bayesian semiparametric; Polya tree; disease diagnosis; medical test evaluation