title: A sequential classification rule based on multiple quantitative tests in the absence of a gold standard.
abstract: In many medical applications, combining information from multiple biomarkers could yield a better diagnosis than any single one on its own. When there is a lack of a gold standard, an algorithm of classifying subjects into the case and non-case status is necessary for combining multiple markers. The aim of this paper is to develop a method to construct a composite test from multiple applicable tests and derive an optimal classification rule under the absence of a gold standard. Rather than combining the tests, we treat the tests as a sequence. This sequential composite test is based on a mixture of two multivariate normal latent models for the distribution of the test results in case and non-case groups, and the optimal classification rule is derived returning the greatest sensitivity at a given specificity. This method is applied to a real-data example and simulation studies have been carried out to assess the statistical properties and predictive accuracy of the proposed composite test. This method is also attainable to implement nonparametrically.
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A.
authors.forename: Jingyang
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, Indiana University Fairbanks School of Public Health and School of Medicine, Indianapolis, IN 46202, U.S.A.|Department of Mathematics, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, University of Iowa, Iowa City, IA 52242, U.S.A.|Department of Statistics and Actuarial Sciences, University of Iowa, Iowa City, IA 52242, U.S.A.
authors.forename: Kathryn
authors.initials: K
authors.lastname: Chaloner
authors.affiliation: Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, U.S.A.
authors.forename: Jack T
authors.initials: JT
authors.lastname: Stapleton
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis; D056808:Biostatistics; D003198:Computer Simulation; D003955:Diagnostic Tests, Routine / Q000145:classification / Q000592:standards / Q000706:statistics & numerical data*; D004797:Enzyme-Linked Immunosorbent Assay / Q000706:statistics & numerical data; D018178:Flaviviridae Infections / Q000150:complications; D029407:GB virus C; D015658:HIV Infections / Q000150:complications / Q000821:virology; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests
chemical_list: D015415:Biomarkers
keywords: EM algorithm; diagnostic test; mixture model; multivariate normal