title: Value of information methods for assessing a new diagnostic test.
abstract: Value-of-information methods are applied to assess the evidence in support of a new diagnostic test and, where the evidence is insufficient for decision making, to determine the optimal sample size for future studies. Net benefit formulations are derived under various diagnostic and treatment scenarios. The expressions for the expected opportunity loss of adopting strategies that include the new test are given. Expressions for the expected value of information from future studies are derived. One-sample and two-sample designs, with or without known prevalence, are considered. An example is given.
authors.affiliation: Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
authors.forename: Maggie Hong
authors.initials: MH
authors.lastname: Chen
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Willan
mesh_terms: D001499:Bayes Theorem; D003362:Cost-Benefit Analysis / Q000191:economics*; D003662:Decision Theory; D003955:Diagnostic Tests, Routine / Q000379:methods* / Q000592:standards; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D018803:Models, Economic; D011247:Pregnancy; D011655:Pulmonary Embolism / Q000175:diagnosis
keywords: diagnostic tests; full Bayesian approach, incremental net benefit; value of information