title: Assessing treatment efficacy in the presence of diagnostic errors.
abstract: This paper investigates estimating and testing treatment effects in randomized control trials where imperfect diagnostic device is used to assign subjects to treatment and control group(s). The paper focuses on pre-post design and proposes two new methods for estimating and testing treatment effects. Furthermore, methods for computing sample sizes for such design accounting for misclassification of the subjects are devised. The methods are compared with each other and with a traditional method that ignores the imperfection of the diagnostic device. In particular, the likelihood-based approach shows a significant advantage in terms of power, coverage probability and, consequently, in reduction of the required sample size. The application of the results are illustrated with data from an aging trial for dementia and data from electroencephalogram (EEG) recordings of alcoholic and non-alcoholic subjects. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Statistics, University of Kentucky, 725 Rose Street, 347 Multidisciplinary Science, Lexington, 40536, KY, U.S.A.|Department of Mathematical Sciences, University of Montana, 32 Campus Drive, Missoula, 59812, MT, U.S.A.
authors.forename: Solomon
authors.initials: S
authors.lastname: Harrar
authors.affiliation: Department of Health Sciences, New Mexico State University, 1335 International Mall, Suite 326, Las Cruces, NM, 88003, U.S.A.
authors.forename: Anup
authors.initials: A
authors.lastname: Amatya
authors.affiliation: Department of Mathematical Sciences, University of Montana, 32 Campus Drive, Missoula, 59812, MT, U.S.A.
authors.forename: Leonid
authors.initials: L
authors.lastname: Kalachev
mesh_terms: D003951:Diagnostic Errors; D006801:Humans; D016013:Likelihood Functions; D018401:Sample Size; D016896:Treatment Outcome
keywords: diagnostic accuracy; expectation-maximization algorithm; mixture of normals; negative predictive value; positive predictive value; power; sample size