Confidence interval construction for proportion difference from partially validated series with two fallible classifiers. Issue 6 (2nd November 2022)

• Record Type: Journal Article
• Authors: Qiu, Shi-Fang; Wang, Li-Ming; Tang, Man-Lai; Poon, Wai-Yin
• Is Part Of: Journal of biopharmaceutical statistics. Volume 32:Issue 6(2022)
• Page Start: 871
• Page End: 896
• Publication Date: 2022-11-02
• Subjects: Bootstrap resampling -- Bayesian method -- confidence interval -- fallible classifier -- method of variance estimates recovery; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2022.2058527
• Languages: English
• ISSNs: 1054-3406

Abstract

ABSTRACT: This article investigates the confidence interval (CI) construction of proportion difference for two independent partially validated series under the double-sampling scheme in which both classifiers are fallible. Several CIs based on the variance estimates recovery method of combining confidence limits from asymptotic, bootstrap, and Bayesian methods for two independent binomial proportions are developed under two models. Simulation results show that all CIs except for the bootstrap percentile-t CI and Bayesian credible interval with uniform prior under the independence model and all CIs under the dependence model generally perform well and are recommended. Two examples are used to illustrate the methodologies.