title: A novel nonparametric confidence interval for differences of proportions for correlated binary data.
abstract: Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n - 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance.
authors.affiliation: 1 State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Guangzhou, China; Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China.
authors.forename: Chongyang
authors.initials: C
authors.lastname: Duan
authors.affiliation: 1 State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Guangzhou, China; Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China.
authors.forename: Yingshu
authors.initials: Y
authors.lastname: Cao
authors.affiliation: 1 State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Guangzhou, China; Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China.
authors.forename: Lizhi
authors.initials: L
authors.lastname: Zhou
authors.affiliation: 1 State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Guangzhou, China; Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China.|2 Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University, NW, Washington DC, USA.
authors.forename: Ming T
authors.initials: MT
authors.lastname: Tan
authors.affiliation: 1 State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Guangzhou, China; Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China.
authors.forename: Pingyan
authors.initials: P
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000706:statistics & numerical data; D016001:Confidence Intervals; D006801:Humans; D011336:Probability; D012372:ROC Curve; D018401:Sample Size; D018709:Statistics, Nonparametric
keywords: Nonparametric confidence interval; area under receiver operating characteristic; paired data; proportion difference; t-approximation