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Kappa statistic for clustered matched‐pair data
Author(s) -
Yang Zhao,
Zhou Ming
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6113
Subject(s) - estimator , statistic , statistics , variance (accounting) , nonparametric statistics , mathematics , cohen's kappa , cluster (spacecraft) , kappa , computer science , accounting , business , programming language , geometry
Kappa statistic is widely used to assess the agreement between two procedures in the independent matched‐pair data. For matched‐pair data collected in clusters, on the basis of the delta method and sampling techniques, we propose a nonparametric variance estimator for the kappa statistic without within‐cluster correlation structure or distributional assumptions. The results of an extensive Monte Carlo simulation study demonstrate that the proposed kappa statistic provides consistent estimation and the proposed variance estimator behaves reasonably well for at least a moderately large number of clusters (e.g., K ⩾ 5 0 ). Compared with the variance estimator ignoring dependence within a cluster, the proposed variance estimator performs better in maintaining the nominal coverage probability when the intra‐cluster correlation is fair ( ρ ⩾ 0 . 3 ) , with more pronounced improvement when ρ is further increased. To illustrate the practical application of the proposed estimator, we analyze two real data examples of clustered matched‐pair data. Copyright © 2014 John Wiley & Sons, Ltd.