A new approach for interval estimation and hypothesis testing of a certain intraclass correlation coefficient: the generalized variable method.
We consider the problems of interval estimation and hypothesis testing for the intraclass correlation coefficient in an interrater reliability study when both raters and subjects are assumed to be randomly selected from populations of raters and subjects, respectively. We propose a novel approach for the confidence interval estimation and hypothesis testing using the concepts of generalized confidence interval (GCI) and generalized P-values. A simulation study is conducted to investigate the coverage probabilities of the GCI approach relative to the modified large sample (MLS) approach. Both methods tend to provide somewhat conservative coverage. Relative to the MLS approach, the GCI approach is closer to the correct (nominal) coverage for a two-sided interval, but farther to the correct coverage for a one-sided lower interval. Unlike the MLS approach, the GCI approach can also easily provide P-values. The fact that the GCI approach is suitable for confidence interval estimation and obtaining P-values makes the GCI approach a suitable candidate for making inference about interrater reliability.
Tian
Lili
L
Department of Statistics, Biostatistics Division, University of Florida, Gainesville 32610, USA. ltian@biostat.ufl.edu
Cappelleri
Joseph C
JC
D000465:Algorithms; D016001:Confidence Intervals; D015233:Models, Statistical; D015588:Observer Variation; D015203:Reproducibility of Results; D014481:United States