Simple moment-based inferences of generalized concordance correlation
Autor: | John J. Chen, Chen Ji, George F. Steinhardt, Guangxiang Zhang |
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Rok vydání: | 2011 |
Předmět: |
Statistics and Probability
Intraclass correlation Fisher transformation media_common.quotation_subject Estimator Inference Variance (accounting) Moment (mathematics) Concordance correlation coefficient Statistics Applied mathematics Statistics Probability and Uncertainty Normality Mathematics media_common |
Zdroj: | Journal of Applied Statistics. 38:1867-1882 |
ISSN: | 1360-0532 0266-4763 |
Popis: | We proposed two simple moment-based procedures, one with (GCCC1) and one without (GCCC2) normality assumptions, to generalize the inference of concordance correlation coefficient for the evaluation of agreement among multiple observers for measurements on a continuous scale. A modified Fisher's Z-transformation was adapted to further improve the inference. We compared the proposed methods with U-statistic-based inference approach. Simulation analysis showed desirable statistical properties of the simplified approach GCCC1, in terms of coverage probabilities and coverage balance, especially for small samples. GCCC2, which is distribution-free, behaved comparably with the U-statistic-based procedure, but had a more intuitive and explicit variance estimator. The utility of these approaches were illustrated using two clinical data examples. |
Databáze: | OpenAIRE |
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