Simple moment-based inferences of generalized concordance correlation

Autor:	John J. Chen, Chen Ji, George F. Steinhardt, Guangxiang Zhang
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
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::167a4536ecb9c91d59a09716c84b62ca https://doi.org/10.1080/02664763.2010.529884 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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