TY - JOUR
T1 - L-statistics of absolute differences for quantifying the agreement between two variables
AU - Tashakor, Elahe
AU - Chinchilli, Vernon M.
N1 - Publisher Copyright:
© 2018, © 2018 Taylor & Francis.
PY - 2019/1/2
Y1 - 2019/1/2
N2 - In many clinical studies, Lin’s (1989) concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. Most commonly, it is used under the assumption that data are normally distributed. However, in many practical applications, data are often skewed and/or thick-tailed. King and Chinchilli (2001) proposed robust estimation methods of alternative CCC indices, and we propose an approach that extends the existing methods of robust estimators by focusing on functionals that yield robust L-statistics. We provide two data examples to illustrate the methodology, and we discuss the results of computer simulation studies that evaluate statistical performance.
AB - In many clinical studies, Lin’s (1989) concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. Most commonly, it is used under the assumption that data are normally distributed. However, in many practical applications, data are often skewed and/or thick-tailed. King and Chinchilli (2001) proposed robust estimation methods of alternative CCC indices, and we propose an approach that extends the existing methods of robust estimators by focusing on functionals that yield robust L-statistics. We provide two data examples to illustrate the methodology, and we discuss the results of computer simulation studies that evaluate statistical performance.
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U2 - 10.1080/10543406.2018.1489406
DO - 10.1080/10543406.2018.1489406
M3 - Article
C2 - 29953327
AN - SCOPUS:85049145347
SN - 1054-3406
VL - 29
SP - 174
EP - 188
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
IS - 1
ER -