A local agreement pattern measure based on hazard functions for survival outcomes

A local agreement pattern measure based on hazard functions for survival outcomes IntroductionIn biomedical studies, researchers are often interested in assessing agreement on measurements taken on the same subjects using different methods or by different raters. There has been extensive literature on assessing agreement and making appropriate inference. For continuous data, various measures including both scaled measures, such as concordance correlation coefficient (CCC) (Lin et al., ) and unscaled ones, such as total deviation index (Lin et al., ), have been proposed. For categorical data, the kappa coefficient and its extensions (Cohen, , ) have been widely used.All of the aforementioned methods take the strategy of quantifying the agreement of interest by a global summary measure. While being simple, they have been criticized for their limitations in fully capturing agreement information (Tanner and Young, ; Darroch and McCloud, ). For example, with two categorical scales, Agresti () showed that when a simple quasi‐symmetry model holds for the contingency table, the kappa contains all relevant information about the structure of the agreement. However, when the quasi‐symmetry model fails, various agreement patterns can produce the same kappa value and the kappa coefficient alone is not capable of distinguishing different agreement patterns. Given the limitation of kappa coefficient, Tanner and Young (), Agresti (), http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

A local agreement pattern measure based on hazard functions for survival outcomes

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12740
Publisher site
See Article on Publisher Site

Abstract

IntroductionIn biomedical studies, researchers are often interested in assessing agreement on measurements taken on the same subjects using different methods or by different raters. There has been extensive literature on assessing agreement and making appropriate inference. For continuous data, various measures including both scaled measures, such as concordance correlation coefficient (CCC) (Lin et al., ) and unscaled ones, such as total deviation index (Lin et al., ), have been proposed. For categorical data, the kappa coefficient and its extensions (Cohen, , ) have been widely used.All of the aforementioned methods take the strategy of quantifying the agreement of interest by a global summary measure. While being simple, they have been criticized for their limitations in fully capturing agreement information (Tanner and Young, ; Darroch and McCloud, ). For example, with two categorical scales, Agresti () showed that when a simple quasi‐symmetry model holds for the contingency table, the kappa contains all relevant information about the structure of the agreement. However, when the quasi‐symmetry model fails, various agreement patterns can produce the same kappa value and the kappa coefficient alone is not capable of distinguishing different agreement patterns. Given the limitation of kappa coefficient, Tanner and Young (), Agresti (),

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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