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Activity Number: 684
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308717
Title: Simultaneous Inference of Method Agreement and Rater Reliability Through General Clustered Repeated-Measures Data
Author(s): Shasha Bai*+ and Abigail Shoben and Haikady Nagaraja
Companies: The Ohio State University and The Ohio State University and The Ohio State University
Keywords: agreement ; reliability ; intraclass correlation ; heteroscedasticity
Abstract:

Agreement indices provide important information about the quality of the measurements. While agreement estimates are affected by various sources, most studies focus on either method bias or human error, but not both simultaneously. This research proposes a study design where raters use multiple methods to take replicated measures on the experimental units. The units are nested within independent clusters. This general data structure covers a wide range of scenarios where agreement factors can be nested or crossed, random or fixed. Moreover, different units may show heterogeneous error variability due to their distinct biological or geographic conditions, which should be taken into consideration in measuring agreement. Simultaneous inferences of method agreement and rater reliability can be made with the cluster-adjusted intra-class correlation coefficient (ICC) through the linear mixed-effect model assuming heteroscedasticity. An example from Sports Medicine of method comparison and surgeon agreement on knee injuries is provided as illustration.


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