Abstract Details
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.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.