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Comparison of concordance correlation coefficient estimating approaches with skewed data
Josep L Carrasco1, Lluis Jover, Tonya S King
1Biostatistics, Public Health Department, University of Barcelona, Barcelona, Spain. jlcarrasco@ub.edu
Journal of Biopharmaceutical Statistics
|July 7, 2007
Summary
This study compares four methods for estimating the concordance correlation coefficient (CCC) with skewed data. Results highlight differences in performance when data distributions deviate from normality, crucial for accurate agreement assessment.
Area of Science:
- Biostatistics
- Statistical Modeling
Background:
- The concordance correlation coefficient (CCC) measures agreement between continuous variables.
- Four primary estimation methods exist: Lin's method, Variance Components (assume normality), and U-statistics, Generalized Estimating Equations (GEE) (distribution-free).
Purpose of the Study:
- To compare the performance of four CCC estimation methods using skewed data.
- To evaluate methods under a model with log-normal subject means and normal within-subject variability.
Main Methods:
- Simulation study using skewed data.
- Model: Subject means log-normally distributed, within-subject variability normally distributed.
- Comparison of Lin's method, Variance Components, U-statistics, and GEE.
Main Results:
- The study identified performance variations among the four CCC estimation methods when applied to skewed data.
- Differences in CCC estimates were observed based on the underlying data distribution assumptions.
Conclusions:
- The choice of CCC estimation method is critical when dealing with non-normally distributed data.
- Findings provide guidance on selecting appropriate methods for agreement assessment in skewed data scenarios.
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