## Concordance Correlation Coefficient: Definition & Interpretations

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How well do two diagnostic measurements agree? Many times continuous units of measurement are used in the diagnostic test. We may not be interested in correlation or linear relationship between the two measures, but in a measure of agreement.

The concordance correlation coefficient, r cfor measuring agreement between continuous variables X and Y both approximately normally distributedis calculated as follows:. The sample estimate, r cis an estimate of the population concordance correlation coefficient:. In that trial, participants underwent hourly blood draws between The participants hated this!

They complained about the sleep disruption every hour when the nurses came by to draw blood, so the ACRN wanted to determine for future studies if the cortisol AUC calculated on measurements every two hours was in good agreement with the binary correlation coefficient measures concordance cortisol AUC calculated on hourly measurements. The baseline data were used to investigate how well these two measurements agreed. If there is good agreement, the protocol could be changed to take blood every two hours.

The ACRN judged this to be excellent agreement, so it will use two-hourly the binary correlation coefficient measures concordance in future studies. Eberly College of Science. Printer-friendly version How the binary correlation coefficient measures concordance do two diagnostic measurements agree?

The concordance correlation coefficient, r cfor measuring agreement between continuous variables X and Y both approximately normally distributedis calculated as follows: The sample estimate, r cis an estimate of the population concordance correlation coefficient: Note for this SAS program - Run the program to view the output.

This is higher level SAS than you are expected to program yourself in this course, but some of you may find the programming of interest. What about binary or ordinal data? Cohen's Kappa Statistic will handle this Welcome to STAT !

Clinical Trials as Research Lesson 2: Ethics of Clinical Trials Lesson 3: Clinical Trial Designs Lesson 4: Bias and Random Error Lesson 5: Objectives and Endpoints Lesson 6: The Study Cohort Lesson 8: Treatment Allocation and Randomization Lesson 9: Interim Analyses and Stopping Rules Lesson Missing Data and Intent-to-Treat Lesson Estimating Clinical Effects Lesson Prognostic Factor Analyses Lesson Factorial Design Lesson Crossover Designs Lesson Overviews and Meta-analysis Lesson Medical Diagnostic Testing Lesson Correlation and Agreement