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This web-page describes the concept of estimating agreement and provides examples of such statistical tests. Reading this will give you an overview of what analysis of agreement is.
Recommended prereading are the pages Introduction to statistics and Inferential statistics.
Agreement means to investigate to what extent different measurements trying to estimate the same phenomenon agrees with each other. Typical situations are:
- To compare two different measurements of the same phenomenon as when you evaluate a diagnostic test against a gold standard.
- Estimate inter rater agreement (if different users come to the same result when estimating the same phenomenon).
- Estimate test-retest agreement (if the same user come to the same estimate if their testing of the same phenomenon is repeated)
The statistical approach most suitable depends on what level of measurement (or scale of measurement) is most appropriate for the investigated variable:
- Agreement between variables measured with the nominal scale
- The outcome is dichotomous (only two possible outcomes)
- Kappa coefficient
- Sensitivity / Specificity
- Likelihood ratio
- Predictive value of tests
- Etiologic predictive value (when there is no gold standard)
- The outcome can have more than two possible outcomes
- The outcome is dichotomous (only two possible outcomes)
- Agreement between variables measured with the ordinal scale
- Agreement between variables measured with an interval scale or a ratio scale
Always require a 95% confidence interval for estimates of sensitivity, specificity, likelihood ratios and predictive values! Point estimates without a confidence interval are useless.
Gold standard
(This section is under construction. We apoligise for any inconvinience.)
Estimating the clinical value of a test
Sensitivity and specificity informs us about the health of the diagnostic test being evaluated. This is great if you are a manufacturer of a diagnostic test but of limited value if you are a doctor. Likelihood ratios informs how much more information a test adds and predictive values informs us about the health of our patient (provides the probability that the individual has what we are looking for). Read more about this on the page Evaluation of tests.