Agreement Analysis: What He Said, She Said Versus You Said.

Correlation and agreement are 2 concepts that are widely applied in the medical literature and clinical practice to assess for the presence and strength of an association. However, because correlation and agreement are conceptually distinct, they require the use of different statistics. Agreement is a concept that is closely related to but fundamentally different from and often confused with correlation. The idea of agreement refers to the notion of reproducibility of clinical evaluations or biomedical measurements. The intraclass correlation coefficient is a commonly applied measure of agreement for continuous data. The intraclass correlation coefficient can be validly applied specifically to assess intrarater reliability and interrater reliability. As its name implies, the Lin concordance correlation coefficient is another measure of agreement or concordance. In undertaking a comparison of a new measurement technique with an established one, it is necessary to determine whether they agree sufficiently for the new to replace the old. Bland and Altman demonstrated that using a correlation coefficient is not appropriate for assessing the interchangeability of 2 such measurement methods. They in turn described an alternative approach, the since widely applied graphical Bland-Altman Plot, which is based on a simple estimation of the mean and standard deviation of differences between measurements by the 2 methods. In reading a medical journal article that includes the interpretation of diagnostic tests and application of diagnostic criteria, attention is conventionally focused on aspects like sensitivity, specificity, predictive values, and likelihood ratios. However, if the clinicians who interpret the test cannot agree on its interpretation and resulting typically dichotomous or binary diagnosis, the test results will be of little practical use. Such agreement between observers (interobserver agreement) about a dichotomous or binary variable is often reported as the kappa statistic. Assessing the interrater agreement between observers, in the case of ordinal variables and data, also has important biomedical applicability. Typically, this situation calls for use of the Cohen weighted kappa. Questionnaires, psychometric scales, and diagnostic tests are widespread and increasingly used by not only researchers but also clinicians in their daily practice. It is essential that these questionnaires, scales, and diagnostic tests have a high degree of agreement between observers. It is therefore vital that biomedical researchers and clinicians apply the appropriate statistical measures of agreement to assess the reproducibility and quality of these measurement instruments and decision-making processes.