Distinguishability and agreement with continuous data

Abstract

The analysis of concordance among repeated measures has received a huge amount of attention in the statistical literature leading to a range of different approaches. However, because all the approaches are able to assess the closeness among the readings taken on the same subject, the conclusions about the degree of concordance should be similar regardless the approach applied. Here, two indices to assess the concordance among continuous repeated measures, the intraclass correlation coefficient and the total deviation index, are applied and compared in two case examples. The first example concerns the repeatability of individual nutrient allocation strategy assessed by stable isotope analysis. The second example dealt with the assessment of the concordance of functional magnetic resonance imaging data that shows spatial correlation. The results differ depending upon the approach applied leading to contradictory conclusions about the degree of concordance. The reason behind these results is discussed reaching the conclusion that the total deviation index is just assessing agreement among repeated measurements, whereas the intraclass correlation coefficient assesses the concept of distinguishability among subjects that involves agreement among repeated measurements and spread of subjects at once. Therefore, the best way to select the right approach is to understand the right question behind the research hypothesis.

Keywords: agreement; concordance; distinguishability; functional magnetic resonance imaging; intraclass correlation coefficient; linear mixed models; repeatability; repeated measures; total deviation index.