**9. Laboratory data**

### **9.1. Advantages of animal models**

Testing in animal models has two big advantages:


### **9.2. Showing accuracy and trending**

Bland-Altman and concordance analysis can still be used to assess accuracy and trending. However, the ability to perform multiple readings over a range of cardiac output and condi‐ tions against a gold standard method allow the test method to be fully assessed. Regression analysis and correlation now are the appropriate methods for analyzing the data. Regression plots from each animal experiment are used to show how the test method behaves over a range of cardiac output. The regression line defines the relationship between test and flow probe methods. Correlation reflects the repeatability and trending ability of the test method, rather than the agreement between methods. Either r or R2 are quoted. R2 is used when a relationship exists between the two methods. The correlation coefficient (R2 ) ranges from 0 to 1, where a value > 0.9 signifies good correlation. Ideally, if the test and reference (i.e. flow probe) methods are correctly calibrated, their data should lie along the line of identity y=x and correlation can also be performed along this line, which is known as Lin's concordance. Alternatively, the interclass correlation coefficient (ICC) is used. These methods were used in our 2005 paper to validate the supra-sternal Doppler method in anaesthetized dogs [52].
