Deciding whether two measurement systems agree well enough to be used interchangeably is important in medical and clinical contexts. Recently, the probability of agreement was proposed as an alternative to comparison techniques such as correlation, regression, and the limits of agreement approach, when the systems’ measurement errors are homoscedastic. However, in medical and clinical contexts, it is common for measurement variability to increase proportionally with the magnitude of measurement. In this article, we extend the probability of agreement analysis to accommodate heteroscedastic measurement errors, demonstrating the versatility of this simple metric. We illustrate its use with two examples: one involving the comparison of blood pressure measurement devices, and the other involving the comparison of serum cholesterol assays.
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