The mountain plot, created by my colleague Mike Lynch while we were at Ciba Corning is part of the CLSI standard EP21-A. It was used extensively while we were at Ciba Corning, but it has not been very popular as it is not often cited. On the other hand, the Bland-Altman plot is frequently cited.
But this is not a competition. Sometimes one plot is better, sometimes the other and often both should be shown. I was at a glucose meter conference this September in Washington DC where someone was presenting data for two glucose meters vs. reference using a Bland Altman plot. He should have been using a mountain plot. I don’t have his data, but this is an example of when the mountain plot is better than the Bland-Altman plot.
With the Bland-Altman plot, the pattern of the “bad” vs. “good” assay is harder to see than with the mountain plot. Moreover, as more data gets added, the Bland-Altman plot becomes a mess of dots, whereas the mountain plot remains sharp. If there were 3 or 4 glucose meters, the mountain plot would be even better.
To construct a mountain plot in a spreadsheet:
- Calculate the differences between the candidate and reference assay
- Sort the differences from low to high
- Rank the sorted differences
- Calculate the cumulative probability as rank / (number of observations + 1)
- Calculate the adjusted cumulative probability as: If the cumulative probability is greater than 0.5, use 1- cumulative probability.