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Statistical Reference Guide
Method comparison
Plotting a mountain plot
Plot a mountain plot to see the distribution of the differences between two methods.
Select a cell in the dataset.
On the
Analyse-it
ribbon tab,
in the
Statistical Analyses
group,
click
Method Comparison
, and then click
Mountain
.
The analysis task pane opens.
If the data are in 2 variables:
In the
X
drop-down list, select the comparative or reference measurement procedure variable.
In the
Y
drop-down list, select the test measurement procedure variable.
Note:
If the variables consist of replicate measurements, select the variable name that spans all the replicate columns.
If the data are in 2 variables with a separate variable identifying replicates of each item:
In the
X
drop-down list, select the comparative or reference measurement procedure variable.
In the
Y
drop-down list, select the test measurement procedure variable.
In the
Item
drop-down list, select the variable identifying each item.
If the data are in a single variable with a separate variable matching each item and a variable identifying the method:
In the
Model
drop-down menu, select
Matched Pairs
.
In the
Y
drop-down list, select the measurement variable.
In the
Item
drop-down list, select the item variable that identifies each item.
In the
Method
drop-down list, select the method variable.
If the data are measured in replicate and the X method is a reference method and the Y method is a comparative method, select the
Mean X, 1st Y replicate
check box so that the differences represent the difference between an individual test result by the Y method and the average of the replicates for the X method. These differences are the best representation of the total error in the test method, by using the mean of the X method it reduces the amount of random error in the X result so it reflects the true value of the reference method to compare the test method against. If you select
Mean X, Mean Y
, the differences will only represent the some of the error, namely the systematic error due to bias with some of the random error removed due to averaging of replicates. Likewise, if you select
1st X, replicate, 1st Y replicate
the differences will include any random error in the X method so not reflect the error just in the test method. There may be occasions when these other sources of error are of interest, but generally the interest is in the total error of the test method.
Click
Calculate
.
Related concepts
Mountain plot (folded CDF plot)
Related reference
Dataset layout
Related information
CLSI. (2013).
Estimation of Total Analytical Error for Clinical Laboratory Methods (EP21-A)
. Clinical and Laboratory Standards Institute.
Available in Analyse-it Editions
Method Validation edition
Ultimate edition
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Administrator's Guide
User's Guide
Statistical Reference Guide
Distribution
Compare groups
Compare pairs
Contingency tables
Correlation and association
Principal component analysis (PCA)
Factor analysis (FA)
Item reliability
Fit model
Method comparison
Correlation coefficient
Scatter plot
Fit Y on X
Fitting ordinary linear regression
Fitting Deming regression
Fitting Passing-Bablok regression
Linearity
Residual plot
Checking the assumptions of the fit
Average bias
Estimating the bias between methods at a decision level
Testing commutability of other materials
Difference plot (Bland-Altman plot)
Fit differences
Plotting a difference plot and estimating the average bias
Limits of agreement (LoA)
Plotting the Bland-Altman limits of agreement
Mountain plot (folded CDF plot)
Plotting a mountain plot
Partitioning and reducing the measuring interval
Agreement measures for binary and semi-quantitative data
Chance corrected agreement measures for binary and semi-quantitative data
Agreement plot
Estimating agreement between two binary or semi-quantitative methods
Study design
Study design for qualitative methods
Measurement systems analysis (MSA)
Reference interval
Diagnostic performance
Control charts
Process capability
Pareto analysis
Study Designs
Bibliography
Version 5.65
Published 14-Aug-2020
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