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May 27, 2014

Method Comparison

This add-in helps you compare measurement methods according to CLSI guidelines.  It calls various JMP platforms behind the scenes to fit the data and create a variety of graphical and tabular results.


The add-in consists of four primary routines:  Accuracy, Precision, Linearity, and Performance.  The input data table to each of them must be in stacked format, that is, with one row per individual response.  One column must identify the different rows of data corresponding to the different methods used (the Method Identifer column).  Other required columns of data depend on the routine you use. 


To install the add-in, download "Method Comparison.jmpaddin", drag it onto an open JMP window, then click "Install".   You may also want to download the two example data sets "Compound" and "Bland-Altman" and open them in your JMP window.  The former is used for illustration in the help document.


For details on each of the routines after installing the add-in, click Add-Ins > Method Comparison > Help.


Some example screen shots are below.


6635_Accuracy 1.jpg

6636_Accuracy 2.jpg



6639_Performance 1.jpg

6640_Performance 2.jpg




thanks for the add-in for method comparison. I have a question/remark regarding the Bland Altman plot: why is using the Std Error and not the Std Deviation (SD) to calculate the 95% limits of agreement? The use of Std Error is wrong and consequently is giving wrong limits of agreement.


Literature about Bland Altman plot and the use of Std Deviation (SD):


1. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135-60

2. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud 2010;47:931-6

3.Giavarina D. Understanding Bland Altman analysis. Biochemia Medica. 2015;25(2):141-151






What method are you using for the Confidence Intervals for the Passing Bablock intercept and slope?  If it is bootstrap then please specify what type (e.g., 2.5th and 97th percentile of the distribution of the predicted Y from all the bootstrap estimates) or something else.