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- Method Comparison

May 28, 2014 3:49 AM

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".

For example analyses, click Add-Ins > Method Comparison > Help after installing the add-in. The add-in also includes four example data sets: "Compound Comparison", "Peak Expiratory Flow Rate", "Simulated Example", and "Systolic Blood Pressure". Click Add-Ins > Method Comparison > Example Data to open them.

Some example screen shots are below.

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01-24-2017
04:21 AM

Hi,

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

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03-24-2017
08:10 AM

Hi,

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.

Thanks,

David

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08-24-2017
07:53 PM

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10-17-2017
12:26 PM

@sstinca Apologies for the very long delay. When we switched over to the new JMP Community website somehow I never got notified of your message. Thanks for the great catch and I just uploaded an update that includes the Bland-Altman limits using standard deviation and a shaded display on the Matched Pairs graph. Also added the Systolic Blood Pressure data example from their 1999 paper and numbers agree.

@dstokar The limits are based on the original SAS Macro code from Roche in Penzberg, Germany, which implement the method from Passing and Bablok (1983) and do not use the bootstrap. If you have good evidence the bootstrap works well here please share, and also the JSL code is open so you can see exactly what is going on and modify it as desired.

@juanpahn Suggest working through the example in the help doc, making sure you understand everything, then try it on your own data.

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01-23-2018
12:20 AM

I am using the ‘Method Comparison’ Add-In developed by Russ Wolfinger to compare different methods to analyze ovaries using 2D and 3D ultrasound. I have two questions:

- The Deming Regression provides the confidence interval for the slope to evaluate proportional bias, but a confidence interval is not provided in the output for the intercept. I understand that we can evaluate systematic bias from the mean difference output provided underneath the Bland Altman graphs, but for completeness in our results, it would be helpful to have the CI for the intercept of the Deming Regression as well. Can this be included in the input and/or how do you recommend I ascertain the confidence interval around the intercept?
- I am uncertain what the black diamond/ lines around the periphery of the Bland Altman graphs are – it’s not clarified in the ‘Help’ section and not described by Bland Altman’s original publication. Is it possible to clarify what these lines represent?

Many thanks.

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05-04-2018
05:07 PM

Hi Russ,

Stupid question probably, but I'll ask still...

I went through the tutorial with the example you give in the attached word document and it worked perfectly just like in your example but then when I tried with my data, I got totally confused.

I just would like to have a Passing-Bablok regression analysis done. I just need to have and equation to my curve and a correlation coefficient. My data is like the table below.

specimen | method1 | method2 | method3 |

S1 | 0.322613947 | 0.283500366 | 0.394930806 |

S2 | 1.836453685 | 1.666623257 | 1.081888255 |

S3 | 3.276624674 | 3.338295056 | 1.384727721 |

S4 | 4.718858377 | 4.697249324 | 1.58673576 |

S5 | 0.318644745 | 0.311580094 | 0.272826006 |

S6 | 1.880639096 | 1.957200801 | 0.929268173 |

S7 | 3.390383424 | 3.417362369 | 1.28341812 |

S8 | 4.564628617 | 4.394208312 | 1.527136804 |

S9 | 0.343720425 | 0.424129692 | 0.912531427 |

S10 | 2.187688239 | 2.413766417 | 0.20302838 |

S11 | 3.402107778 | 3.153607989 | 1.277589378 |

S12 | 5.273795942 | 5.300136478 | 1.838240998 |

S13 | 0.67104692 | 0.414658177 | 0.432221887 |

S14 | 2.085992847 | 1.965456038 | 0.940358883 |

S15 | 3.712645639 | 4.047605658 | 1.189165635 |

S16 | 4.987225862 | 4.857789074 | 1.424112418 |

S17 | 0.298670082 | -0.114521527 | 0.449896278 |

S18 | 1.875760383 | 1.8653793 | 0.918795834 |

S19 | 3.703566214 | 3.642433157 | 1.112434596 |

I thought maybe I needed a table like the one below and put the first column as "method identifier" and the second column in "X, concentration" or "Y, response" but then, what do I put in X or Y?

measure | |

method1 | 0.322614 |

method1 | 1.836454 |

method1 | 3.276625 |

method1 | 4.718858 |

method1 | 0.318645 |

method1 | 1.880639 |

method2 | 3.390383 |

method2 | 4.564629 |

method2 | 0.34372 |

method2 | 2.187688 |

method2 | 3.402108 |

method2 | 5.273796 |

In the compound comparison tutorial, I don't understand what is "method identifier" and what I am supposed to put here. I guess "X, concentration" corresponds to the reference method measures and "Y, response" corresponds to the new method measures but I am not even sure of that. Any way to help? Thanks a lot

--Pierre

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05-04-2018
05:29 PM

@Heidi_V, yikes, sorry for the long delay! There is a request in to R&D get that confidence interval on the intercept printed in JMP's Orthogonal Regression platform. Thanks for the request and please feel free to keep bugging us about it, or if you are feeling bold I don't think the calculations are too bad given what the add-in already produces. For the diamonds in the Matched Pairs platform, see page 248 of https://www.jmp.com/support/downloads/pdf/jmp9/basic_analysis_and_graphing.pdf

@Pierre, you are on the right track with stacking your data. Your first new column is the method identifier and the second one is Y. You're going to need add an X column somehow; ideally from the experiment itself or if you just don't have it I think using sequential integers (starting over within each method) should work. The examples from the add-in are good to study.

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06-11-2018
04:09 PM

Russ,

Looking at some data and comparing to manual calculations done in excel and matlab it seems that JMP calculates the LOA as the std deviation of differences * 1.96. However the Bland-Atlman paper from 1986 calls for using a corrected standard deviation when repeated measures are used that is *sc = [S**D^*2 + .25 (*s1)^*2 + .25 (*s*2)^2]^5

Is there a way to run the accuracy add-in that allows for repeated measures?

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3 weeks ago

Hi @russ_wolfinger,

I recently downloaded the method comaprison add-in, but unfortunately, I can't get it to work. I installed the add-in and followed the first example using the provided data table. After hitting OK, I get the following error message:

*could not find column in access or evaluation of 'Column' , Column/*###*/(splitTbl, Eval( opValList[1] ))*

Any help would be much appriciated.

Thanks,

Hans

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3 weeks ago

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3 weeks ago

Hi Russ!

I am using the Method Comparison Add-in to fit a Passing-Bablok regression. The add-in works great because it also adds CI's for the regression estimates which is requested by many regulatory bodies. It also computes an additional orthogonal fit for comparison.

Now I am trying to calculate the predicted value at a given X point, with Confidence Intervals using the PB fit. Is there a way to do this? because the PB fit does not have a red triangle with options.

Thanks!

Dave

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3 weeks ago

Hi @russ_wolfinger ,

Thanks for your fast response. Relaunching JMP worked!

I noticed now that the Action button "Remove" acts as a kind of refresh. In other words, after adding the method identifier, I have to hit "Remove" to update and populate the dialog box in the lower left corner. Afterwards, I can proceed with the analysis as described in the help document.

For your info, I'm running JMP 12.1.0 (64-bit) on a Windows 10 Enterprise 64-bit system with no regional settings (that I know of) and English as the default language.

Now, I moved on to my own data, but apparently I still don't quite understand how to correctly format the input table after studying the example data. Since Pierre previously asked a similar question, I think it would be worthwhile bringing it up again.

In my experiment, I am repeatedly (different donors) measuring two molecules/analytes with methods 1 and 2. Method 1 is the established reference and I want to compare it to the results from method 2. Currently, my table is formatted like this:

Col1: Donor

Col2: Analyte

Col3: results method1

Col4: results method2

Example:

Donor | Analyte | Method1 | Method2 |

1 | 1 | 142 | 143 |

2 | 1 | 143 | 141 |

3 | 1 | 142 | 142 |

.... n = 40 | 1 | 118 | 120 |

1 | 2 | 3.04 | 3.05 |

2 | 2 | 4.49 | 4.39 |

... n =40 | 2 | 6.05 | 5.8 |

Now I would like to compare method 1 & method 2 for each analyte, and get a separate regression and Bland-Altman plot for each analyte.

Do you have any hints on how to format the table correctly?

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3 weeks ago

@vtkm, apologies for the long delay in replying. I've just had the chance to look back at the standard deviation adjustment formula you reference from Bland and Altman (1986). When moving to repeated measures, there are choices regarding the assumed form of covariance structure among the observations. Bland and Altman appear to make a certain assumption along these lines that involves a form of heterogeneity between the two methods, as evidenced by their use of s_1 and s_2 (btw, I could not exactly reproduce the 21.6 and 28.2 values they indicate for s_1 and s_2 in their PEFR example, the data for which is included with this add-in under Example Data). Their adjustment formula also only applies to two repeated measures and perfectly balanced data. A better and more general approach would be a mixed model analysis that considers a few reasonable covariance structures and computes appropriate standard deviations based on a well-chosen one. Refer to recently released *SAS for Mixed Models* by Stroup et al. for full theory and applications + JMP Pro has good mixed model capabilities that could be utilized here. This add-in analyzes the means across repeated measures directly, which I think is still reasonable in many cases, and a natural default.

@david_arteta, no direct way to get at these. One easy thing to try would be to create your own formula column using the Passing-Bablok intercept and slope estimates. More ambitious would be to parse these values out from the appropriate table box using JSL. The ultimate would be adding the red triangle as you indicate. I'm not planning to tackle this any time soon so if anyone is willing to give it a go I'd be happy to incorporate updates into the add-in. Alternatively, it may be a good time to be voting for the formal addition of Passing-Bablok to the JMP Bivariate platform.

@Hanz, you need to transpose your data to have columns Donor, Method, Analyte1, Analyte2, then analyze Analyte1 and Analyte2 separately as the Y variable.

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