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- Concordance analysis with continous variable

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May 7, 2017 5:43 AM
(1786 views)

Hello,

I am trying to solve the following statistical problem in jmp:

1. I got a gold standard fixed variable y (for example: y = 100)

2. I got three data sets x1,x2,x3: (for example x1=92;94;99 / x2=92;94;99;101;103 / x3=92;94;99;101;103;107;100;99;100)

please note: the data sets have similar data, but the number of data points are different. all variables are continuous.

I am looking for with a statistical test or function, to evaluate which dataset x1, x2 or x3 has the closest mean AND the lowest SD to match the gold standard y ? - please note: the gold standard y has no sd, just a fixed value. Looking at ther samples for concordance analysis I just could do this was nominal variables; mine are ordinal or continous.

Any advice much appreciated. Many thanks, Marc

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I understand your use of the word 'concordance' here but that word is also a statistical term that refers to measures of *agreement* among *categorical variables*.

I recommend using confidence interval estimates of the mean. The 'gold standard' is your *null hypothesis* H0: mean = 100) and therefore your alternative hypothesis is that the opposite (H1: mean not = 100). You compare the intervals to the gold standard and to each other using Analyze > Fit Y by X (Oneway platform). Here is your example in a data table laid out for Oneway:

\\

Here is the Oneway platform with a reference line for the gold standard and the results for ANOVA:

Now you compare the confidence interval for each sample. You can plot them in Graph Builder, too. Right-click on the Means for Oneway Anova table and select Make Into Data Table. It might look like this:

Learn it once, use it forever!

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Many thanks @markbailey !

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I understand your use of the word 'concordance' here but that word is also a statistical term that refers to measures of *agreement* among *categorical variables*.

I recommend using confidence interval estimates of the mean. The 'gold standard' is your *null hypothesis* H0: mean = 100) and therefore your alternative hypothesis is that the opposite (H1: mean not = 100). You compare the intervals to the gold standard and to each other using Analyze > Fit Y by X (Oneway platform). Here is your example in a data table laid out for Oneway:

\\

Here is the Oneway platform with a reference line for the gold standard and the results for ANOVA:

Now you compare the confidence interval for each sample. You can plot them in Graph Builder, too. Right-click on the Means for Oneway Anova table and select Make Into Data Table. It might look like this:

Learn it once, use it forever!

- Mark as New
- Bookmark
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Many thanks @markbailey !

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You need to change the "Accepted Solution" from your response to Mark Bailey's response. The marking of an "Accepted Solution" is not intended to just indicate that a solution to a Discussion Question has been found, but it need to point to the response that has the actual solution

Jim