cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
Try the Materials Informatics Toolkit, which is designed to easily handle SMILES data. This and other helpful add-ins are available in the JMP® Marketplace
Choose Language Hide Translation Bar
KF2
KF2
Level I

How to correlation diagram with motor severity score Y using a graph builder by adjusting biomarker X according to age and gender

We would like to clarify whether there is a linear relationship between biomarker X (DATQUANT: dependent variable) and motor severity score Y (MDS-UPDRS-3: explanatory variable). Since age and biological sex influence the biomarker X, we considered them as covariates. We would like to conduct multiple regression.

[X (DATQUANT: dependent variable)] = a1*Y (MDS-UPDRS-3: explanatory variable) + a2*[age] + a3 + residuals

a1, a2 : slope coefficients for each explanatory variable

a3: constant term for biological sex

Now we would like to visualize the relationship between [age and sex adjusted (DATQUANT: dependent variable)] and Y (MDS-UPDRS-3: explanatory variable) So we would like to prepare scatter plot adjusted X (a1*[X(DATQUANT: continusous variable)] - a2*[age] - a3) vs. Y (MDS-UPDRS-3: dependent variable) We would appreciate it very much if you could give us comments and suggestions. Many thanks in advance.

1 REPLY 1
Phil_Kay
Staff

Re: How to correlation diagram with motor severity score Y using a graph builder by adjusting biomarker X according to age and gender

Hi,

I attach a version of your data table with models.

The full model includes all variables. From the prediction profiler you can see the effect of your explanatory variable and the covariates. Sex is not a big effect.

 

Phil_Kay_0-1634140356248.png

 

The next model has only sex and age. From this model we save the residuals: Red triangle menu > Save Columns > Residuals. 

Then we take the intercept from this model and add it to the residuals to create a new column, which is the dependent variable corrected for the effect of age and sex.

Finally we plot this corrected dependent variable versus the explanatory variable:

 

Phil_Kay_1-1634140992142.png

 

I hope this helps.

Phil