I am performing a metaanalysis where I am trying to find predictors for an ordinal response variable. Additionally, I want to perform pair-wise correlations on some of the variables. I have a confounding categorical variable that I want to remove.
In my data, there are 4 levels of variable
RV, and 40 levels of the confounder
CON1. The data looks like this, except I have thousands of cases.
VARNAME RV CON1 IV1 IV2 IV3 IV4 VARTYPE Ordinal Categorical Categorical Categorical Continuous Continuous CASE1 1 1 M R 49 476.23 CASE2 1 2 F S 31 465.11 CASE3 2 2 M R 37 411.20 CASE4 3 7 M X 41 407.33
I don't think I can adjust means because the response variable is ordinal, so no linear regression.
Proportional odds logistic regression might be a good choice, but I don't think the output will produce "standardized" variables, that I can perform the pairwise correlations on.
How do I remove the effect of the confounder variable?