Solved – Comparison of two odds ratios: Take 2

I would like to test the difference of two odds ratios given the following R-output:

f=with(data=imp, glm(Y~X1+X2, family=binomial(link="logit"))) s01=summary(pool(f1)) s01 

                    est        se         t       df   Pr(>|t|)     (Intercept) -1.7805826 0.1857663 -9.585070 391.0135 0.00000000    X1           0.2662796 0.1308970  2.034268 390.4602 0.04259997     X2           0.6757952 0.3869652  1.746398 395.6098 0.08151794  

cbind(exp(s01[, c("est", "lo 95", "hi 95")]), pval=s01[, "Pr(>|t|)"]) 

                            est     lo 95     hi 95       pval              (Intercept) 0.1685399 0.1169734 0.2428389 0.00000000              X1          1.3051000 1.0089684 1.6881459 0.04259997              X2          1.9655955 0.9185398 4.2062035 0.08151794 

To do so, I would need to take the difference of the log odds and obtain the standard error (outlined here: Statistical test for difference between two odds ratios?).

One of the predictor variables is continuous and I am not sure how I could compute the values required for $SE(logOR)$.

Could someone please explain whether the output I have is conducive to this method?

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