# 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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If you want to test whether `exp(X1)` is statistically different from `exp(X2)` then you do not need to perform any statistical tests in this case.

Just observe the confidence intervals. In general, we cannot infer that point estimates are statistically different when the confidence intervals overlap, but this is a special case…..

The confidence interval for `exp(X1)` is (1.01, 1.68) while for `exp(X2)` it is (0.92, 4.21)

The confidence interval for `exp(X1)` is completely contained within the confidence interval for `exp(X2)`

Therefore they are not statistically different.

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