I would like to interpret interactions and their confidence intervals in a logit model.
My model looks like:
model.3 <- glm(NRSsuff ~ Gender + NRS0 + Meds + Gender:Meds, family=binomial(link="logit"), data=dataset.model)
Estimate Std. Error z value Pr(>|z|) (Intercept) 0.742068 1.036340 0.716 0.473962 Gender -0.168875 0.439052 -0.385 0.700508 NRS05 0.158400 0.289230 0.548 0.583924 NRS06 0.551517 0.322361 1.711 0.087105 . NRS07 -0.694288 0.340947 -2.036 0.041715 * Meds 0.863118 0.376975 2.290 0.022045 * Gender:Meds 0.946943 0.465266 2.035 0.041823 *
The response (ref level: NRS=0), Gender (ref level: Gender=F), and Meds (ref level: Meds=0) are all binary.
The OR for a patient when Gender=F and Meds=0 is then exp(coeff.intercept).
The OR for a patient when Gender=M and Meds=0 includes the intercept. The OR for Gender=M,Meds=0 is then exp(coeff.intercept + coeff.Gender).
But what about for the interaction? I would like the OR for patients treated by males and given meds compared with when patients are treated by females and given meds. I've then:
exp(coeff.Gender + coeff.interaction)
Is this correct? Why is the intercept coefficient not included?
Some information in a previous post seems to be conflicting (Interpreting interaction terms in logit regression with categorical variables) The top answer refers changes in the baseline, while a link out to "COMMUNICATING COMPLEX INFORMATION: THE INTERPRETATION OF STATISTICAL INTERACTION IN MULTIPLE LOGISTIC REGRESSION ANALYSIS" J. J. Chen, 2003 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447969/) does not talk about the intercept at all.