I'm trying to interpret some results here, and just want to make sure that my logic is sound.
I'm predicting a binary outcome with a categorical predictor (gene level coded as 0, 1, or 2 dependant on the number of risk alleles present). My hypothesis is that the gene's effect on the outcome is because of its effect on another variable (continous), say blood glucose level, which in turn affects CAD.
When I model the response as a function of the gene, all is fine, and it predicts very well (this is an established loci).
glm(cad ~ gene, Mastersheet, family = binomial) %>% summary() Estimate Std. Error z value Pr(>|z|) (Intercept) 0.002729 0.041267 0.066 0.947 gene 0.354027 0.032885 10.766 <2e-16 ***
When I include my covariate in the model, all is still fine. The covariate is also an established predictor of cad.
glm(cad ~ gene+glucose, Mastersheet, family = binomial) %>% summary() Estimate Std. Error z value Pr(>|z|) (Intercept) 2.66501 0.10820 24.630 <2e-16 *** gene 0.33467 0.03813 8.778 <2e-16 *** glucose -2.17507 0.07722 -28.168 <2e-16 ***
However, when an interaction term is included, the gene is no longer significant, though the model continues to be.
glm(formula = cad ~ gene * glucose, family = binomial, data = Mastersheet) Estimate Std. Error z value Pr(>|z|) (Intercept) 3.2845 0.1920 17.109 < 2e-16 *** gene -0.2306 0.1449 -1.591 0.112 glucose -2.6674 0.1482 -18.004 < 2e-16 *** gene:glucose 0.4471 0.1108 4.035 5.47e-05 ***
I'm looking for some help interpreting this meaningfully. Does this mean that because the gene becomes insignificant when the interaction is factored in, the effect of the gene on CAD is mediated entirely by it's interaction with glucose?
I couldn't find any other questions like this, sorry if it is a repeat.
Any and all help is appreciated! Thank you for your time!