I am trying to determine to what extent the age ( continuous variable) affect the outcome of the patient ( outcome - dead/recovered). So, I tried to build a model in R using glm(family = "binomial"). There are many other predictors, but my concern is about the variable "age" or how it is named in the output below "vecums". It's continuous variable, and as I understand the glm function, when using binomial regression, outputs log odds, so for variable age - the coefficient is equal to exp(2.309e-01) = 1.26.
> summary(fullModel) Call: glm(formula = Izn ~ ., family = "binomial", data = myData14) Deviance Residuals: Min 1Q Median 3Q Max -2.00942 -0.20697 -0.01299 0.00155 2.62596 Coefficients: (1 not defined because of singularities) Estimate Std. Error z value Pr(>|z|) (Intercept) -1.868e+01 4.508e+00 -4.144 3.41e-05 *** DZ 9.712e-01 7.510e-01 1.293 0.195917 Vecums 2.309e-01 6.039e-02 3.824 0.000131 ***
So, if reference level for the response variable is recovered, then there are 1.26 people aged y+1 that have died per every y aged person. Somebody told me that that's not a correct way to interpret the coefficient, because the age variable is not linearly related to the outcome. So, I tried to cut data into several age groups, but in that case the coefficients were not significant, most probably due to the data size (300 patients). How precise is the coefficient 1.26 in case, I am not cutting age into groups? How to correctly interpret it?