# Dummy variable trap in survival models

I am familiar with the dummy variable trap in normal OLS, in which we should include one less dummy variable than the total of categories to avoid the problem of multicollinearity.

However, I was wondering if it is also the case in duration models. Specifically, I am running a Weibull survival model with six dummy variables, exhaustive and mutually exclusive, and there is no sign of multicollinearity (Even if all the coefficients of the dummy variables are negative, which makes it a bit harder to interpret as I'm not sure what the dummies are measuring themselves against).

So the question is: does the dummy variable trap also occur in survival/duration models?

• Should be the same as in OLS... – user73432 Apr 13 '15 at 12:47
• Are you also fitting an intercept? – Scortchi - Reinstate Monica Apr 13 '15 at 15:03
• Yes, there is also an intercept. Also the AIC is (slightly) higher when I exclude one of the dummy variables, which implies that the model fit is better with all the dummies. – Not Fisher Apr 13 '15 at 15:58
• How are you determining that you don't have perfect collinearity in the design matrix? – Scortchi - Reinstate Monica Apr 13 '15 at 16:06
• Very trusting of you! Check the condition number of $X^\mathrm{T}X$ (where $X$ is the design matrix). Or try changing the order in which coefficients are input to the model (I've just experimented with the survfit function in R - fitting a Weibull model when the design matrix isn't full rank also gives one of the infinity of possible solutions without warning; it gives different solutions when you vary the order of the predictors). – Scortchi - Reinstate Monica Apr 13 '15 at 16:39