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?
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). $\endgroup$