date. So the cumulative hazard for each time would be the integral of the hazard from the "start_date" to the "end_date". (where these can be derived from an element of
timeand its corresponding date.) What I really care about is the cumulative incidence function (CIF) for each kind of event. If the idea of getting
_cumulative_hazardfunction works, then I was hoping to use this technique to model the CIF for one of the competing event types.
Your explanation of
_cumulative_hazard is correct. But you can also see it as simply the cumulative hazard you wish to implement (i.e., not necessary to think about "durations" or "unknowns")
I was thinking about your seasonal model, and actually tried to code something up, but there is a problem I think. The
_cumulative_hazard is invoked for both the censored and uncensored data, so your code needs to handle that (and you won't know which until you see the shapes of the input data)
Clock-dependent hazards I think are actually pretty common
Agree, but I feel like the common strategy is to use a regression model or fit N univariate models (i.e. partition the data)
I think a seasonal model is a great idea, so I want this to work.
@CamDavidsonPilon Let me know if you have any suggestions for question below:
Hi, I am using CoxPHFitter with IPS weights and
robust=Trueflag. However, the fit is taking really long time to finish. I have about million instances and 6 features in my dataset. Let me know if slower runtime is expected in weighted version and what can be done to speed it up.
Hi all. I've somewhat new to using lifelines, and in using the CoxPHFitter, when I run
check_assumptions, I end up with an error that reads as follows:
/RuntimeWarning: overflow encountered in exp scores = weights * np.exp(np.dot(X, self.params_))
Any suggestions on dealing with this issue? I'm starting down the road of normalization, but I'm not sure if that's 100% correct.