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:wave: new lifelines release: 0.22.0. Some important API changes to take a look at, but some really powerful new regression models: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.0

lifelines has focused less on purely predictive models, and more on inference

Hi everyone, I'm trying to fit a model onto a recurrent process. I.E: Patient returns to a doctor. Is there a way to do so using lifelines ? So far the closest that I've got was this repo: https://github.com/dunan/MultiVariatePointProcess

Unfortunately

@CamDavidsonPilon Let me know if you have any suggestions for question below:

Hi, I am using CoxPHFitter with IPS weights and

`robust=True`

flag. 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.

:wave: minor version of lifelines released: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.1

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.

Hi! I am currently trying to create mixed cure models using the lifelines fitter. I saw that there is an example code in the GitHub under experiments. I was going to use this as a starting point and then adjust accordingly but I am getting an error when I run that code saying: "AttributeError: 'CureModel' object has no attribute '_primary_parameter_name'

I don't have a full understanding of the input arguments for _cumulative_hazard so I am not sure what is causing this error. Thank you!

I don't have a full understanding of the input arguments for _cumulative_hazard so I am not sure what is causing this error. Thank you!

If not, try upgrading. Otherwise, if you are still getting the error, can you post the entire stack trace?

in reference to the subclass my computer doesn't recognize ParametricRegressionFitter as an option but it does recognize ParametericRegressionFitter - perhaps also because of the version?

Hi folks, does anyone have experience explaining concordance index to a nontechnical audience (like execs), or even devising an alternative method of presenting model accuracy? I don't think describing the model's predictions in terms of ordered pairs is likely to be of interest - they just want to know how accurate the model is in terms of customer retention/LTV.

@blissfulchar_twitter personally I like using the survival probabilities to calculate the CLV assuming contractual settings (e.g. Berry & Linof, 2004). I don't like it for a non-technical audience. I normally I try to link survival to CLV for execs. Anyone correct me if I am wrong but concordance is "global" index for validating the predictive ability of a survival model, representing how well the variables allow to predict the survival, e.g. observations with higher survival time has the higher probability of survival predicted by your model.

@blissfulchar_twitter we used the survival probabilities under each curve (cohort) and the monthly payment to calculate CLV. We didn’t used individual customer but customers grouped in the survival curves. This option as some limitations but gives us an idea for an estimated CLV. What you say should be very interesting. I think there are other approaches to calculate the predictions of individual CLV.

:wave: minor lifelines release. Important thing is that scipy 1.3 can be used with it now: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.2

Hi, what is the best way to retrieve log likelihood of a fit? it is shown via 'model.print_summary()' but not via 'model.summary', which only shows a summary of the parameters.

I managed to get it via model._log_likelihood, but had to look into the source code for that.

Thanks and kudos for the library!

I managed to get it via model._log_likelihood, but had to look into the source code for that.

Thanks and kudos for the library!

Hi, sorry I was wondering if any maintainers/users of lifelines based in Europe would like to do tutorial/workshop or talk about this package in our conference Python in Pharma (PyPharma) in Basel? Apologies for the unrequested advertisement, I will delete it if this is a problem. The conference would be free to attend (under invites) and 100% volunteer run. It will take place in November 21-22 and our target is 100-150 attendees. We would really be happy if lifelines is represented at this event.

^ no need to apologize, this message welcome here. I would love to join, hopefully someone can take my place. There were a few Euro speakers of lifelines already: Linda Uruchurtu , Lorna Brightmore and Elena Sharova have all recently (past few years) given talks on lifelines. You can search for their videoes online.

Unrelated: :wave: new minor (but important) version of lifelines released: https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.22.3

@julianspaeth the only issue with pysurvival is the support for that isnt good as compared to lifelines.