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##### Activity
githubhsss
@githubhsss
@CamDavidsonPilon Thanks a lot!
Cameron Davidson-Pilon
@CamDavidsonPilon
@fuyb1992 you can do something like this:
from lifelines.utils import median_survival_times

median_survival_times(self.confidence_interval_survival_function_)
Cameron Davidson-Pilon
@CamDavidsonPilon
(though it is pretty efficient, just not most efficient)
This actually isn't the most efficient way to compute the confidence intervals, but I think I'll expose a better way in the future
Cameron Davidson-Pilon
@CamDavidsonPilon
efficiency in the statistical sense, not performance
fuyb1992
@fuyb1992
@CamDavidsonPilon Thank you for your answer!!! I tried your answer, it only works for data with S(t)<=0.5 and return days interval, but for data with S(t)>0.5 return None .
fuyb1992
@fuyb1992
@CamDavidsonPilon I'am new to survival analysis, excuse me please if I'm wrong. I'm confused after reading wiki and papers about the confidence interval of survival function for parameter models, it would be a great help if you can give some references or documents about that!! Thanks a lot!
Cameron Davidson-Pilon
@CamDavidsonPilon
@fuyb1992 you can do something like this:
from lifelines.utils import median_survival_times
median_survival_times(self.confidence_interval_survival_function_)
fuyb1992
@fuyb1992
@CamDavidsonPilon Thank you for your answer!!! I tried your answer, it only works for data with S(t)<=0.5 and return days interval, but for data with S(t)>0.5 return None .
'''
Cameron Davidson-Pilon
@CamDavidsonPilon
efficient as in "statistical efficiency", not peformance
githubhsss
@githubhsss
@CamDavidsonPilon
Thanks for sharing the thesis again~
I'm dealing with some repeated events data(machine failure time data). Since a machine may have several failures and different machines have different number of failures, I think it's necessary to consider about repeated events and heterogeneity. Will frailty models help? Or any other advice? (^_^)/
Cameron Davidson-Pilon
@CamDavidsonPilon
Yuck, Gitter is being messy and posting my edited messages much later than originally posted. sorry sorry
@fuyb1992 ah yes, you may want to keep your if self.median_ != np.inf check
@githubhsss frailty, is one solution, though it's not in lifelines (but is in R's survival). Another option is to use cluster_col is CoxPHFitter: https://lifelines.readthedocs.io/en/latest/Examples.html#correlations-between-subjects-in-a-cox-model. Another solution is to strata-ify per machine in the CoxPHFitter.
fuyb1992
@fuyb1992
fuyb1992
@fuyb1992
Thanks a lot! I'm trying to understand the confidence interval of survival function for parameter models, the Taylor expansions method is mentioned a lot , and the Jacobian-vector product is used in lifelines code. I'm confused with the relationship between them, it would be a great help if you could give some references or documents about the implementation method. Thank you for your time!!
Cameron Davidson-Pilon
@CamDavidsonPilon
I'd be happy to, as it is something I'm really excited about. Let me type something up tomorrow
fuyb1992
@fuyb1992
Thank you so much, I'm looking forward it!!
Cameron Davidson-Pilon
@CamDavidsonPilon
let me know if you have questions about it
:wave: A minor release, 0.20.4, is available. Bug fixes, improvements to large datasets in AFT, and left-truncation in AFT models.
https://github.com/CamDavidsonPilon/lifelines/releases/tag/v0.20.4
Cameron Davidson-Pilon
@CamDavidsonPilon
Let me know if you are having install problems, please - w.r.t. to the 0.20.4 release
Also, I'm working on a new survival regression model. The original motivation was the predictable behaviour of SaaS companies customer churn, but it's generally a very flexible model. Have a look here if interested and I'm looking for feedback on it: https://nbviewer.jupyter.org/gist/CamDavidsonPilon/ce93dc24947c45b034402edc657aa6eb
fuyb1992
@fuyb1992
Thank you very much for your answer, which explains the delta method on parameter models clearly!!
githubhsss
@githubhsss
@CamDavidsonPilon Thanks~
githubhsss
@githubhsss
@CamDavidsonPilon
I have been reading your recommended thesis. It helps a lot. Though still lots of questions...
I tried Cox and WeibullAFT, but the concordance was only 0.53. Does this mean that the models fit unacceptably? What is the reference of the range of 0.55-0.7? In addition to concordance, can I directly compare log likelihood? Have no idea about goodness of fit and model selection...
Cameron Davidson-Pilon
@CamDavidsonPilon

Disappointingly, 0.53 is a bit on the low end. Have you tried a LogNormalAFT - it can fit some models better.

What is the reference of the range of 0.55-0.7?

I think I saw it in Frank H. work, maybe his blog?

You can't compare CoxPH and WeibullAFT log likelihood values, no. Mostly because the CoxPH is a partial likelihood.

I recently added some resources here to help with model selection between CoxPH and parametric models: https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html#parametric-vs-semi-parametric-models
It's also very possible you are missing interactions or non-linear effects in your models.
githubhsss
@githubhsss
@CamDavidsonPilon Thanks for your answer~ I got to keep working on it...
Manon Wientjes
@manonww
Hi @CamDavidsonPilon How do you ensure that lambda and rho are greater than 0 if you fit a weibull distribution using the WeibullFitter? You do not set the bounds as in the LogNormalFitter?
Cameron Davidson-Pilon
@CamDavidsonPilon
@manonww good observation. The bounds, when not specified, are set to be always positive
hgfabc
@hgfabc
hi, I'm quite new to using lifelines and I stumble upon errors while executing. I was wondering when using the cph.fit() method, does it omit the missing values/Nan ? Or do I have to reform the dataframe? thanks
Manon Wientjes
@manonww
@CamDavidsonPilon Thanks! I was also wondering why the Weibull distribution is not defined at 0. According to Wikipedia it is defined? https://en.m.wikipedia.org/wiki/Weibull_distribution
Cameron Davidson-Pilon
@CamDavidsonPilon
@manonww it is defined at 0, but the probability of an event there is nil (hence why we reject any 0 durations - probably it's malformed data). Is there a place in lifelines where the weibull if not defined at 0? (Maybe my docs?)
@hgfabc welcome! It does not omit or drop NaNs, that up to you to handle first
hgfabc
@hgfabc
@CamDavidsonPilon so if my original data frame contains Nans in it, it doesn’t raise errors and would proceed with it?
Cameron Davidson-Pilon
@CamDavidsonPilon
It will raise an error, which you must fix @hgfabc
hgfabc
@hgfabc
My mistake, I didn’t read through. Thank you!@CamDavidsonPilon
Typo sorry @CamDavidsonPilon
Manon Wientjes
@manonww
@CamDavidsonPilon No, sorry I didn't read the error message properly. I have another question :). To determine rho and lambda of a Weibull distribution, you use scipy optimize minimize with the L-BFGS-B method?
Cameron Davidson-Pilon
@CamDavidsonPilon
@manonww yup that is correct!
Cameron Davidson-Pilon
@CamDavidsonPilon
:wave: minor release alert! Update to 0.20.5 for some bug fixes. Changelog here
Also, here's how I'm thinking about including interval censoring for a future 0.21 release: CamDavidsonPilon/lifelines#700
Dan Turkel
b) in the rossi dataset example provided, plotting the KM curve against the baseline hazards appears not to have a good spread. is there an included dataset that could be used for this example that would show a bigger spread, like the one in the goodfit picture?
kmf = lifelines.KaplanMeierFitter()
ax.plot(kmf.survival_function_,color='r')