0.19.0
New features
- New regression model
WeibullAFTFitterfor fitting accelerated failure time models. Docs have been added to our documentation about how to useWeibullAFTFitter(spoiler: it's API is similar to the other regression models) and how to interpret the output. CoxPHFitterperformance improvements (about 10%)CoxTimeVaryingFitterperformance improvements (about 10%)
API changes
- Important: we changed the
.hazards_and.standard_errors_on Cox models to be pandas Series (instead of Dataframes). This felt like a more natural representation of them. You may need to update your code to reflect this. See notes here: #636 - Important: we changed the
.confidence_intervals_on Cox models to be transposed. This felt like a more natural representation of them. You may need to update your code to reflect this. See notes here: #636 - Important: we changed the parameterization of the
WeibullFitterandExponentialFitterfrom\lambda * ttot / \lambda. This was for a few reasons: 1) it is a more common parameterization in literature, 2) it helps in convergence. - Important: in models where we add an intercept (currently only
AalenAdditiveModel), the name of the added column has been changed frombaselineto_intercept - Important: the meaning of
alphain all fitters has changed to be the standard interpretation of alpha in confidence intervals. That means that the default for alpha is set to 0.05 in the latest lifelines, instead of 0.95 in previous versions.
Bug Fixes
- Fixed a bug in the
_log_likelihood_property ofParametericUnivariateFittermodels. It was showing the "average" log-likelihood (i.e. scaled by 1/n) instead of the total. It now displays the total. - In model
print_summarys, correct a label erroring. Instead of "Likelihood test", it should have read "Log-likelihood test". - Fixed a bug that was too frequently rejecting the dtype of
eventcolumns. - Fixed a calculation bug in the concordance index for stratified Cox models. Thanks @airanmehr!
- Fixed some Pandas <0.24 bugs.