Survival Regression

Often we have additional data aside from the duration, and if applicable any censorships that occurred. In the regime dataset, we have the type of government the political leader was part of, the country they were head of, and the year they were elected. Can we use this data in survival analysis?

Yes, the technique is called survival regression – the name implies we regress covariates (e.g., age, country, etc.) against another variable – in this case durations and lifetimes. Similar to the logic in the first part of this tutorial, we cannot use traditional methods like linear regression.

There are two popular competing techniques in survival regression: Cox’s model and Aalen’s additive model. Both models attempt to represent the hazard rate \(\lambda(t | x)\) as a function of \(t\) and some covariates \(x\). We explore these models next.

Cox’s proportional hazard model

Lifelines has an implementation of the Cox propotional hazards regression model (implemented in R as coxph). The idea behind the model is that the log-hazard of an individual is a linear function of their static covariates and a population-level baseline hazard that changes over time. Mathematically:

\[\underbrace{\lambda(t | x)}_{\text{hazard}} = \overbrace{b_0(t)}^{\text{baseline hazard}} \underbrace{\exp \overbrace{\left(\sum_{i=1}^n b_i (x_i - \overline{x_i})\right)}^{\text{log-partial hazard}}}_ {\text{partial hazard}}\]

Note a few facts about this model: the only time component is in the baseline hazard, \(b_0(t)\). In the above product, the partial hazard is a time-invariant scalar factor that only increases or decreases the baseline hazard. Thus a changes in covariates will only increase or decrease the baseline hazard.

The dataset for regression

The dataset required for survival regression must be in the format of a Pandas DataFrame. Each row of the DataFrame should be an observation. There should be a column denoting the durations of the observations. Optionally, there could be a column denoting the event status of each observation (1 if event occured, 0 is censored). There are also the additional covariates you wish to regress against. Optionally, there could be columns in the DataFrame that are used for stratification, weights, and clusters which will be discussed later in this tutorial.

An example data is from the paper here, available in lifelines as datasets.load_rossi.

from lifelines.datasets import load_rossi

rossi = load_rossi()

     week  arrest  fin  age  race  wexp  mar  paro  prio
0      20       1    0   27     1     0    0     1     3
1      17       1    0   18     1     0    0     1     8
2      25       1    0   19     0     1    0     1    13
3      52       0    1   23     1     1    1     1     1

The dataframe rossi contains 432 observations. The week column is the duration, the arrest column is the event occured, and the other columns represent variables we wish to regress against.

If you need to first “clean” your dataset (encode categorical variables, add interation terms, etc.), that should happen before using lifelines. Libraries like Pandas and Patsy help with that.

Running the regression

The implementation of the Cox model in lifelines is called CoxPHFitter. Like R, it has a print_summary function that prints a tabular view of coefficients and related stats.

from lifelines import CoxPHFitter
from lifelines.datasets import load_rossi

rossi_dataset = load_rossi()

cph = CoxPHFitter(), duration_col='week', event_col='arrest', show_progress=True)

cph.print_summary()  # access the results using cph.summary

      duration col = week
         event col = arrest
number of subjects = 432
  number of events = 114
    log-likelihood = -658.748
  time fit was run = 2018-10-22 20:47:44 UTC

        coef  exp(coef)  se(coef)       z      p  lower 0.95  upper 0.95
fin  -0.3794     0.6843    0.1914 -1.9826 0.0474     -0.7545     -0.0043   *
age  -0.0574     0.9442    0.0220 -2.6109 0.0090     -0.1006     -0.0143  **
race  0.3139     1.3688    0.3080  1.0192 0.3081     -0.2898      0.9176
wexp -0.1498     0.8609    0.2122 -0.7058 0.4803     -0.5657      0.2662
mar  -0.4337     0.6481    0.3819 -1.1358 0.2561     -1.1821      0.3147
paro -0.0849     0.9186    0.1958 -0.4336 0.6646     -0.4685      0.2988
prio  0.0915     1.0958    0.0286  3.1939 0.0014      0.0353      0.1476  **
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Concordance = 0.640
Likelihood ratio test = 33.266 on 7 df, p=0.00002

To access the coefficients and the baseline hazard directly, you can use cph.hazards_ and cph.baseline_hazard_ respectively.


Fitting the Cox model to the data involves using gradient descent. Lifelines takes extra effort to help with convergence, so please be attentive to any warnings that appear. Fixing any warnings will generally help convergence. If you wish to see the fitting, there is a show_progress parameter in function. For further help, see Problems with convergence in the Cox Proportional Hazard Model.

After fitting, the value of the maximum log-likelihood this available using cph._log_likelihood. Similarly, the score and Hessian matrix are available under _score_ and _hessian_ respectively.

Goodness of fit and prediction

After fitting, you may want to know how “good” of a fit your model was to the data. Aside from traditional approaches, two methods the author has found useful is to 1. look at the concordance-index (see below section on Model Selection in Survival Regression), available as cph.score_ or in the print_summary and 2. compare spread between the baseline survival function vs the Kaplan Meier survival function (Why? Interpret the spread as how much “variance” is provided by the baseline hazard versus the partial hazard. The baseline hazard is approximately equal to the Kaplan-Meier curve if none of the variance is explained by the covariates / partial hazard. Deviations from this provide a visual measure of variance explained). For example, the first figure below is a good fit, and the second figure is a much weaker fit.

_images/goodfit.png _images/badfit.png

After fitting, you can use use the suite of prediction methods: .predict_partial_hazard, .predict_survival_function, etc.

X = rossi_dataset.drop(["week", "arrest"], axis=1)


cph.predict_survival_function(X, times=[5., 25., 50.])


Plotting the coefficients

With a fitted model, an alternative way to view the coefficients and their ranges is to use the plot method.

from lifelines.datasets import load_rossi
from lifelines import CoxPHFitter

rossi_dataset = load_rossi()
cph = CoxPHFitter(), duration_col='week', event_col='arrest', show_progress=True)


Plotting the effect of varying a covariate

After fitting, we can plot what the survival curves look like as we vary a single covarite while holding everything else equal. This is useful to understand the impact of a covariate, given the model. To do this, we use the plot_covariate_groups method and give it the covariate of interest, and the values to display.

from lifelines.datasets import load_rossi
from lifelines import CoxPHFitter

rossi_dataset = load_rossi()
cph = CoxPHFitter(), duration_col='week', event_col='arrest', show_progress=True)

cph.plot_covariate_groups('prio', [0, 5, 10, 15])

Checking the proportional hazards assumption

CoxPHFitter has a check_assumptions method that will output violations of the proportional hazard assumption. For a tutorial on how to fix violations, see Testing the Proportional Hazard Assumptions.

Non-proportional hazards is a case of model misspecification. Suggestions are to look for ways to stratify a column (see docs below), or use a time-varying model (see docs much further below).


Sometimes one or more covariates may not obey the proportional hazard assumption. In this case, we can allow the covariate(s) to still be including in the model without estimating its effect. This is called stratification. At a high level, think of it as splitting the dataset into N datasets, defined by the covariate(s). Each dataset has its own baseline hazard (the non-parametric part of the model), but they all share the regression parameters (the parametric part of the model). Since covariates are the same within each dataset, there is no regression parameter for the covariates stratified on, hence they will not show up in the output. However there will be N baseline hazards under baseline_cumulative_hazard_.

To specify categorical variables to be used in stratification, we define them in the call to fit:

from lifelines.datasets import load_rossi
from lifelines import CoxPHFitter

rossi_dataset = load_rossi()
cph = CoxPHFitter(), 'week', event_col='arrest', strata=['race'], show_progress=True)

cph.print_summary()  # access the results using cph.summary

      duration col = week
         event col = arrest
            strata = ['race']
number of subjects = 432
  number of events = 114
    log-likelihood = -620.564
  time fit was run = 2018-10-23 02:45:52 UTC

        coef  exp(coef)  se(coef)       z      p  lower 0.95  upper 0.95
fin  -0.3788     0.6847    0.1913 -1.9799 0.0477     -0.7537     -0.0038   *
age  -0.0576     0.9440    0.0220 -2.6198 0.0088     -0.1008     -0.0145  **
wexp -0.1428     0.8670    0.2128 -0.6708 0.5023     -0.5598      0.2743
mar  -0.4388     0.6448    0.3821 -1.1484 0.2508     -1.1878      0.3101
paro -0.0858     0.9178    0.1958 -0.4380 0.6614     -0.4695      0.2980
prio  0.0922     1.0966    0.0287  3.2102 0.0013      0.0359      0.1485  **
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Concordance = 0.638
Likelihood ratio test = 109.634 on 6 df, p=0.00000

# (49, 2)

Weights & Robust Errors

Observations can come with weights, as well. These weights may be integer values representing some commonly occuring observation, or they may be float values representing some sampling weights (ex: inverse probability weights). In the method, an kwarg is present for specifying which column in the dataframe should be used as weights, ex: CoxPHFitter(df, 'T', 'E', weights_col='weights').

When using sampling weights, it’s correct to also change the standard error calculations. That is done by turning on the robust flag in fit. Interally, CoxPHFitter will use the sandwhich estimator to compute the errors.

from lifelines import CoxPHFitter

df = pd.DataFrame({
    'T': [5, 3, 9, 8, 7, 4, 4, 3, 2, 5, 6, 7],
    'E': [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0],
    'weights': [1.1, 0.5, 2.0, 1.6, 1.2, 4.3, 1.4, 4.5, 3.0, 3.2, 0.4, 6.2],
    'month': [10, 3, 9, 8, 7, 4, 4, 3, 2, 5, 6, 7],
    'age': [4, 3, 9, 8, 7, 4, 4, 3, 2, 5, 6, 7],

cph = CoxPHFitter(), 'T', 'E', weights_col='weights', robust=True)

See more examples in Adding weights to observations in a Cox model.

Clusters & Correlations

Another property your dataset may have is groups of related subjects. This could be caused by:

  • a single individual having multiple occurrences, and hence showing up in the dataset more than once.
  • subjects that share some common property, like members of the same family or being matched on prospensity scores.

We call these grouped subjects “clusters”, and assume they are designated by some column in the dataframe (example below). The point estimates of the model don’t change, but the standard errors will increase (in fact, internally this uses the sandwich estimator). An intuitive argument for this is that 100 observations on 100 individuals provide more information than 100 observations on 10 individuals (or clusters).

from lifelines import CoxPHFitter

df = pd.DataFrame({
    'T': [5, 3, 9, 8, 7, 4, 4, 3, 2, 5, 6, 7],
    'E': [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0],
    'month': [10, 3, 9, 8, 7, 4, 4, 3, 2, 5, 6, 7],
    'age': [4, 3, 9, 8, 7, 4, 4, 3, 2, 5, 6, 7],
    'id': [1, 1, 1, 1, 2, 3, 3, 4, 4, 5, 6, 7]

cph = CoxPHFitter(), 'T', 'E', cluster_col='id')

For more examples, see Correlations between subjects in a Cox model.


After fitting a Cox model, we can look back and compute important model residuals. These residuals can tell us about non-linearities not captured, violations of proportional hazards, and help us answer other useful modelling questions. See Assessing Cox model fit using residuals.

Aalen’s Additive model


This implementation is still experimental.

The estimator to fit unknown coefficients in Aalen’s additive model is located in estimators under AalenAdditiveFitter. For this exercise, we will use the regime dataset and include the categorical variables un_continent_name (eg: Asia, North America,…), the regime type (e.g., monarchy, civilian,…) and the year the regime started in, start_year.

Aalen’s additive model typically does not estimate the individual \(b_i(t)\) but instead estimates \(\int_0^t b_i(s) \; ds\) (similar to the estimate of the hazard rate using NelsonAalenFitter above). This is important to keep in mind when analyzing the output.

from lifelines import AalenAdditiveFitter
from lifelines.datasets import load_dd

data = load_dd()
ctryname cowcode2 politycode un_region_name un_continent_name ehead leaderspellreg democracy regime start_year duration observed
Afghanistan 700 700 Southern Asia Asia Mohammad Zahir Shah Mohammad Zahir Shah.Afghanistan.1946.1952.Monarchy Non-democracy Monarchy 1946 7 1
Afghanistan 700 700 Southern Asia Asia Sardar Mohammad Daoud Sardar Mohammad Daoud.Afghanistan.1953.1962.Civilian Dict Non-democracy Civilian Dict 1953 10 1
Afghanistan 700 700 Southern Asia Asia Mohammad Zahir Shah Mohammad Zahir Shah.Afghanistan.1963.1972.Monarchy Non-democracy Monarchy 1963 10 1
Afghanistan 700 700 Southern Asia Asia Sardar Mohammad Daoud Sardar Mohammad Daoud.Afghanistan.1973.1977.Civilian Dict Non-democracy Civilian Dict 1973 5 0
Afghanistan 700 700 Southern Asia Asia Nur Mohammad Taraki Nur Mohammad Taraki.Afghanistan.1978.1978.Civilian Dict Non-democracy Civilian Dict 1978 1 0

I’m using the lovely library patsy here to create a covariance matrix from my original dataframe.

import patsy
X = patsy.dmatrix('un_continent_name + regime + start_year', data, return_type='dataframe')
X = X.rename(columns={'Intercept': 'baseline'})
 'regime[T.Military Dict]',
 'regime[T.Mixed Dem]',
 'regime[T.Parliamentary Dem]',
 'regime[T.Presidential Dem]',

We have also included the coef_penalizer option. During the estimation, a linear regression is computed at each step. Often the regression can be unstable (due to high co-linearity or small sample sizes) – adding a penalizer term controls the stability. I recommend always starting with a small penalizer term – if the estimates still appear to be too unstable, try increasing it.

aaf = AalenAdditiveFitter(coef_penalizer=1.0, fit_intercept=False)

An instance of AalenAdditiveFitter includes a fit method that performs the inference on the coefficients. This method accepts a pandas DataFrame: each row is an individual and columns are the covariates and two individual columns: a duration column and a boolean event occurred column (where event occurred refers to the event of interest - expulsion from government in this case)

X['T'] = data['duration']
X['E'] = data['observed'], 'T', event_col='E')

After fitting, the instance exposes a cumulative_hazards_ DataFrame containing the estimates of \(\int_0^t b_i(s) \; ds\):

baseline un_continent_name[T.Americas] un_continent_name[T.Asia] un_continent_name[T.Europe] un_continent_name[T.Oceania] regime[T.Military Dict] regime[T.Mixed Dem] regime[T.Monarchy] regime[T.Parliamentary Dem] regime[T.Presidential Dem] start_year
-0.03447 -0.03173 0.06216 0.2058 -0.009559 0.07611 0.08729 -0.1362 0.04885 0.1285 0.000092
0.14278 -0.02496 0.11122 0.2083 -0.079042 0.11704 0.36254 -0.2293 0.17103 0.1238 0.000044
0.30153 -0.07212 0.10929 0.1614 0.063030 0.16553 0.68693 -0.2738 0.33300 0.1499 0.000004
0.37969 0.06853 0.15162 0.2609 0.185569 0.22695 0.95016 -0.2961 0.37351 0.4311 -0.000032
0.36749 0.20201 0.21252 0.2429 0.188740 0.25127 1.15132 -0.3926 0.54952 0.7593 -0.000000

AalenAdditiveFitter also has built in plotting:

aaf.plot(columns=['regime[T.Presidential Dem]', 'baseline', 'un_continent_name[T.Europe]'], iloc=slice(1,15))

Regression is most interesting if we use it on data we have not yet seen, i.e., prediction! We can use what we have learned to predict individual hazard rates, survival functions, and median survival time. The dataset we are using is available up until 2008, so let’s use this data to predict the duration of former Canadian Prime Minister Stephen Harper.

ix = (data['ctryname'] == 'Canada') & (data['start_year'] == 2006)
harper = X.loc[ix]
print("Harper's unique data point:")
Harper's unique data point:
     baseline  un_continent_name[T.Americas]  un_continent_name[T.Asia] ...  start_year  T  E
268       1.0                            1.0                        0.0 ...      2006.0  3  0
ax = plt.subplot(2,1,1)

ax = plt.subplot(2,1,2)


Because of the nature of the model, estimated survival functions of individuals can increase. This is an expected artifact of Aalen’s additive model.

Cox’s Time Varying Proportional Hazard model


This implementation is still experimental.

Often an individual will have a covariate change over time. An example of this is hospital patients who enter the study and, at some future time, may recieve a heart transplant. We would like to know the effect of the transplant, but we cannot condition on whether they recieved the transplant naively. Consider that if patients needed to wait at least 1 year before getting a transplant, then everyone who dies before that year is considered as a non-transplant patient, and hence this would overestimate the hazard of not recieving a transplant.

We can incorporate changes over time into our survival analysis by using a modification of the Cox model above. The general mathematical description is:

\[\lambda(t | x) = \overbrace{b_0(t)}^{\text{baseline}}\underbrace{\exp \overbrace{\left(\sum_{i=1}^n \beta_i (x_i(t) - \overline{x_i}) \right)}^{\text{log-partial hazard}}}_ {\text{partial hazard}}\]

Note the time-varying \(x_i(t)\) to denote that covariates can change over time. This model is implemented in lifelines as CoxTimeVaryingFitter. The dataset schema required is different than previous models, so we will spend some time describing this.

Dataset creation for time-varying regression

Lifelines requires that the dataset be in what is called the long format. This looks like one row per state change, including an ID, the left (exclusive) time point, and right (inclusive) time point. For example, the following dataset tracks three unique subjects.

id start stop group z event
1 0 8 1 0 False
2 0 5 0 0 False
2 5 8 0 1 True
3 0 3 1 0 False
3 3 12 1 1 True

5 rows × 6 columns

In the above dataset, start and stop denote the boundaries, id is the unique identifier per subject, and event denotes if the subject died at the end of that period. For example, subject ID 2 had variable z=0 up to and including the end of time period 5 (we can think that measurements happen at end of the time period), after which it was set to 1. Since event is 1 in that row, we conclude that the subject died at time 8,

This desired dataset can be built up from smaller datasets. To do this we can use some helper functions provided in lifelines. Typically, data will be in a format that looks like it comes out of a relational database. You may have a “base” table with ids, durations alive, and a censorsed flag, and possibly static covariates. Ex:

id duration event var1
1 10 True 0.1
2 12 False 0.5

2 rows × 4 columns

We will perform a light transform to this dataset to modify it into the “long” format.

from lifelines.utils import to_long_format

base_df = to_long_format(base_df, duration_col="duration")

The new dataset looks like:

id start stop var1 event
1 0 10 0.1 True
2 0 12 0.5 False

2 rows × 5 columns

You’ll also have secondary dataset that references future measurements. This could come in two “types”. The first is when you have a variable that changes over time (ex: administering varying medication over time, or taking a tempature over time). The second types is an event-based dataset: an event happens at some time in the future (ex: an organ transplant occurs, or an intervention). We will address this second type later. The first type of dataset may look something like:


id time var2
1 0 1.4
1 4 1.2
1 8 1.5
2 0 1.6

4 rows × 3 columns

where time is the duration from the entry event. Here we see subject 1 had a change in their var2 covariate at the end of time 4 and at the end of time 8. We can use add_covariate_to_timeline to fold the covariate dataset into the original dataset.

from lifelines.utils import add_covariate_to_timeline

df = add_covariate_to_timeline(base_df, cv, duration_col="time", id_col="id", event_col="event")
id start stop var1 var2 event
1 0 4 0.1 1.4 False
1 4 8 0.1 1.2 False
1 8 10 0.1 1.5 True
2 0 12 0.5 1.6 False

4 rows × 6 columns

From the above output, we can see that subject 1 changed state twice over the observation period, finally expiring at the end of time 10. Subject 2 was a censored case, and we lost track of them after time 12.

You may have multiple covariates you wish to add, so the above could be streamlined like so:

from lifelines.utils import add_covariate_to_timeline

df = base_df.pipe(add_covariate_to_timeline, cv1, duration_col="time", id_col="id", event_col="event")\
            .pipe(add_covariate_to_timeline, cv2, duration_col="time", id_col="id", event_col="event")\
            .pipe(add_covariate_to_timeline, cv3, duration_col="time", id_col="id", event_col="event")

If your dataset is of the second type, that is, event-based, your dataset may look something like the following, where values in the matrix denote times since the subject’s birth, and None or NaN represent the event not happening (subjects can be excluded if the event never occurred as well) :


    id    E1
0   1     1.0
1   2     NaN
2   3     3.0

Initially, this can’t be added to our baseline dataframe. However, using utils.covariates_from_event_matrix we can convert a dataframe like this into one that can be easily added.

from lifelines.utils import covariates_from_event_matrix

cv = covariates_from_event_matrix(event_df, id_col="id")

event  id  duration  E1
0       1       1.0   1
1       3       3.0   1

base_df = add_covariate_to_timeline(base_df, cv, duration_col="time", id_col="id", event_col="E")

For an example of pulling datasets like this from a SQL-store, and other helper functions, see Example SQL queries and transformations to get time varying data.

Cumulative sums

One additional flag on add_covariate_to_timeline that is of interest is the cumulative_sum flag. By default it is False, but turning it to True will perform a cumulative sum on the covariate before joining. This is useful if the covariates describe an incremental change, instead of a state update. For example, we may have measurements of drugs administered to a patient, and we want the covariate to reflect how much we have administered since the start. Event columns do make sense to cumulative sum as well. In contrast, a covariate to measure the temperature of the patient is a state update, and should not be summed. See Example cumulative total using and time-varying covariates to see an example of this.

Delaying time-varying covariates

add_covariate_to_timeline also has an option for delaying, or shifting, a covariate so it changes later than originally observed. One may ask, why should one delay a time-varying covariate? Here’s an example. Consider investigating the impact of smoking on mortality and available to us are time-varying observations of how many cigarettes are consumed each month. Unbeknownst to us, when a subject reaches critical illness levels, they are admitted to the hospital and their cigarette consumption drops to zero. Some expire while in hospital. If we used this dataset naively, we would see that not smoking leads to sudden death, and conversely, smoking helps your health! This is a case of reverse causation: the upcoming death event actually influences the covariates.

To handle this, you can delay the observations by time periods:

from lifelines.utils import covariates_from_event_matrix

base_df = add_covariate_to_timeline(base_df, cv, duration_col="time", id_col="id", event_col="E", delay=14)

Fitting the model

Once your dataset is in the correct orientation, we can use CoxTimeVaryingFitter to fit the model to your data. The method is similar to CoxPHFitter, expect we need to tell the fit about the additional time columns.

Fitting the Cox model to the data involves using gradient descent. Lifelines takes extra effort to help with convergence, so please be attentive to any warnings that appear. Fixing any warnings will generally help convergence. For further help, see Problems with convergence in the Cox Proportional Hazard Model.

from lifelines import CoxTimeVaryingFitter

ctv = CoxTimeVaryingFitter(), id_col="id", event_col="event", start_col="start", stop_col="stop", show_progress=True)

Short note on prediction

Unlike the other regression models, prediction in a time-varying setting is not trivial. To predict, we would need to know the covariates values beyond the observed times, but if we knew that, we would also know if the subject was still alive or not! However, it is still possible to compute the hazard values of subjects at known observations, the baseline cumulative hazard rate, and baseline survival function. So while CoxTimeVaryingFitter exposes prediction methods, there are logicial limitations to what these predictions mean.

Model Selection in Survival Regression

Model selection based on residuals

The sections Testing the Proportional Hazard Assumptions and Assessing Cox model fit using residuals may be useful for modelling your data better.

Model selection based on predictive power

If censorship is present, it’s not appropriate to use a loss function like mean-squared-error or mean-absolute-loss. Instead, one measure is the concordance-index, also known as the c-index. This measure evaluates the accuracy of the ordering of predicted time. It is infact a generalization of AUC, another common loss function, and is interpreted similarly:

  • 0.5 is the expected result from random predictions,
  • 1.0 is perfect concordance and,
  • 0.0 is perfect anti-concordance (multiply predictions with -1 to get 1.0)

A fitted model’s concordance-index is present in the print_summary(), but also available under the score_ property. Generally, the measure is implemented in lifelines under lifelines.utils.concordance_index and accepts the actual times (along with any censorships) and the predicted times.

from lifelines import CoxPHFitter
from lifelines.datasets import load_rossi

rossi = load_rossi()

cph = CoxPHFitter(), duration_col="week", event_col="arrest")

# method one

# method two

# method three
from lifelines.utils import concordance_index
print(concordance_index(rossi['week'], -cph.predict_partial_hazard(rossi).values, rossi['arrest']))

However, there are other, arguably better, methods to measure the fit of a model. Included in print_summary is the log-likelihood, which can be used in an AIC calculation, and the log-likelihood ratio statistic. Generally, I personally loved this article by Frank Harrell, “Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements”.

Lifelines has an implementation of k-fold cross validation under lifelines.utils.k_fold_cross_validation. This function accepts an instance of a regression fitter (either CoxPHFitter of AalenAdditiveFitter), a dataset, plus k (the number of folds to perform, default 5). On each fold, it splits the data into a training set and a testing set fits itself on the training set and evaluates itself on the testing set (using the concordance measure).

from lifelines import CoxPHFitter
from lifelines.datasets import load_regression_dataset
from lifelines.utils import k_fold_cross_validation

regression_dataset = load_regression_dataset()
cph = CoxPHFitter()
scores = k_fold_cross_validation(cph, regression_dataset, 'T', event_col='E', k=3)

#[ 0.5896  0.5358  0.5028]
# 0.542
# 0.035