# More Examples and Recipes¶

This section goes through some examples and recipes to help you use lifelines.

## Statistically compare two populations¶

(though this applies just as well to Nelson-Aalen estimates). Often researchers want to compare survival curves between different populations. Here are some techniques to do that:

### Subtract the difference between survival curves¶

If you are interested in taking the difference between two survival curves, simply trying to subtract the survival_function_ will likely fail if the DataFrame’s indexes are not equal. Fortunately, the KaplanMeierFitter and NelsonAalenFitter have a built in subtract method:

kmf1.subtract(kmf2)


will produce the difference at every relevant time point. A similar function exists for division: divide.

### Compare using a hypothesis test¶

For rigorous testing of differences, lifelines comes with a statistics library. The logrank_test function compares whether the “death” generation process of the two populations are equal:

 from lifelines.statistics import logrank_test

results = logrank_test(T1, T2, event_observed_A=E1, event_observed_B=E2)
results.print_summary()

"""
df=1, alpha=0.95, t0=-1, test=logrank, null distribution=chi squared

test_statistic        p
3.528  0.00034  **

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
"""

print(results.p_value)        # 0.46759
print(results.test_statistic) # 0.528
print(results.is_significant) # False


If you have more than two populations, you can use pairwise_logrank_test (which compares each pair in the same manner as above), or multivariate_logrank_test (which tests the hypothesis that all the populations have the same “death” generation process).

## Model selection using lifelines¶

If using lifelines for prediction work, it’s ideal that you perform some sort of cross-validation scheme. This allows you to be confident that your out-of-sample predictions will work well in practice. It also allows you to choose between multiple models.

lifelines has a built in k-fold cross-validation function. For example, consider the following example:

from lifelines import AalenAdditiveFitter, CoxPHFitter
from lifelines.utils import k_fold_cross_validation

#create the three models we'd like to compare.
cph = CoxPHFitter()

print(np.mean(k_fold_cross_validation(cph, df, duration_col='T', event_col='E')))
print(np.mean(k_fold_cross_validation(aaf_1, df, duration_col='T', event_col='E')))
print(np.mean(k_fold_cross_validation(aaf_2, df, duration_col='T', event_col='E')))


From these results, Aalen’s Additive model with a penalizer of 10 is best model of predicting future survival times.

## Displaying at-risk counts below plots¶

The function add_at_risk_counts in lifelines.plotting allows you to add At-Risk counts at the bottom of your figures. For example:

from numpy.random import exponential
T_control = exponential(10, size=250)
T_experiment = exponential(20, size=200)
ax = plt.subplot(111)

from lifelines import KaplanMeierFitter

kmf_control = KaplanMeierFitter()
ax = kmf_control.fit(T_control, label='control').plot(ax=ax)

kmf_exp = KaplanMeierFitter()
ax = kmf_exp.fit(T_experiment, label='experiment').plot(ax=ax)



will display

Alternatively, you can add this at the call to plot: kmf.plot(at_risk_counts=True)

## Transforming survival-table data into lifelines format¶

Some lifelines classes are designed for lists or arrays that represent one individual per row. If you instead have data in a survival table format, there exists a utility method to get it into lifelines format.

Example: Suppose you have a csv file with data that looks like this:

time (months, days, …) observed deaths censored
0 7 0
1 1 1
2 2 0
3 1 2
4 5 2
import pandas as pd
from lifelines.utils import survival_events_from_table

df = pd.read_csv('file.csv', columns = ['observed deaths', 'censored'])

T, E = survival_events_from_table(df, observed_deaths_col='observed deaths', censored_col='censored')

print(T) # array([0,0,0,0,0,0,0,1,...])
print(E) # array([1,1,1,1,1,1,1,0,...])


## Transforming observational data into survival-table format¶

Perhaps you are interested in viewing the survival table given some durations and censorship vectors.

from lifelines.utils import survival_table_from_events

table = survival_table_from_events(T, E)

"""
removed  observed  censored  entrance  at_risk
event_at
0               0         0         0        60       60
2               2         1         1         0       60
3               3         1         2         0       58
4               5         3         2         0       55
5              12         6         6         0       50
"""


## Plotting multiple figures on a plot¶

When .plot is called, an axis object is returned which can be passed into future calls of .plot:

kmf.fit(data1)
ax = kmf.plot()

kmf.fit(data2)
ax = kmf.plot(ax=ax)


If you have a pandas DataFrame with columns “group”, “T”, and “E”, then something like the following would work:

from lifelines import KaplanMeierFitter
from matplotlib import pyplot as plt

ax = plt.subplot(111)

kmf = KaplanMeierFitter()
for group in df['group'].unique():
data = grouped_data.get_group(group)
kmf.fit(data["T"], data["E"], label=group)
kmf.plot(ax=ax)


## Plotting options and styles¶

### Standard¶

kmf = KaplanMeierFitter()
kmf.fit(T, E, label="kmf.plot()")
kmf.plot()


### R-style¶

kmf.fit(T,C,label="kmf.plot(flat=True)")
kmf.plot(flat=True)


### Show censorships¶

kmf.fit(T, C, label="kmf.plot(show_censors=True)")
kmf.plot(show_censors=True)


### Hide confidence intervals¶

kmf.fit(T,C,label="kmf.plot(ci_show=False)")
kmf.plot(ci_show=False)


## Set the index/timeline of a estimate¶

Suppose your dataset has lifetimes grouped near time 60, thus after fitting KaplanMeierFitter, you survival function might look something like:

print(kmf.survival_function_)

KM-estimate
0          1.00
47         0.99
49         0.97
50         0.96
51         0.95
52         0.91
53         0.86
54         0.84
55         0.79
56         0.74
57         0.71
58         0.67
59         0.58
60         0.49
61         0.41
62         0.31
63         0.24
64         0.19
65         0.14
66         0.10
68         0.07
69         0.04
70         0.02
71         0.01
74         0.00


What you would really like is to have a predictable and full index from 40 to 75. (Notice that in the above index, the last two time points are not adjacent – this is caused by observing no lifetimes existing for times 72 or 73) This is especially useful for comparing multiple survival functions at specific time points. To do this, all fitter methods accept a timeline argument:

kmf.fit(T, timeline=range(40,75))
print(kmf.survival_function_)

KM-estimate
40         1.00
41         1.00
42         1.00
43         1.00
44         1.00
45         1.00
46         1.00
47         0.99
48         0.99
49         0.97
50         0.96
51         0.95
52         0.91
53         0.86
54         0.84
55         0.79
56         0.74
57         0.71
58         0.67
59         0.58
60         0.49
61         0.41
62         0.31
63         0.24
64         0.19
65         0.14
66         0.10
67         0.10
68         0.07
69         0.04
70         0.02
71         0.01
72         0.01
73         0.01
74         0.00


Lifelines will intelligently forward-fill the estimates to unseen time points.

## Example SQL query to get survival data from a table¶

Below is a way to get an example dataset from a relational database (this may vary depending on your database):

SELECT
id,
DATEDIFF('dd', started_at, COALESCE(ended_at, CURRENT_DATE)) AS "T",
(ended_at IS NOT NULL) AS "E"
FROM table


### Explanation¶

Each row is an id, a duration, and a boolean indicating whether the event occurred or not. Recall that we denote a “True” if the event did occur, that is, ended_at is filled in (we observed the ended_at). Ex:

id T E
10 40 True
11 42 False
12 42 False
13 36 True
14 33 True

## Example SQL queries and transformations to get time varying data¶

For Cox time-varying models, we discussed what the dataset should look like in Dataset for time-varying regression. Typically we have a base dataset, and then we fold in the covariate datasets. Below are some SQL queries and Python transformations from end-to-end.

### Base dataset: base_df¶

SELECT
id,
group,
DATEDIFF('dd', started_at, COALESCE(ended_at, CURRENT_DATE)) AS "T",
(ended_at IS NOT NULL) AS "E"
FROM dimension_table dt


### Time varying dataset: cv¶

SELECT
id,
DATEDIFF('dd', dt.started_at, ft.event_at) AS "time",
ft.var1
FROM fact_table ft
JOIN dimension_table dt
USING(id)

from lifelines.utils import to_long_format

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


## Example cumulative total using add_covariate_to_timeline¶

Often we have transactional covariate datasets and state covariate datasets. In a transaction dataset, it may make sense to sum up the covariates to represent administration of the treatment over time. For example, in the risky world of start-ups, we may want to sum up the funding amount recieved at a certain time. We also may be interested in the amount of the last round of funding. Below is an example to do just that:

Suppose we have an initial DataFrame of start-ups like:

seed_df = pd.DataFrame.from_records([
{'id': 'FB', 'E': True, 'T': 12, 'funding': 0},
{'id': 'SU', 'E': True, 'T': 10, 'funding': 0},
])


And a covariate dataframe representing funding rounds like:

cv = pd.DataFrame.from_records([
{'id': 'FB', 'funding': 30, 't': 5},
{'id': 'FB', 'funding': 15, 't': 10},
{'id': 'FB', 'funding': 50, 't': 15},
{'id': 'SU', 'funding': 10, 't': 6},
{'id': 'SU', 'funding': 9,  't':  10},
])


We can do the following to get both the cumulative funding recieved and the latest round of funding:

from lifelines.utils import to_long_format

df = seed_df.pipe(to_long_format, 'T')\
.pipe(add_covariate_to_timeline, cv, 'id', 't', 'E', cumulative_sum=True)\
.pipe(add_covariate_to_timeline, cv, 'id', 't', 'E', cumulative_sum=False)

"""
start  cumsum_funding  funding  stop  id      E
0      0             0.0      0.0   5.0  FB  False
1      5            30.0     30.0  10.0  FB  False
2     10            45.0     15.0  12.0  FB   True
3      0             0.0      0.0   6.0  SU  False
4      6            10.0     10.0  10.0  SU  False
5     10            19.0      9.0  10.0  SU   True
"""


## Sample size determination under a CoxPH model¶

Suppose you wish to measure the hazard ratio between two populations under the CoxPH model. That is, we want to evaluate the hypothesis H0: relative hazard ratio = 1 vs H1: relative hazard ratio != 1, where the relative hazard ratio is $$\exp{\left(\beta\right)}$$ for the experiment group vs the control group. Apriori, we are interested in the sample sizes of the two groups necessary to achieve a certain statistical power. To do this in lifelines, there is the lifelines.statistics.sample_size_necessary_under_cph function. For example:

from lifelines.statistics import sample_size_necessary_under_cph

desired_power = 0.8
ratio_of_participants = 1.
p_exp = 0.25
p_con = 0.35
postulated_hazard_ratio = 0.7
n_exp, n_con = sample_size_necessary_under_cph(desired_power, ratio_of_participants, p_exp, p_con, postulated_hazard_ratio)
# (421, 421)


This assumes you have estimates of the probability of event occuring for both the experiment and control group. This could be determined from previous experiments.

## Power determination under a CoxPH model¶

Suppose you wish to measure the hazard ratio between two populations under the CoxPH model. To determine the statistical power of a hazard ratio hypothesis test, under the CoxPH model, we can use lifelines.statistics.power_under_cph. That is, suppose we want to know the probability that we reject the null hypothesis that the relative hazard ratio is 1, assuming the relative hazard ratio is truely different from 1. This function will give you that probability.

from lifelines.statistics import power_under_cph

n_exp = 50
n_con = 100
p_exp = 0.25
p_con = 0.35
postulated_hazard_ratio = 0.5
power = power_under_cph(n_exp, n_con, p_exp, p_con, postulated_hazard_ratio)
# 0.4957


## Problems with convergence in the Cox Proportional Hazard Model¶

Since the estimation of the coefficients in the Cox proportional hazard model is done using the Newton-Raphson algorithm, there is sometimes a problem with convergence. Here are some common symptoms and possible resolutions:

• Some coefficients are many orders of magnitude larger than others, and the standard error of the coefficient is equally as large. This can be seen using the print_summary method on a fitted CoxPHFitter object. Look for a RuntimeWarning about variances being too small. The dataset may contain a constant column, which provides no information for the regression (Cox model doesn’t have a traditional “intercept” term like other regression models). Or, the data is completely separable, which means that there exists a covariate the completely determines whether an event occured or not. For example, for all “death” events in the dataset, there exists a covariate that is constant amongst all of them. Another problem may be a colinear relationship in your dataset - see the third point below.
• Adding a very small penalizer_coef significantly changes the results. This probably means that the step size is too large. Try decreasing it, and returning the penalizer_coef term to 0.
• LinAlgError: Singular matrix is thrown. This means that there is a linear combination in your dataset. That is, a column is equal to the linear combination of 1 or more other columns. Try to find the relationship by looking at the correlation matrix of your dataset.
• If using the strata arugment, make sure your stratification group sizes are not too small. Try df.groupby(strata).count().