I'm trying to figure out the quickest way to get survival analysis data into a format that will allow for time varying covariates. Basically this would be a python implementation of stsplit
in Stata. To give a simple example, with the following set of information:
id start end x1 x2 exit
1 0 18 12 11 1
This tells us that an observation started at time 0, and ended at time 18. Exit tells us that this was a 'death' rather than right censoring. x1 and x2 are variables that are constant over time.
id t age
1 0 30
1 7 40
1 17 50
I'd like to get:
id start end x1 x2 exit age
1 0 7 12 11 0 30
1 7 17 12 11 0 40
1 17 18 12 11 1 50
Exit is only 1 at the end, signifying that t=18 is when the death occurred.
Assuming:
>>> df1
id start end x1 x2 exit
0 1 0 18 12 11 1
and:
>>> df2
id t age
0 1 0 30
1 1 7 40
2 1 17 50
You can do:
df = df2.copy() # start with df2
df['x1'] = df1.ix[0, 'x1'] # x1 column
df['x2'] = df1.ix[0, 'x2'] # x2 column
df.rename(columns={'t': 'start'}, inplace=True) # start column
df['end'] = df['start'].shift(-1) # end column
df.ix[len(df)-1, 'end'] = df1.ix[0, 'end']
df['exit'] = 0 # exit column
df.ix[len(df)-1, 'exit'] = 1
df = df[['id', 'start', 'end', 'x1', 'x2', 'exit', 'age']] # reorder columns
Output:
>>> df
id start end x1 x2 exit age
0 1 0 7 12 11 0 30
1 1 7 17 12 11 0 40
2 1 17 18 12 11 1 50
This is possible using lifelines , specifically, the add_covariate_to_timeline
function, example here . The function is quite flexible and can doing things like cumulative sums, etc.
For the example above:
"""
id start end x1 x2 exit
1 0 18 12 11 1
"""
long_df = pd.DataFrame([
{'id': 1, 'start': 0, 'end': 18, 'x1': 12, 'x2': 11, 'exit': 1}
])
"""
id t age
1 0 30
1 7 40
1 17 50
"""
tv_covariates = pd.DataFrame([
{'id': 1, 't': 0, 'age': 30},
{'id': 1, 't': 7, 'age': 40},
{'id': 1, 't': 17, 'age': 50},
])
from lifelines.utils import add_covariate_to_timeline
add_covariate_to_timeline(long_df, tv_covariates, id_col='id', duration_col='t', event_col='exit', start_col='start', stop_col='end')
"""
start age x1 x2 end id exit
0 0 30 12.0 11.0 7.0 1 False
1 7 40 12.0 11.0 17.0 1 False
2 17 50 12.0 11.0 18.0 1 True
"""
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