kmsurvival

kmsurvial is an implementation of Kaplan-Meier (KM) survival estimation in Python. It's a practical program for comparing survial probabilities qualitatively among groups. And it's also small, fast, and easy to use.

The reason for writing a new KM estimator is that some features I want are not available or flexible in other implementations as of early 2016.

Features

• Differentiate between the snapshot date and cutoff dates of a data set.
• Support hierarchical strata.
• Flexible combination of groups for comparisions.
• Support multiple data input formats.
• Users can easily get hazards and survival functions which can be piped into visualziaiton or further data processing.

Installation

kmsurvival can be installed with the following command:

pip install kmsurvival

Examples

import pandas as pd
from kmsurvival import KMSurvival, plot_right_censor

df = pd.read_csv('censored_start_stop.txt', sep='\t', 
                 parse_dates=['start_date', 'stop_date'])
kms = KMSurvival(col_start_date='start_date',
                 col_stop_date='stop_date',
                 col_censored = 'censored')
cutoff = '2008-12-28'                 
group = ['market']
kms.fit(df, cutoff, group)
kms.plot(vlines=[365])                 

kmsurvival includes an auxiliary function to plot right-censoring.

snapshot_date = '2008-12-28'
cutoff_date = '2008-09-18'
n = 20
plot_right_censor(df[:n].copy(), snapshot_date, cutoff_date)

See the post about dynamic right-censoring.

Dependencies

kmsurvival has been tested under Python 3.5, Numpy 1.1, pandas 0.18, and matplotlib 1.5.