surpyval 0.16.0

A python package for survival analysis

SurPyval - Survival Analysis in Python

Yet another Python survival analysis tool.

This is another pure python survival analysis tool so why was it needed? The intent of this package was to closely mimic the scipy API as close as possible with a simple .fit() method for any type of distribution (parametric or non-parametric); other survival analysis packages don't completely mimic that API. Further, there is currently (at the time of writing) no pacakage that can take an arbitrary comination of observed, censored, and truncated data. Finally, surpyval is unique in that it can be used with multiple parametric estimation methods. This allows for an analyst to determine a distribution for the parameters if another method fails. The parametric methods available are Maximum Likelihood Estimation (MLE), Probability Plotting (MPP), Mean Square Error (MSE), Method of Moments (MOM), and Maximum Product of Spacing (MPS). Surpyval can, for each type of estimator, take the following types of input data:

Method	Para/Non-Para	Observed	Censored	Truncated
MLE	Parametric	Yes	Yes	Yes
MPP	Parametric	Yes	Yes	Limited
MSE	Parametric	Yes	Yes	Limited
MOM	Parametric	Yes	No	No
MPS	Parametric	Yes	Yes	No
Kaplan-Meier	Non-Parametric	Yes	Right only	Left only
Nelson-Aalen	Non-Parametric	Yes	Right only	Left only
Fleming-Harrington	Non-Parametric	Yes	Right only	Left only
Turnbull	Non-Parametric	Yes	Yes	Yes

SurPyval also offers many different distributions for users, and because of the flexible implementation adding new distributions is easy. Further, the power of SurPyval lay in the robust parameter estimation, as such, some distributions, those that are supported on the half real line, can be offset to make a three- or four-parameter version. The currently available distributions are:

Distribution	Offsetable
Weibull	Yes
Normal	No
LogNormal	Yes
Gamma	Yes
Beta	No
Beta (4 parameter)	No
Uniform	No
Exponential	Yes
Exponentiated Weibull	Yes
Gumbel	No
Logistic	No
LogLogistic	Yes

This project spawned from a Reliaility Engineering project; due to the history of reliability engineers estimating parameters from a probability plot. SurPyval has continued this tradition to ensure that any parametric distribution can have the estimate plotted on a probability plot. These visualisations enable an analyst to get a sense of the goodness of fit of the parametric distribution with the non-parametric distribution.

The Model Landscape

SurPyval's models can be placed on a set of orthogonal axes. The table below cross-tabulates four of those axes — the time scale (continuous-time durations vs discrete-time trials), event recurrence, competing events, and covariates — against the estimation axis, and fills each cell with what can be used to implement it. The discrete-time models are single-event, not recurrent: Bernoulli is a single trial and Binomial is the sum of n such trials — neither is a recurrent-event process. A — marks a combination that is either not applicable (e.g. semiparametric estimation requires covariates) or not yet built.

Time	Recurrence	Events	Covariates	Parametric	Semi-/Nonparametric
Continuous time	Single event	Single	Without	Weibull, Exponential, LogNormal, Gamma, …	KaplanMeier, NelsonAalen, FlemingHarrington, Turnbull
Continuous time	Single event	Single	With	WeibullPH/WeibullAFT (PH/AFT/PO families)	CoxPH
Continuous time	Single event	Competing	Without	—	CompetingRisks (CIF)
Continuous time	Single event	Competing	With	—	FineGray, CRPH
Continuous time	Recurrent	Single	Without	HPP, NHPP, CrowAMSAA, Duane, CoxLewis	NonParametricCounting (MCF)
Continuous time	Recurrent	Single	With	ProportionalIntensityHPP, ProportionalIntensityNHPP	—
Continuous time	Recurrent	Competing	Without	—	CauseSpecificMCF
Continuous time	Recurrent	Competing	With	—	—
Discrete time	Single event	Single	Without	Bernoulli (single trial), Binomial (n trials)	—
Discrete time	Single event	Single	With	use logistic / binomial regression (out of scope for this package)	—

Install and Quick Intro

SurPyval can be installed via pip using the PyPI repository

pip install surpyval

If you're familiar with survival analysis, and Weibull plotting, the following is a quick start.

from surpyval import Weibull
from surpyval.datasets import load_bofors_steel

# Fetch some data that comes with SurPyval
data = load_bofors_steel()

x = data['x']
n = data['n']

model = Weibull.fit(x=x, n=n, offset=True)
model.plot();

Documentation

SurPyval is well documented, and improving, at the main documentation.

Development

Dependencies

pip install -r requirements_dev.txt

Testing

Run the testing suite by simply executing:

pytest

or use coverage to get a coverage report:

coverage run -m pytest  # Run pytest under coverage's watch
coverage report         # Print coverage report
coverage html           # Make a html coverage report (really useful), open htmlcov/index.html

Pre-commit

• Pip install pre-commit (it's in requirements_dev.txt anyways)
• Run pre-commit install which sets up the git hook scripts
• If you'd like, run pre-commit run --all-files to run the hooks on all files
• When you go to commit, it will only proceed after all the hooks succeed

Contact

Email derryn if you want any features or to see how SurPyval can be used for you.