survivalstan 0.1.1

Library of Stan Models for Survival Analysis

[](https://travis-ci.org/hammerlab/survivalstan)
[](https://coveralls.io/github/hammerlab/survivalstan?branch=master)
[](https://badge.fury.io/py/survivalstan)

survivalstan: Survival Models in Stan
===============================

author: Jacki Novik

Overview
--------

Library of Stan Models for Survival Analysis

Installation / Usage
--------------------

Once we push this repo to pypi, you will be able to install using pip, as:

$ pip install survivalstan ## (not yet set up)


For now, please clone the repo:

$ git clone https://github.com/hammerlab/survivalstan.git
$ pip install .

Contributing
------------

TBD

Example
-------

```
import survivalstan
import stanity
import seaborn as sb
import matplotlib.pyplot as plt
import statsmodels

## load flchain test data from R's `survival` package
dataset = statsmodels.datasets.get_rdataset(package = 'survival', dataname = 'flchain' )
d = dataset.data.query('futime > 7')
d.reset_index(level = 0, inplace = True)

## e.g. fit Weibull survival model
testfit_wei = survivalstan.fit_stan_survival_model(
model_cohort = 'Weibull model',
model_code = survivalstan.models.weibull_survival_model,
df = d,
time_col = 'futime',
event_col = 'death',
formula = 'age + sex',
iter = 3000,
chains = 4,
make_inits = survivalstan.make_weibull_survival_model_inits
)

## coefplot for Weibull coefficient estimates
sb.boxplot(x = 'value', y = 'variable', data = testfit_wei['coefs'])

## print summary of MCMC draws from posterior for each parameter
print(testfit_wei['fit'])


## e.g. fit Piecewise-exponential survival model
dlong = survivalstan.prep_data_long_surv(d, time_col = 'futime', event_col = 'death')
testfit_pem = survivalstan.fit_stan_survival_model(
model_cohort = 'PEM model',
model_code = survivalstan.models.pem_survival_model,
df = dlong,
sample_col = 'index',
timepoint_end_col = 'end_time',
event_col = 'end_failure',
formula = 'age + sex',
iter = 3000,
chains = 4,
)

## print summary of MCMC draws from posterior for each parameter
print(testfit_pem['fit'])

## coefplot for PEM model results
sb.boxplot(x = 'value', y = 'variable', data = testfit_pem['coefs'])

## e.g. compare models using PSIS-LOO
stanity.loo_compare(testfit_wei['loo'], testfit_pem['loo'])

## compare coefplots
sb.boxplot(x = 'value', y = 'variable', hue = 'model_cohort',
data = testfit_pem['coefs'].append(testfit_wei['coefs']))
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
```