Library of Stan Models for Survival Analysis
Jupyter Notebook Python Stan
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survivalstan: Survival Models in Stan

author: Jacki Novik


Library of Stan Models for Survival Analysis


  • Variety of standard survival models
    • Weibull, Exponential, and Gamma parameterizations
    • PEM models with variety of baseline hazards
    • PEM model with varying-coefficients (by group)
    • PEM model with time-varying-effects
  • Extensible framework - bring your own Stan code, or edit the models above
  • Uses pandas data frames & patsy formulas
  • Graphical posterior predictive checking (currently PEM models only)
  • Plot posterior estimates of key parameters using seaborn
  • Annotate posterior draws of parameter estimates, format as pandas dataframes
  • Works with extensions to pystan, such as stancache or pystan-cache


Documentation is available online.

For help, please reach out to us on gitter.

Installation / Usage

Install using pip, as:

$ pip install survivalstan

Or, you can clone the repo:

$ git clone
$ pip install .


Please contribute to survivalstan development by letting us know if you encounter any bugs or have specific feature requests.

In addition, we welcome contributions of:

  • Stan code for survival models
  • Worked examples, as jupyter notebooks or markdown documents

Usage examples

There are several examples included in the example-notebooks, roughly one corresponding to each model.

If you are not sure where to start, Test pem_survival_model with simulated data.ipynb contains the most explanatory text. Many of the other notebooks are sparse on explanation, but do illustrate variations on the different models.

For basic usage:

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  ='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'])

## or, use plot_coefs

## print summary of MCMC draws from posterior for each parameter

## 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

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

## plot baseline hazard (only PEM models)
survivalstan.utils.plot_coefs([testfit_pem], element='baseline')

## posterior-predictive checking (only PEM models)

## 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.)

## (or, use survivalstan.utils.plot_coefs)
survivalstan.utils.plot_coefs([testfit_wei, testfit_pem])