Overview

BART-Survival is a Python package that supports discrete-time Survival analyses using the non-parametric machine learning algorithm, Bayesian Additive Regression Trees (BART). BART-Survival combines the performance of the BART algorithm from the PyMC-BART library with the proper structural formatting required to complete the end-to-end Survival analysis.

BART-Survival's performance is comparative to other Survival regression methods, such as Cox Proportional Hazard and AFT models, in low-complexity settings and can outperform these other models in high-complexity settings were their respective assumptions may fail (i.e. proportional hazard assumption, linearity assumptions). Additionally, the Bayesian framework provides easily accessible uncertainty intervals and capabilities to extensively interrogate trained model.

The BART-Survival library provides a simple API for completing standard Survival analysis, as well as allowing exposure to the underlying PyMC code providing accessibility to extend the BART-Survival library when necessary.

This repository contains the source code and documentation for the BART-Survival package as well as example notebooks.

BART-Survival can be installed directly from PyPi python pip install `BART-Survival`==0.1.1 Or as accessed from this repository.

API

BART-Survival API

Demonstration

Brief demo of the basic steps.

```python from lifelines.datasets import loadrossi from bartsurvival import surv_bart as sb import numpy as np

Load rossi dataset from lifelines

rossi = loadrossi() names = rossi.columns.tonumpy() rossi = rossi.to_numpy()

Transform data into 'augmented' dataset

Requires creation of the training dataset and a predictive dataset for inference

trn = sb.getsurvpretrain( ytime=rossi[:,0], ystatus=rossi[:,1], x = rossi[:,2:], timescale=7 )

posttest = sb.getposteriortest( ytime=rossi[:,0], ystatus=rossi[:,1], x = rossi[:,2:], timescale=7 )

Instantiate the BART models

model_dict is defines specific model parameters

modeldict = {"trees": 50, "splitrules": [ "pmb.ContinuousSplitRule()", # time "pmb.OneHotSplitRule()", # fin "pmb.ContinuousSplitRule()", # age "pmb.OneHotSplitRule()", # race "pmb.OneHotSplitRule()", # wexp "pmb.OneHotSplitRule()", # mar "pmb.OneHotSplitRule()", # paro "pmb.ContinuousSplitRule()", # prio ] }

sampler_dict defines specific sampling parameters

samplerdict = { "draws": 200, "tune": 200, "cores": 8, "chains": 8, "computeconvergencechecks": False } BSM = sb.BartSurvModel(modelconfig=modeldict, samplerconfig=sampler_dict)

Fit Model

BSM.fit( y = trn["y"], X = trn["x"], weights=trn["w"], coords = trn["coord"], random_seed=5 )

Get posterior predictive for evaluation.

post1 = BSM.sampleposteriorpredictive(Xpred=posttest["postx"], coords=posttest["coords"])

Convert to SV probability.

svprob = sb.getsv_prob(post1)

```

Validation study links

In progress...

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