Biostatistics Python Packages

comprisk

Scalable, scikit-learn-compatible competing-risks survival analysis in pure Python — CR random survival forest, Fine-Gray, cause-specific Cox, Aalen-Johansen CIF, Gray's test, and exact TreeSHAP. 10–22× faster than randomForestSRC on real EHR and 16.6–544× vs scikit-survival (n=5k→50k); scales to n=10⁶ in ~1 min.

pyseqtarget

Estimation of the observational analogs of the intention-to-treat and per-protocol effects of hypothetical treatment strategies

gen-surv

Simulate survival data with a known truth: twelve models spanning proportional hazards, accelerated failure time, competing risks, cure fractions, recurrent events and multi-state processes. Docs: https://diogoribeiro7.github.io/genSurvPy/

pygna

A Python package for gene network analysis

pymmeans

Estimated marginal means, contrasts, and small-sample mixed-model inference for Python — a native port of R's emmeans and pbkrtest.

biostatistics

An Intuitive App for Statistical Analysis

survshap

SurvSHAP(t): Time-dependent explanations of machine learning survival models

pysofra

Python equivalent of R's gtsummary and tableone — publication-ready Table 1, regression, and survival tables with survey weights and multiple imputation

crforest

Scalable, scikit-learn-compatible competing-risks survival analysis in pure Python — CR random survival forest, Fine-Gray, cause-specific Cox, Aalen-Johansen CIF, Gray's test, and exact TreeSHAP. 10–22× faster than randomForestSRC on real EHR and 16.6–544× vs scikit-survival (n=5k→50k); scales to n=10⁶ in ~1 min.

missingdata

missing data handing: visualize and impute

pycmprsk

A Python port of the R package cmprsk. Estimation, testing and regression modeling of subdistribution functions in competing risks.

py4csr

Python for Clinical Study Reporting - Professional clinical trial reporting package

chai-one

An free and open source community implementation of Chai-1 in PyTorch

medicaid-utils

Python toolkit for Medicaid claims data analysis — preprocessing, cleaning, risk adjustment (Elixhauser, CDPS-Rx), quality measures (PQI, BETOS, low-value care), and patient-level analytic file construction for MAX and TAF CMS data. Built on Dask for scalable processing of large-scale healthcare claims datasets.

pmoss

pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets