Survivex brings GPU-accelerated survival analysis to Python

Survival analysis, the branch of statistics devoted to modeling the time until an event occurs, has long been a stronghold of the R programming language. Now a new open-source library called Survivex aims to change that. Developed by Tanin Zeraati and Patricia Lasserre and described in the journal SoftwareX, Survivex consolidates classical statistical estimators, semi-parametric regression, machine learning methods, and GPU acceleration into a single Python package, addressing a fragmentation problem that has forced data scientists to juggle multiple incompatible tools or switch to R entirely for advanced analyses.

The defining feature of survival data is censoring: for many subjects, the event of interest—death, machine failure, rearrest, or customer churn—is only partially observed because a study ends before the event happens. Statistical methods must therefore extract information from incomplete observations, a challenge that has produced a rich family of techniques over decades. Clinical researchers use these methods to evaluate treatment efficacy, reliability engineers to predict component failure, and social scientists to study recidivism, unemployment duration, and other time-to-event phenomena. In every case, the mathematics must account for the fact that some event times are simply unknown.

R has historically dominated this field, with the survival package offering comprehensive functionality refined over many years. Python, despite becoming the dominant language for data science and machine learning, has lacked an equivalent. Existing libraries each cover only a slice of the landscape: lifelines implements classical models such as Kaplan–Meier estimation and Cox regression on CPU but offers no tree-based machine learning or recurrent-event models; scikit-survival targets machine-learning estimators like random survival forests but omits frailty and multi-state models; and deep-learning packages such as pycox provide neural-network survival models without the classical estimators. No single Python package previously spanned all of these families under one interface.

Survivex closes that gap with a modular design organized by model family, comprising a models module, a datasets module for loading and validation, and a core module with shared data structures. All models follow scikit-learn conventions, exposing a fit method for parameter estimation and trailing-underscore attributes for fitted values, which makes the library immediately familiar to the large community of Python machine-learning practitioners. The package handles both wide-format data, where each subject occupies a single row, and long-format data, which supports time-varying covariates, recurrent events, and multi-state models, with converters and validators to catch common data quality issues before fitting.

The methodological coverage spans eight categories. Non-parametric estimators include the Kaplan–Meier survival curve with Greenwood’s variance estimator, the Nelson–Aalen cumulative hazard estimator, and the log-rank test for comparing groups. Semi-parametric regression centers on Cox proportional hazards with both Breslow and Efron tie handling, solved by Newton–Raphson iterations using analytically derived gradients and Hessians, plus stratified and ridge-penalized variants. Parametric accelerated failure time models cover Weibull, Log-Normal, Log-Logistic, and Exponential distributions. Competing risks are handled through the Aalen–Johansen estimator and the Fine–Gray subdistribution hazard model, while multi-state models support illness-death and progressive disease configurations.

Recurrent-event analysis, historically unavailable in Python, receives three approaches: the Andersen–Gill model treating repeated events as exchangeable, and two Prentice–Williams–Peterson variants stratified by event number on either total time or gap time, all with robust sandwich variance estimates for within-subject correlation. Frailty models capture unobserved heterogeneity in clustered data using gamma or log-normal random effects fitted by an expectation-maximization algorithm. Machine learning methods include random survival forests built on log-rank splitting statistics and gradient boosting that optimizes the Cox partial likelihood, both providing feature importance scores.

Correctness is the library’s central claim, and the developers back it with unusually rigorous validation. Every estimator was checked against established reference implementations—R’s survival, penalized, frailtyEM, and mstate packages, plus Python’s lifelines—using standard benchmark datasets including the gbsg2 breast cancer cohort, the Rossi recidivism data, and bladder cancer and kidney infection datasets. Cox models reproduce R’s coefficients to machine precision, around ten to the minus fifteen, under all tested variants. Frailty estimates agree to within roughly ten to the minus four, a residual bounded by the iterative convergence of the EM algorithm rather than exact algebra. All comparisons are seeded, scripted, and run in double precision, with a continuous-integration pipeline testing across Python versions and nightly gold-standard comparisons against R.

Performance results are equally striking. On CPU, Survivex runs two to eighteen times faster than lifelines on standard benchmarks, with non-parametric estimators and Weibull models gaining the most. The real headline, however, is GPU acceleration through PyTorch. Because the Cox-family solvers rely on linear algebra, GPU speedup grows with problem dimension: on synthetic data, Cox regression reaches a 35.6-fold speedup at one hundred covariates, and recurrent-event models gain thirteen to sixteen times. A crucial design decision is the use of closed-form analytical derivatives rather than automatic differentiation, which the authors show is sixteen to fifty-one times slower for the same fit, while still yielding the standard errors and confidence intervals that survival analysis demands.

The most dramatic demonstration comes from a real-world genomics benchmark. The authors pooled eight TCGA PanCancer Atlas cohorts—4,112 patients and 1,382 events—into a ridge-penalized stratified Cox model and swept the covariate count up to 5,000 genes. With GPU acceleration, Survivex was twelve to 308 times faster than lifelines, the margin widening with dimension: at 5,000 covariates, the fit took 43 seconds versus 3.7 hours. CPU and GPU coefficients agreed to machine precision at every dimension, and peak GPU memory stayed below 2 gigabytes thanks to a sufficient-statistics identity that avoids materializing enormous per-sample tensors. The authors caution that these results establish numerical correctness and speed, not clinical readiness; deployment in healthcare or other sensitive settings would still require domain-specific validation and governance review. Released under the Apache License 2.0 with a zero-install Binder environment for browser-based experimentation, Survivex positions itself as validated analytical infrastructure for the era of large-scale electronic health records and high-dimensional genomics.

Subject of Research: A GPU-accelerated Python library for survival analysis of time-to-event data

Article Title: Survivex: A GPU-accelerated survival analysis library for Python

Article References: Zeraati, T., & Lasserre, P. (2026). Survivex: A GPU-accelerated survival analysis library for Python. SoftwareX, 36, Article 103081. https://doi.org/10.1016/j.softx.2026.103081

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103081

Keywords: survival analysis, Python, GPU acceleration, PyTorch, Cox regression, Kaplan-Meier, recurrent events, frailty models, competing risks, machine learning, biostatistics, open-source software