A software package for simple and rigorous survival machine learning analysis in biomedical research

Survival analysis is a fundamental technique in biomedical research for modeling time-to-event data. It enables the identification of prognostic factors in disease, compares survival outcomes across treatment groups, and performs targeted treatment selection. A variety of machine learning (ML) approaches to survival analysis have emerged to complement classical statistical methods, especially for high-dimensional datasets with complex, nonlinear interactions between features. However, using survival ML methods requires addressing challenges such as censoring-unaware evaluation, overfitting, selecting performance metrics, and data leakage. To address these and other difficulties in using survival ML models, we developed the mlsurv software package. mlsurv is an open-source Python package built around three major design principles: 1) methodological rigor, including evidence-based model selection, leakage-free pipelines, and multi-metric evaluation, 2) multi-scale evaluation and interpretation, including population and subpopulation evaluation, patient-level explanations, and feature analysis, and 3) automated trust and transparency, including limitation flagging and TRIPOD+AI-aligned reporting. mlsurv bundles ten models spanning linear, ensemble, kernel, and deep learning families within a unified software package. To our knowledge, mlsurv is the first package to span the complete survival ML workflow from automated model recommendation through TRIPOD+AI reporting and individual patient explanation. We demonstrate mlsurv on the Chowell immunotherapy cohort (n=1,479). We found that overall survival (OS) was more predictable than progression-free survival (PFS) (concordance of 0.73 vs 0.67). Albumin was a top feature for both endpoints but dominated OS prediction, whereas tumor mutational burden rose to co-lead PFS prediction. Survival models matched the response-trained LORIS clinical score on PFS prediction and exceeded it on OS. mlsurv enables biomedical researchers to conduct rigorous, multi-model survival analysis and benchmarking using minimal code with default best practices rather than implementing custom scripts and methodological safeguards from scratch.