Competing Risks and Survival Analysis

Resources for Survival Analysis

Extended Joint Models for Longitudinal and Survival Data

Code repository for the manuscript 'Validation of the performance of competing risks prediction models: a guide through modern methods' (2023, BMJ)

Finite-Interval Forecasting Engine: Machine learning models for discrete-time survival analysis and multivariate time series forecasting

Targeted Learning for Survival Analysis

R package for fitting joint models to time-to-event and longitudinal data

Implementation of the DeepHit model for survival analysis with competing risks using PyTorch

HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks

Comparison of joint models for competing risks and longitudinal data

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.

Supplementary material for the paper: A review on competing risks methods for survival analysis

MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation. ML4H 2025.

Code and results of Section 4 of the paper "Fine-Gray subdistribution hazard models to simultaneously estimate the absolute risk of different event types: cumulative total failure probability may exceed 1", by Peter Austin, Ewout Steyerberg & Hein Putter

Code repository for the manuscript 'Multiple imputation of missing covariates when using the Fine–Gray model' (2025, Statistics in Medicine)

Code and supplementary materials for the manuscript "Multiple imputation for cause-specific Cox models: assessing methods for estimation and prediction" (2022, Statistical Methods in Medical Research)

Code accompanying the manuscript "Why you should avoid using multiple Fine–Gray models: insights from (attempts at) simulating proportional subdistribution hazards data" (2024, JRSS-A)

R pipeline for survival analysis with automatic detection of competing risks, recurrent events, time-varying exposures, and clustering—routes to appropriate statistical methods and generates publication-ready Quarto manuscripts.