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)

Targeted Learning for Survival Analysis

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

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

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

Comparison of joint models for competing risks and longitudinal data

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

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 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 and supplementary materials for the manuscript "Joint models quantify associations between T-cell kinetics and allo-immunological events after allogeneic stem cell transplantation and subsequent donor lymphocyte infusion" (2023, Frontiers in Immunology)

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)

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

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

Simulating time-to-event data from parametric distributions, custom distributions, competing risk models and general multi-state models in Stata

A Time-Dependent Structural Model Between Latent Classes and Competing Risks Outcomes