A curated list of awesome resources for survival analysis.
- lifelines: A complete survival analysis library, written in pure Python.
- scikit-survival: A Python module for survival analysis built on top of scikit-learn.
- PySurvival: A Python package for survival analysis, offering 10+ models from Cox PH to Random Survival Forests, with tools for model building, cross-validation, and prediction.
- CRAN Task View: Survival Analysis: A comprehensive overview of available R packages for survival analysis, including tools for estimation, regression, and multistate models, along with many others aimed at the analysis of time-to-event data.
- survival: Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier curves, and Cox models.
- Cox model predictions: Documentation for making predictions from a Cox regression model.
- Concordance in Survival Analysis: A document detailing the concept of concordance in survival analysis.
- dynpred: Tools for the dynamic prediction in survival analysis.
- pec: Prediction error curves for risk prediction models in survival analysis.
- Landmarking: Methods for Landmarking and Survival Analysis.
- rstanarm: Bayesian Applied Regression Modeling via Stan.
- JM: Joint Modeling of Longitudinal and Time-to-Event Data.
- JMbayes: Joint modeling of longitudinal and time-to-event data, employing Bayesian methods with MCMC techniques
- randomForestSRC: Random Forests for Survival, Regression, and Classification (RF-SRC).
- LTRCforests: Analyzes Long-Term, Right-Censored longitudinal data using random forests, suitable for censored data in medical and reliability studies.
- rms: Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit.
- survPresmooth: Provides presmoothed estimation in survival analysis, enhancing classical estimators with methods for incorporating all data, including censored observations.
- survminer: Facilitates the creation of survival plots, featuring 'number at risk' tables, censoring count plots, and options for customized, publication-ready outputs.
- gwasurvivr: Enables efficient analysis of genetic variants' impact on survival outcomes.
- cenROC: Provides tools for analyzing time-dependent receiver operating characteristic (ROC) curves with right-censored event time data.
- survivalmodels: Extensive suite for survival analysis in R, offering a broad array of tools for modeling, prediction, and inference. Features support for time-dependent covariates, multiple types of censoring, and complex survival models.
- survivalAnalysis: High-Level Interface for Survival Analysis and Associated Plots
- SurvMetrics: Comprehensive performance metrics for survival models
- icensBKL: Bias-corrected estimation for interval-censored data
- SmoothHazard: Smooth hazard models for interval-censored data
- bayesSurv: Bayesian Survival Regression with Flexible Error and Random Effects Distributions
- censored: Censored is the backend engines that are used by parsnip which of itself is part of the tidymodels ecosystem.
- tidymodels: Tidymodels is a collection of packages for modeling and machine learning using tidyverse principles. It includes many ML implementations, including survival analysis. Use the search option to find the survival analysis package (mainly {parsnip} and {censored}) you need.
- Survival.jl: Provides types and methods for performing survival analysis in Julia (event times, Kaplan-Meier Estimator, Nelson-Aalen Estimator, Cox Proportional Hazards Model)
- Time-dependent covariates in survival analysis: A vignette discussing the use of time-dependent covariates in the context of survival analysis.
- Competing Risks in Survival Analysis: A vignette covering multi-state models and competing risks from the
survivalpackage. - How to use the Landmarking package: A guide on using the Landmarking package in R.
- Joint Modeling using rstanarm: Vignette on joint modeling using the rstanarm package.
- Survival Analysis using tidymodels: A ‘Get Started’ for event-time based models.
- Survival Analysis Basics: A comprehensive tutorial on survival analysis basics by STHDA, covering key concepts, methods, and R implementation.
- Survival Analysis in R: An extensive tutorial by Emily Zabor on performing survival analysis in R.
- An Introduction to Joint Modeling in R: A tutorial on joint modeling in R from R-bloggers.
- Survival Analysis in tidymodels: A brief intro to survival analysis by Max Kuhn (author of the tidymodels).
- Survival Analysis in tidymodels 2: A brief intro to survival analysis from the 2022 R conference.
- Prognostic Factor Analysis using Survival Data: An academic article from NCBI discussing methods and considerations in prognostic factor analysis using survival data, providing insights into advanced survival analysis techniques.
- Dynamic Predictions using Joint Modeling and Landmarking: Dynamic Predictions with Time-Dependent Covariates in Survival Analysis using Joint Modeling and Landmarking.
- Concordance for Survival Time Data: Concordance in survival analysis with fixed and time-dependent covariates, including methods for dealing with ties in predictor and event times.