A curated list of awesome resources for survival analysis.
Packages
Python Packages
lifelines: A complete survival analysis library, written in pure Python.
scikit-survival: A Python module for survival analysis built on top of scikit-learn.
R Packages
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.
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.
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.