A curated list of useful resources on learning and researching 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.
- 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.
- 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 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.
- 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.