Survival analysis in Python

Fast Best-Subset Selection Library

Explainable Machine Learning in Survival Analysis

Resources for Survival Analysis

COX Proportional risk model and survival analysis implemented by tensorflow.

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Survival analysis in Julia

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

Code repository for the manuscript: 'Assessing performance in prediction models with survival outcomes: practical guidance for Cox proportional hazards models' (published in Annals of Internal Medicine)

Code for the paper "Deep Cox Mixtures for Survival Regression", Machine Learning for Healthcare Conference 2021

snpnet - Efficient Lasso Solver for Large-scale genetic variant data

geneSurv: an interactive web-based tool for survival analysis in genomics research

ISMB 2020: Improved survival analysis by learning shared genomic information from pan-cancer data

R material for LSHTM's Advanced Statistical Methods in Epidemiology (ASME) practical sessions

psfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets

A 30+ node flowchart for selecting the right statistical test for evaluating experimental data.

Survival analysis utility functions using functional programming principals

MAMBA – Multi-pAradigM voxel-Based Analysis: a computational cookbot

Survival analysis functions that allow left truncation and weighting, including Aalen-Johansen, Kaplan-Meier, and Cox proportional hazards regression