Survival Analysis of Lung Cancer Patients

Python survival analysis/survival curves, with a scikit-learn-like api

KM plots and Cox Proportional Hazards model for feature selection

A Python distribution of iCARE, a tool for individualized Coherent Absolute Risk Estimation.

Breast Cancer Survival Analysis using SAS on METABRIC dataset to identify key survival factors with Kaplan-Meier, Cox models, and gamma distributions

Federated algorithm for coxph in Vantage6 v4

Python implementation of extracting body weight dynamics in diversity outbred mice using ARHMM.

survival analysis on cirrhosis data from mayo clinic study: kaplan-meier estimator/curve, log rank test, cox proportional hazards model

CoxKAN: Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival Analysis

Exploring disparities in the COMPAS algorithm: an analysis of recidivism predictions among demographic groups.

Methodology research comparing statistical and ML methods of competing risks analysis

A comprehensive end-to-end survival analysis project using classical and deep learning models with clinical data, including preprocessing, modeling, evaluation, and interpretability.

This repository contains Python code for performing Cox proportional hazards model analysis tailored to crossover study designs.

A deep partially linear regression to estimate the change point and its linear and nonlinear effects.

WhenDidThatHappen is an R package for preparing survival analyses. It takes your Datetimes and derives time-to-event variables for use in Kaplan-Meier models, Cox Proportional Hazards models, Competing Risks models, etc. It supports right-censored simple and composite outcomes, with optional blanking periods and minimum observation periods.

Survival analysis on GAN-generated synthetic breast cancer data using Kaplan–Meier, Log-rank test, and Cox PH model.

Cardiovascular Disease Risk Prediction from Cardiac CT and Risk Factors Using Deep Survival Networks