28 results for “topic:cox-proportional-hazard”
Survival Analysis of Lung Cancer Patients
A Python distribution of iCARE, a tool for individualized Coherent Absolute Risk Estimation.
KM plots and Cox Proportional Hazards model for feature selection
Breast Cancer Survival Analysis using SAS on METABRIC dataset to identify key survival factors with Kaplan-Meier, Cox models, and gamma distributions
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
A comprehensive end-to-end survival analysis project using classical and deep learning models with clinical data, including preprocessing, modeling, evaluation, and interpretability.
CoxKAN: Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival Analysis
Methodology research comparing statistical and ML methods of competing risks analysis
Exploring disparities in the COMPAS algorithm: an analysis of recidivism predictions among demographic groups.
Federated algorithm for coxph in Vantage6 v4
Predicting survival for out of sample data
No description provided.
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A deep partially linear regression to estimate the change point and its linear and nonlinear effects.
This repository contains Python code for performing Cox proportional hazards model analysis tailored to crossover study designs.
No description provided.
Biostatistics internship on proteomics and cancer avoidance
A JavaScript wrapper for the WebAssembly module of iCARE Python (pyicare) package.
Prognostic significance of sarcopenia in urothelial cancer
Cardiovascular Disease Risk Prediction from Cardiac CT and Risk Factors Using Deep Survival Networks
Survival analysis of stomach cancer, using TCGA gene expression data.
Analyzed user events from a leading scheduling SaaS platform to uncover what drives activation, engagement, and subscription [Part 2]
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.
This is a practice of survival analysis concepts on an existing dataset, analysis using R code and interpretation. Suggestions for improvement always welcome.
Machine learning for predicting negative cardiac outcomes in patients with atrial fibrillation (AF)
Statistical models and machine learning models on health care data
Code for: The Cox-Polya-Gamma Algorithm for Flexible Bayesian Inference of Multilevel Survival Models. Weibull PH model based on Cox-PG algorithm has been added (25JAN2026).