Simple library to help calculate and graph survival curves.

A machine learning project focused on predicting chronic kidney disease (CKD) stages and performing survival analysis using clinical biomarkers. It utilizes the Kaplan-Meier estimator to analyze patient progression and visualize survival probabilities, offering insights into CKD management.

ML models to predict the probability of patient survival based on various KPI's.

Analyzed employee turnover (Jan 2022 - Mar 2023) at my former organization, considering trends, departmental attrition, and tenure insights. Used predictive analytics from the 2022 Employee Engagement Survey to identify groups with flight risk. Incorporated Survival Analysis for temporal patterns, guiding decisions to improve retention.

🚙 Comprehensive driver risk analytics using Cox proportional hazards (C-index: 0.79) and Bayesian hierarchical models (91.4% accuracy) ⚡ Production-ready system with real-time scoring for 300K+ drivers, SHAP explainability, and full Docker/Kubernetes deployment stack

Frequency Table, Chi-Squared & ANOVA Test, KM Model, Median Time Comparison, Log-Rank & Wilcoxon Test, Tukey Multiple Comparison, Immortal Time Bias, Cox Model, Proportional Hazards Assumption Tests, Supremum Test for Functional Form. *NCDB data is publicly available. Team members: Kah Meng Soh, Dr. Lynette Smith, Dr. Sharma Smriti, Dr. Apar Ganti.

Coursework, Stata code, and notes for PBHS 32700: Biostatistical Methods (Spring 2024, University of Chicago). Topics include contingency tables, logistic regression, Poisson and negative binomial models, and survival analysis using Kaplan-Meier, Cox, and parametric models. The course emphasizes categorical and time-to-event analysis using Stata.

Multi-agent AI system for evidence-based oncology clinical decision support with physician oversight – Kaggle AI Agents Capstone 2025

A preprocessor to construct medical history table from data source

Survival Analysis on the patients from a trial of laser coagulation for the treatment of diabetic retinopathy. Survival times in this dataset are actual time to blindness in months, minus the minimum possible time to event (6.5 months).

End-to-end workflow on synthetic accelerated life test (ALT) data: dataset generation, Kaplan–Meier survival analysis, Weibull-2P modeling, and Arrhenius temperature acceleration. Includes Py scripts, Jupyter notebooks, plots, and CSV outputs.

Small-sample bias of the Kaplan-Meier Estimator

this repository hold the supporting code for the blog post

Create the covariate-adjusted Kaplan-Meier and cumulative incidence functions

This is a practice of survival analysis concepts on an existing dataset, analysis using R code and interpretation. Suggestions for improvement always welcome.

A survival analysis study of ovarian carcinoma patients involved in clinical trials using R

Repository with the Gofcens R package. The package contains Goodness-of-Fit Methods for Complete and Right-Censored Data.

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 repository contains the exercises and workshops of the survival analysis classes based on the book by Peter J. Smith and the lecture notes of Luis Guillermo Monroy Diaz (UNAL).