Survival analysis in Julia
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Updated
Jun 7, 2024 - Julia
Survival analysis in Julia
kaplanmeier is an python library to create survival curves using kaplan-meier, and compute the log-rank test.
geneSurv: an interactive web-based tool for survival analysis in genomics research
ACM CHIL 2021: "Enabling Counterfactual Survival Analysis with Balanced Representations"
this repository hold the supporting code for the blog post
Survival modelling using Cox proportional hazard regression model
[Survival Analytics] Working with Dr. Surbhi Grover MD, MPH evaluating survival outcomes, treatment, and survivorship for a cervical cancer cohort with or without HIV co-infection in Botswana, Africa using methods rooted in statistics.
Data Set on Chilean Undersecretaries (1990-2022)
An introduction to the concepts of Survival Analysis and its implementation in lifelines package for Python.
Survival Analysis of Lung Cancer Patients
Data Set on Chilean Ministers (1990-2014)
Survival analysis functions that allow left truncation and weighting, including Aalen-Johansen, Kaplan-Meier, and Cox proportional hazards regression
IEEE TNNLS 2020: "Calibration and Uncertainty in Neural Time-to-Event Modeling"
Kaplan-Meier-Estimator also known as the product limit estimator.
Best practices for survival analysis at PNT Lab
Simple library to help calculate and graph survival curves.
ML models to predict the probability of patient survival based on various KPI's.
Applying KaplanMeierFitter model on Time and Events
Survival Analysis
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