An introduction to Survival Analysis

An introduction to Survival Analysis

Mathieu Ribatet Mathieu Ribatet

Mathieu Ribatet

Data scientist - Full professor of Statistics / Machine Learning

发布日期: 2023年2月28日
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Lecture series about #statisticalmodelling again (and again). A completely different topic (but very fascinating): survival analysis. #Survival analysis is the framework to model time to event, and as so, widely applied in #clinicalresearch or #reliability. This lecture is an (rather long) introduction to this topic, and it will cover:

  • specificities of time to event data
  • different type of censoring and useful quantities
  • non-parametric estimation of the survival function (Kaplan-Meier, life table, Flemming-Harrington)
  • Comparing survival curves, i.e., log-rank based hypothesis testing
  • Dealing with covariates using the Cox proportional hazard rate model
  • Having fun by creating our own (censored) dataset, i.e., we're all embarked on the Titanic which is about to sink... Hold your breath.

You can get the course material and hope it will be useful.

Jordi Cabot

Head of the Software Engineering RDI Unit at LIST. FNR Pearl Chair. Affiliate Professor in CS at University of Luxembourg. Combining modeling, low-code, OSS and AI to make Better Software Faster.

1 年

We probably use a too simplistic approach but you may find curious how we applied survival analysis to the world of open source: https://livablesoftware.com/survival-rate-github-projects-empirical/

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Mathan Raj RAVI (PMP)?

2 年

It is interesting to analyze the probability of survival.

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