Event History Analysis

RADIANCE courses are targeted to the broad community of UK data scientists working in public health. They include epidemiologists, clinicians, data engineers/informaticians, statisticians, as well as quantitative researchers from other disciplines (e.g. psychology, social sciences, health economics).

They may be from academia, charities, government departments and non-profitable organisations.

Lecturers

Paola Zaninotto and Feifei Bu

Course description

This course is for anyone wanting to learn to use statistical methods that are designed to describe, explain or predict the occurrence of events such as death, incidence disease, time to employment etc. The course will cover censoring, Kaplan-Meier methods and Cox regression methods. It will also cover how to model recurrent events and competing events.

Learning objectives

By the end of this course you will be able to:

• Describe and model time to single event data
• Model recurrent events taking account of clustering within individuals (random effects/frailty models)
• To correctly estimate marginal probability of an event in the presence of competing events

Course Structure

There will be two online live lectures sessions and two online live computer practical sessions in R or Stata.

Pre-requisite

Familiarity with Stata or R, statistical inference and regression models. Attendees are encouraged to watch

Longitudinal Data Structures

Hypothesis testing and P-values

Sampling

Fee: Free

More information

This is a UKRI funded project offering rigorous training in longitudinal data science. Please note that this training is NOT available to undergraduate or masters students.

Know what’s Happening Next — before everyone else does.

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