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Survival Analysis (Time-to-Event Data): an Introduction

  • 5 hours
  • 1 day

Overview

This one-day course for non-statisticians will introduce you to the concept of modelling time-to-event data ('survival analysis').

Survival/time-to-event analysis is appropriate when the outcome of interest is an event and that event has not occurred for everyone in the dataset. The outcome can be something negative (e.g. death, recurrence of a tumour) or something positive (e.g. recovery, task completion).

The simplest analysis is the log-rank test that assesses differences according to a single factor. Cox proportional hazards regression is appropriate to investigate the rate at which the event occurs according to several potential predictors. Both will be covered in this course.

This course is run by UCL's Centre for Applied Statistics Courses (CASC) - part of the UCL Great Ormond Street Institute of Child Health (ICH).

Course content

This workshop will cover the following topics:

  • Use of Kaplan-Meier plots
  • Life tables
  • Cox regression analyses
  • Hazard ratios
  • How to set up the data for analysis
  • Including interaction terms in the models
  • Assessing model outliers
  • Interpretation of SPSS output

Software used

Please note that there will be no actual computer work during the workshop. Although SPSS is used for illustration purposes, the workshop is suitable if you're using other software packages.

The datasets can be taken away and models fitted using alternative software.

Who this course is for

The course is for non statisticians and is open to anyone who wants to learn about the basics of survival/time-to-event analysis.

Knowledge of the very basics of statistics (such as means, standard deviation, confidence intervals, etc.) is beneficial.

Learning outcomes

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

  • recognise the applicability of time-to-event/survival analyses
  • understand censoring
  • understand how life-tables and Kaplan-Meier plots are constructed
  • understand the applicability and limitations of log-rank testing
  • have a basic understanding of Cox proportional hazards regression
  • compare Cox models and identify outliers
  • know how to set up a dataset for time-varying covariates

Cost and concessions

The fees are as follows:

  • External delegates (non UCL) - £150
  • UCL staff, students, alumni - £75*
  • ICH/GOSH staff and students - free    

* valid UCL email address and/or UCL alumni number required upon registration

Prices include printed course materials, refreshments (and lunch for non-ICH  participants).

Certificates

You can request a certificate of attendance for this course once you've completed it. Please send your request to ich.statscou@ucl.ac.uk

Include the following in your email:

  • the name of the completed course for which you'd like a certificate
  • how you'd like your name presented on the certificate (if the name/format differs from the details you gave during registration)

Cancellations

We accept cancellations up to five working days before the start of the course with a full refund, though we'd appreciate as much notice as possible to re-allocate the place. Places cancelled or changed after this point won't be eligible for a refund. Please send all cancellation requests directly to the course administrator.

Find out about other statistics courses

CASC's stats courses are for anyone requiring an understanding of research methodology and statistical analyses. The courses will allow non-statisticians to interpret published research and/or undertake their own research studies.

Find out more about CASC's full range of statistics courses, and the continuing statistics training scheme (book six one-day courses and get a seventh free.)

Sign up for short course announcements: Subscribe to the UCL Life Learning newsletter to receive news and updates on courses in your chosen area. (For updates on a specific course, contact the administrator - see 'Contact information'.)

Course team

Eirini Koutoumanou - Course Lead

Eirini joined GOS ICH in 2008 as the first CASC Teaching Fellow and was promoted to Senior Teaching Fellow in 2014. She has a Bachelor’s degree in Statistics from the Athens University of Economics and Business and a Master's degree in Statistics from Lancaster University, and she's currently studying for a PhD at ICH. Eirini started teaching at ICH straight after her student days, putting into practice and further developing her passion for statistics teaching. She's played an instrumental role in the formation of CASC and hopes to see it develop further.


Dr Dan Green - Course Lead

Dan joined GOS ICH in May 2017 as a Teaching Fellow in CASC. He has a Bachelor’s degree in Mathematics and a Master's degree in Medical Statistics from University of Leicester. He was awarded a NIHR Research Methods Fellowship, hosted at the Arthritis Research UK Primary Care Centre within Keele University. He started a PhD at the same department in 2012, with a NIHR School for Primary Care Research Studentship, exploring hand symptoms over a six year follow-up using latent-related methodology. During his six years at Keele, Dan was involved in numerous applied observational studies and a clinical trial that included a variety of statistical methods, with specialties including latent class analysis (and extensions), STATA programming and survival analysis.


Student review

"A well-taught and helpful course. Thank you!”

“Excellent course. Only stats course I have understood all the way through. Angie Wade was a great teacher - very clear. Both tutors very approachable with queries about specifics of my project. Thank you very much.”

“Enjoyed the course - the course administrator was very good and explained it well. I feel that I have gained from going on this course.”

“Very enjoyable, good value for money.”


Course information last modified: 10 Jan 2019, 14:04

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