Prediction Modelling (Winter School)
This 5 day course provides a comprehensive introduction to the fundamentals of clinical prediction modelling using modern statistical modelling techniques for health research. It will cover all steps of developing and accessing a prediction model. Computer based teaching introduces students the theory and practical implementation of cutting-edge predictive statistical and machine learning modelling techniques using the R statistical software.
Academic lead: Prof Daniel Stahl
Instructors: Dr Cedric Ginestet, Dr Raquel Iniesta, Dr Daniel Stamate (Goldsmiths), Dr Mizanur Khondoker (UEA), Mr Dominic Stringer, and Dr Deborah Agbedjro.
Date: 10th December – 14th December 2018
Venue: Institute of Psychiatry, Psychology and Neurosciences - Computer Rooms A & B
- External Early bird: £855 (till 18/10/18, price thereafter £950)
- KCL Staff Early bird: £641.25 (till 18/10/18, price thereafter £712.5)
- KCL Student Early bird: £427.5 (till 18/10/18, price thereafter £475)
- Other student Early bird: ££641.25 (till 18/10/18, price thereafter £712.5)
Last booking: 29th November 2018
BOOKING: To apply please email us with the following details:
- Email Subject Line: Application for Prediction Modelling 2018
- Email Address:
- Contact Phone Number:
- Are you affiliated with KCL and/or King's Health Partners?
- If Yes, indicate how you are affiliated with KCL and/or King's Health Partners
- Indicate your education/employee status: KCL PhD, KCL student, KCL staff, King's Health Partners affiliate, External Student or External
- In 100 words, state why you wish to enrol/participate in this course:
- In 100 words, state which skills you hope to acquire:
Once your application has been approved, you will be sent a link to payment and a discount code if one is to be applied.
This workshop will assume that participants have a good knowledge of regression analyses (as can be obtained from the BHI Introduction to Statistical Modelling Course in January) and some experience with R or any other syntax based statistical software, such as STATA (An introduction to R can be obtained from the BHI Introduction to Programming course running in October or the Intro to R course running in February 2019). Participants will need to bring their own laptop computer with R installed (http://www.r-project.org). We recommend to further install RStudio, a very handy user interface for R (free download from http://www.rstudio.com/)
Clinical prediction research develops models that try to predict the chances of a clinical outcome (such as death, diagnosis, treatment success or other future outcomes) based on characteristics related to the patient. Such models can be used to help clinician communicate the chances of clinical outcomes to their patients and to improve their management. It is therefore of crucial importance that such models are developed and tested appropriately. This 5 day course is aimed to PhD students and researchers in health research and will provide an introduction to key components of prognosis and stratified medicine research using cutting edge statistical and machine learning modelling techniques.
The course covers all major steps of developing and accessing a clinical prediction model, including study design and data preparation, the problem of over-fitting in regression models, how to overcome over-fitting using penalized regression and cross-validation methods, how to deal with missing data, feature variable selection, performance assessment and clinical usefulness of a model. An introduction to other machine learning techniques for prediction modelling, such as random forests and support vector machines, will be provided.Each day a short presentation of an application in prediction modelling will be presented. Teaching will be through lecturers and practical computer lab session interspersed with short presentations of prediction modelling researchers on current work. Practical sessions will involve the analyses and interpretation of practice datasets using the software R. Syntax of all procedures will be provided and explained but some familiarity with a syntax-based software (R, STATA, SAS) is advised. A short 1.5 h introduction to R will be provided at the beginning of the course
A good summary of building clinical prediction model is the paper "Towards better clinical prediction models: seven steps for development and an ABCD for validation" by E.W. Steyerberg and V. Vergouwe (2014) Eur Heart J. 35(29):1925-31. doi: 10.1093/eurheartj/ehu207. We will explain most concepts in details in the course.
An excellent introduction is provided by E.W. Steyerberg's 2009 book "Clinical Prediction models: A practical approach to development, validation and updating". New York: Springer. Another very useful textbook is Max Kuhn's 2013 book "Applied prediction modelling. New York: Springer. Both books present examples using R.
Subject-specific: Knowledge, Understanding and Skills
At the end of the course the students should be able to demonstrate subject-specific knowledge, understanding and skills and have the ability to:
- Have a good understanding of core clinical prediction concepts, such as prognosis, prognostic factors, prognostic models, and stratified medicine and will be able to apply this understanding to the design, conduct, and interpretation of clinical prediction modelling research studies;
- Be able to describe how modern statistical concepts, regression and machine learning methods can be applied in medical prediction problems;
- Be familiar with the principles that play a role in internal validation such as over-fitting, optimism and shrinkage and understand key components of internal validation methods such as cross-validation or bootstrapping;
- Be able to develop simple prediction models, assess their quality and validate them using R software;
- Be able to critically assess the general applicability of a developed model to predict future outcomes;
- Be equipped with a range of statistical and machine learning skills, including problem -solving, project work and presentation, which will enable students to take prominent roles in a wide spectrum of employment and research.
General: Knowledge, Understanding and Skills.
On successful completion of this course the student should be able to:
- to show initiative and the ability to work autonomously and independently with minimal guidance from others;
- to effectively communicate and critically assess own work in discussion groups;
- to successfully work in a team during computer group lab sessions;
- to show confidence in the use of programming software to implement prediction models.
Please note the following: Your place will not be confirmed until payment has been made. Failure to cancel without sufficient notice will forfeit your course fee. If you would like to pay by internal transfer, please contact us here.