This 5 day course is aimed at PhD students and researchers in clinical prediction modelling in medical research. The course provides a comprehensive introduction to the fundamentals of prognosis and stratified medicine research. It will cover all steps of developing and assessing 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. The course will be of interest to statisticians, researchers, PhD students and medical professionals who wish to acquire and/or consolidate knowledge and skills necessary for medical research.
Autumn School 2017
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Prediction modelling and personalised medicine in medical research using modern statistical methods
(3rd Edition)
For all Inquiries about the Autumn School please email: daniel.r.stahl@kcl.ac.uk
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You can book your place here
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Statistical Softwares
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 2 h introduction to R will be provided at the beginning of the course
Requirements
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This course will assume that participants have a good knowledge of regression analyses and some experience with R or any other syntax based statistical software, such as STATA. 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/
Instructors
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Dr. Daniel Stahl (King's College London)
Dr. Raquel Iniesta (King's College London)
Dr Cedric Ginestet (King's College London)
Ms. Deborah Agbedjro (King's College London)
Dr. Daniel Stamate (Goldsmiths Univeristy)
Dr Mizanur Khondoker (University of East Anglia)
Mr Dominic Stringer (King's College London)
Mr Ewan Carr (King's College London)
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Teaching Assistants: Dr. John Hodsoll, Mr. Ben Clapperton, Mr. Hamel Patel
Guest speakers: Dr. Paulo Fusar-Poli, Dr. Honghan Wu, Mr. Ross Willams (Manchester University)
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Science communication (video-interviews producer and social media):
Dr Raquel Iniesta
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BHI Education Team: Ms. Zahra Abduallh
Administration: Ms Naomi Cockshutt and Ms. Josephine Mumford
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Fees, date & venue
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Fees:
The course fee is £900 for non-IoPPN members. This fee does not include travel, accommodation and subsistence which delegates need to arrange for themselves.
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The course lasts 5 days:
October 30th, 31st & November 1st, 2nd, 3rd 2017
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At:
Institute of Psychiatry, Psychology & Neuroscience
King's College London
16 De Crespigny Park
London SE5 8AF
+44 (0) 20 7848 0002
(Room TBC)
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Course Description
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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 mental health and will provide an introduction to key components of prognosis and stratified medicine research using cutting edge statistical and machine learning modelling techniques.
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Content Overview
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The course covers all major steps of developing and assessing 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 using random forests, and performance assessment and clinical usefulness of a model. 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. The schedule is available.