Dear Allstat members,
We are offering our popular 5 day “Introduction to prediction modelling” course from 19th to 14th December 2018 at the Institute of Psychiatry, Psychology and Neuroscience, King’ s College London.
For more information and course fees please see: https://www.kcl.ac.uk/ioppn/depts/biostatisticshealthinformatics/teaching/courses/prediction-modelling-.aspx
COURSE AIM
To provide 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.
Requirements: This workshop will assume that participants have a good knowledge of regression analyses and some experience with R <https://www.r-project.org/> or any other syntax based statistical software, such as STATA. An introduction to R can be obtained from the Biostatsitics and Health Informatics “Introduction to Programming” course<https://www.kcl.ac.uk/ioppn/depts/biostatisticshealthinformatics/teaching/courses/introduction-to-programming.aspx> running in October or the BHI “Intro to R” course<https://www.kcl.ac.uk/ioppn/depts/biostatisticshealthinformatics/teaching/courses/intro-to-r.aspx> 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/)
CONTENT OVERVIEW
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, and 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
LEARNING OUTCOMES
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.
Kind regards,
Daniel
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Daniel Stahl, PhD
Professor of Medical Statistics and Statistical Learning
Lead of Precision Medicine and Statistical Learning Group<https://www.kcl.ac.uk/ioppn/depts/biostatisticshealthinformatics/research/precision-medicine-and-statistical-learning/index.aspx>
Department of Biostatistics and Health Informatics,<https://www.kcl.ac.uk/ioppn/depts/biostahttps:/www.kcl.ac.uk/ioppn/depts/biostatisticshealthinformatics/index.aspxtisticshealthinformatics/index.aspx/kclad.ds.kcl.ac.uk/anywhere/UserData/PSStore01/spakdas/My%20Documents/backup> S2.05
Institute of Psychiatry, Psychology & Neuroscience, King's College London
De Crespigny Park, Box PO20
London SE5 8AF
Email: [log in to unmask]<mailto:[log in to unmask]>
Tel: 0207 848 0964
Personal Webpage<https://kclpure.kcl.ac.uk/portal/daniel.r.stahl.html>
********* Winter School “Prediction modelling”<https://www.kcl.ac.uk/ioppn/depts/biostatisticshealthinformatics/teaching/courses/prediction-modelling-.aspx> from 10.12.-14.12.2018 ******************
Upcoming Biostatistics and Health Informatics courses 2018/19<https://www.kcl.ac.uk/ioppn/depts/biostatisticshealthinformatics/teaching/upcoming-courses.aspx>
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