Prediction modelling is distinct from statistical modelling in that the primary objective is to deliver models that make accurate predictions, rather than estimate parameters such as risk factors. This course is for you if you want to:
Have a good understanding of core clinical prediction concepts, such as prognosis, prognostic factors, prognostic models, and stratified medicine and want to be able to apply this understanding to the design, conduct, and interpretation of clinical prediction modelling research studies, and the critical appraisal of research papers.
Be able to describe how modern statistical concepts, regression and machine learning methods can be applied to 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 and 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, which will enable you to take prominent roles in a wide spectrum of employment and research.