This module will introduce you to a range of more advanced statistical techniques used in healthcare. In particular you will learn advanced modelling methods, including semi-parametric survival models and generalised linear models. Incomplete data is a common problem in healthcare data, so you will study methods for handling missing data, with a focus on multiple imputation. We will also discuss how conclusions about causality can be drawn from observational data using methods such as directed acyclic graphs and propensity scores. You will also learn about bootstrapping and other related methods.

At the end of the module you will:


	Be able to use linear regression, logistic regression and Poisson regression within a GLM framework
	
	
	Be able to apply Cox proportional hazards and Weibull modules for time-to-event data
	
	
	Understand and be able to apply the MAR/MCAR/MNAR framework for missing data
	
	
	Be able to use multiple imputation
	
	
	Be able to use directed acyclic graphs
	
	
	Be able to use propensity scores
	
	
	Be able to use bootstrap methods
	
The module is delivered through web-based distance learning in the UCL Virtual Learning Environment plus a 3-day face-to-face teaching session.

This module is intended to be a ‘synthetic chemistry bootcamp’ whereby students with differing levels of experimental chemistry experience can...

This module is intended to be a ‘synthetic chemistry bootcamp’ whereby students with differing levels of experimental chemistry experience can learn skills and techniques that will raise them all to a similar level and have the confidence to conduct their own synthetic chemistry research with minimal supervision.

The theoretical content of this module will be delivered through a series of laboratory demonstrations and lectures; students understanding of the theory will be tested through group work in the lectures and some online tutorial workThe module will run in Term 1, in order to prepare the students for the practical demands of their independent Research Project.

Intended learning outcomes include:

LO 1; understand the interaction between scientific theory and experimentation. Have a good knowledge and understanding of some of the experimental synthetic techniques commonly used in inorganic, materials and organic synthetic chemistry.

LO 2; be able to apply these methodologies to conduct chemical syntheses and analysis in a research laboratory environment.

LO 3; be able to design and carry out scientific experiments and accurately record and independently analyse the results of these experiments.

LO 4; know how to access, search and use the chemical literature to help with this.

LO5; be aware of the safety issues in chemical research. Be able to complete COSHH and risk assessments.

Students will be assessed by a practical exam that incorporates all of these learning outcomes.