Aims - This module aims to provide students with a basic understanding of the principles and methods underpinning statistical inference, with particular reference to the conduct of statistical analysis embedded in wider health economic evaluation (e.g. cost-effectiveness analysis). The module will cover the fundamental aspects of statistical distributions; probability calculus; parameter estimation; statistical testing; regression analysis. It is intended for students registered on the MSc Health Economics and Decision Science degree programme.

Objectives - On successful completion of the module, students should have an understanding of the essentials of statistical modelling, with specific focus on practical aspects and problems relevant to health economic modelling, an ability to make effective use of the R statistical software suite in order to visualize and analyse data typically encountered in modelling for health economic evaluation, an understanding of the differences in the major approaches to statistical inference, an understanding of the concepts underlying the philosophy and conduct of statistical analysis sufficient to follow other taught postgraduate level modules offered by the Department of Statistical Science in this application area, an ability to deploy these concepts in a practical context, performing basic computer-assisted statistical analyses and interpreting the results.

Applications - This module will focus on applications in medicine, public health, epidemiology and health services research, although the same skills are readily transferrable to a variety of other fields and applications.

Content - Basics of statistical inference; commonly used distributional assumptions; probability calculus vs statistical inference; methods for point estimates (including basic concepts in Bayesian modelling and maximum likelihood estimation); interval estimates; principles of hypothesis and significance testing; basics of and rationale behind regression modelling.

Key texts - Available from ReadingLists@UCL.