External Courses & Workshops

Some members of the MRC Biostatistics Unit teach at externally led courses and workshops on statistics. Please see below for further details.


RSS Pre-conference course: 'Using simulation studies to evaluate statistical methods in Stata'

5th September 2016

Location: University of Manchester, University Place, Oxford Road, M13 9PL
CPD: 6 hours
Level: Professional

To be delivered by Ian White from MRC Biostatistics Unit


Simualtion studies are an important tool for statistical research. They help us understand the properties of statistical methods and compare different methods. To perform a meaningful simualtion study, careful thought needs to be put into the design, coding, analysis and interpretation.

For further information and to register for this course, please click here


ICSB Pre-conference Course: 'Demystifying causal inference in randomised trials'

21st August 2016

Location: Birmingham (pre-conference course for 37th Annual ICSB Conference)

To be delivered by:

Ian White, MRC Biostatistics Unit, Cambridge
Graham Dunn, The University of Manchester
Sabine Landau, King’s College London
Richard Emsley, The University of Manchester


Randomised trials provide a gold standard design for assessing the effectiveness of an intervention or treatment, based on an intention to treat analysis. However, this suffices to only answer a narrow question about the effectiveness of offering the intervention, based on comparing the average outcome between randomised groups. Other important questions include “what is the effect of actually receiving the intervention?” and “how does the intervention work?”. To answer these questions, we require different analysis approaches, using methods drawn from the causal inference literature.

This course aims to introduce participants to the concepts of causal inference in randomised trials and the statistical methods used to answer various causal questions. It will focus on worked examples from different clinical areas, modelling issues and the key assumptions, and how these methods can be implemented in standard statistical software. No previous experience of causal inference or prior knowledge of any particular software package is required.

The morning session will give an introduction to the terminology of causal inference, the analysis of randomised trials following the intention-to-treat principle, and the problem caused by departures from randomised allocation. We will introduce alternative estimands including the complier average causal effect, and we will show how these can be estimated by two broad classes of methods: instrumental variables methods, which use the randomisation to estimate a causal model, and inverse probability weighting methods, which censor data after departures and then correct for selection bias under a no unmeasured confounders assumption.

The afternoon session will introduce the concept of mediation analysis, describing both its potential and outlining the major difficulties. We will introduce approaches that can deal with measured post-randomisation confounders and hidden confounding in trials: these extend the inverse probability weighting methods and the instrumental variables methods of the morning.

For further information and to register for this course, please click here