Overview

This introductory course is for anyone wishing to understand how causal questions can be investigated using real world data (RWD), that is data on the everyday experiences of individuals that are collected through surveys, cohort studies, administrative and clinical databases or accrued for reasons other than research. These data are observational, as opposed to experimental. Because of this, using them to address causal questions raises many concerns and difficulties. In this course we will describe the main sources of bias affecting RWD and possible strategies to deal with them. 
 
The course will start by distinguishing between different types of studies (e.g., RCTs, cross-sectional and longitudinal) and data sources (e.g., research-based, administrative databases).  It will then describe the sources of bias that are likely to affect observational data, in particular those arising from the non-randomized allocation of exposures (denoted confounding bias in epidemiology and selection bias in the social sciences), from missing participation (including missing data), and from measurement errors. We will then introduce two main design-based approaches to attempt dealing with (some of) these biases: the framework of target trial emulation and the exploitation of natural experiments. 

This course is specifically designed to enhance knowledge, self-confidence and expertise among researchers who wish to develop their skills in analysing complex longitudinal biosocial data. It makes key concepts and approaches accessible to quantitative researchers from a wide range of disciplines and sectors, with particular emphasis on how these can be practically applied in their work. It is part of the RADIANCE programme, which has been developed by an expert team from UCL and the University of Manchester to offer comprehensive, state-of-the-art longitudinal data science training all in one place.

Who this course is for

This course is aimed at quantitative researchers working with biomedical and social data who wish to use causal questions using real world data their research and would like a short introduction to this topic.

Course content

This course will cover an introduction to the following topics:

• different types of studies (e.g. RCTs, cross-sectional and longitudinal) and data sources (e.g. research-based, administrative databases).
• the sources of bias that are likely to affect observational data, in particular those arising from the non-randomized allocation of exposures, from missing participation, and from measurement errors.
• two main design-based approaches to attempt dealing with (some of) these biases: the framework of target trial emulation and the exploitation of natural experiments.

Teaching and structure

The course consists of three pre-recorded lectures, four computer practicals and solutions.

Certificates

A certificate of completion will be available to download once the required activities have been completed.

Learning outcomes

By the end of this course you will be able to:

• identify the most appropriate study design and data source for developing an observational study
• recognise the main challenges arising from using real-world data for investigating causal questions
• understand some of the approaches that can be taken to mitigate these challenges

Cost and concessions

The standard fee for this course is £175.

A 10% discount is available for UCL students and staff – please email radiance@ucl.ac.uk for details.

Course team

Prof Bianca De Stavola

Bianca is a Professor of Medical Statistics at UCL. Bianca’s areas of expertise include causal inference, analysis of longitudinal and survival data, growth modelling, and life course epidemiology.

Dr Eduardo Fe

Eduardo is a Senior Lecturer in Social Statistics at the University of Manchester. Eduardo’s areas of expertise include Causal Inference; Nonparametric Methods (Estimation and Inference of Conditional Moments; Bootstrapping); Panel Data; Labour and Experimental Economics. 

Eleanor Lob

Eleanor is a Teaching Fellow in Quantitative Research Methods at UCL. Eleanor’s areas of expertise include casual inference analysis of longitudinal biosocial data and innovative data science methods.

Course information last modified: 25 Jan 2024, 17:22