Bristol Medical School

About the course

Trusted Research Environments (TREs) are in many cases now the default pathway to accessing data, representing a step change in how data is accessed for research and introducing new challenges.

This course will introduce some exemplar TREs (e.g. The UK Longitudinal Linkage Collaboration (UK LLC), NHS England TRE and OpenSAFELY) to highlight the different ways TREs function, and some of the strengths and challenges these environments can introduce to research such as the availability of different tools.

We aim to equip researchers with the confidence to work within TREs through awareness of the benefits, challenges and differences that exist across TREs. The course is intended for researchers who want to learn more about conducting research within a TRE.

What this course will cover

• Different approaches to working within TREs
• Insights into the strengths and challenges of different tools that can be used to manipulate data, conduct analyses, and create safe research outputs
• The steps that need to be taken before analysis can be undertaken and once analysis is complete including quality assurance, requesting safe research outputs
• Governance and data standards
• Common approaches to dealing with known challenges
• The importance of patient and public involvement and engagement including fair processing and transparency
• The benefits of developing good practice in documentation not least to support reproducible research

How to join

Please note, there are pre-requisites for enrolling on this course – details can be found on the full course listing linked below.

Sign up via the University of Bristol Medical School website

About the course

Health research is increasingly turning to high-throughput molecular datasets (also known as ‘omic’ datasets) to discover novel biomarkers of disease risk and outcome. Unfortunately, the size and complexity of these datasets makes them difficult to manage and prone to many pitfalls.

In this course, we introduce you to the latest approaches from data science for interpreting and extracting useful and reliable biomarkers from these challenging datasets. It aims provide:

• An overview of the principles and methods of epidemiology and data science that are relevant to high-throughput omic studies
• The knowledge and skills necessary to design and utilize population-based omic studies to gain insight and to derive robust biomarkers of exposures and health outcomes

Attendees may have a background in epidemiology, genetics, statistics, public health or a clinical speciality. A basic knowledge of epidemiology is required and some understanding of molecular epidemiology terminology and machine learning would be advantageous. Practical knowledge of R is required as students will be processing large omic datasets in practical sessions.

What this course will cover

• Examples of published omic analyses and models for epidemiological and medical applications

• Statistical methods for preprocessing, discovering patterns and testing associations in omic datasets

• Interpreting the biological relevance of omic patterns and associations

• Estimating the heritability and proportion of variation explained by omic data

• Approaches from machine learning for deriving reliable omic biomarkers for indexing exposures and predicting health outcomes

• Application and interpretation of appropriate metrics for evaluating biomarker performance

• Ethical challenges of developing, interpreting and applying molecular biomarkers

How to join

Please note, there are pre-requisites for enrolling on this course – details can be found on the full course listing linked below.

Sign up via the University of Bristol Medical School website