Title: Methodological innovation in large-scale epidemiology
Funder: Medical Research Council

Abstract: Advances in biomedical and healthcare data science offer the potential of dramatic changes in the scale (size, breadth, depth and duration) and efficiency (data accumulation, storage, processing and dissemination) of clinical trials and observational studies. However, in order to maximise the scientific and public health returns from these new opportunities, it will be important to design systems and approaches that are focused on the key drivers of study quality and essential to ensure that their appropriate use is unencumbered by disproportionate regulatory or bureaucratic obstacles. Our programme comprises three, overlapping elements: Streamlining the design, conduct and analysis of population-based studies: We are developing and evaluating new methods for efficient recruitment (including use of routine clinical data), data collection (e.g. electronic case report forms, scalable approaches to assessment of cognitive function and physical activity), trial management (e.g. to maximise adherence to treatment, completeness of follow-up), quality assurance and trial oversight (including central statistical monitoring and rational pharmacovigilance), adjudication and clinical phenotyping (including theoretical and empirical research on the impact of different approaches on study results), data analysis (e.g. modular tools, test suites, ontological tools, and use of CDISC standards), and data sharing (e.g. clinical trial adverse events, controlled approaches for observational studies). Big Data for Population Health Research: This work centres on the collection, analysis and interpretation of population scale biomedical data. In conjunction with the University of Oxford Big Data Institute (www.bdi.ox.ac.uk) and using the streamlined approaches to building and managing large cohorts outlined above, this work focuses on new approaches to the measurement of health and disease. Examples include the use of genomics and immune profiling, imaging (e.g. morphology, digital pathology, radiology), mobile sensors and cameras (e.g. to assess activity and behavioural factors), and patient-oriented applications (e.g. to assess cognitive function, mood, quality of life, diet). Where appropriate, machine learning and other novel statistical approaches are used to analyse these complex, heterogeneous datasets and draw inferences about the causes and consequences, prevention and treatment of disease. Proportionate regulation of large-scale clinical trials and observational studies: This work seeks to promote the development and adoption of regulatory approaches that facilitate high quality clinical research. This work is founded on the key principles of protecting the rights, safety and well-being of study participants and ensuring the reliability of the study results (which influence the clinical care of future patients). For example, most clinical trial regulations and guidelines were written many years before the widespread use of information technology and the internet, and are no longer fit for purpose. We work with regulators, industry, academia and patient advocates to identify issues, develop recommendations, make the case for change, and encourage the adoption of more appropriate regulatory approaches in areas such as clinical trials, access to clinical data, and sharing of research data.


Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: GENERIC HEALTH RELEVANCE