Overview of Research at the MRC Biostatistics Unit
- Mission of the Unit
- The Unit's distinct role
- Research themes
Progress in biomedical science is increasingly dominated by quantitative inference in the presence of uncertainty, and this is the focus of biostatistical endeavour. Innovative statistical methods are required for issues that arise with:
- new sources of data: bioinformatics is a clear case in which large and complex data structures arise from, for example, functional genomic or microarray studies. Functional MRI and other imaging technologies also generate vast quantities of data and a demand for efficient and robust analysis.
- new forms of data collection: increased computerisation and systematization of data collection mean that what were previously considered as individual studies can now be linked and simultaneously analysed. Examples include the use of multiple performance indicators in monitoring a healthcare system, systematic reviews based on individual patient data, and linked longitudinal data for studies of disease natural history.
- new study designs: standard designs such as case-control studies or two-arm randomised trials are increasingly being supplemented by innovations such as life-course studies and cluster randomised trials, requiring appropriate methodology and guidance to practitioners.
- new application areas: for example, recent requirements by agencies such as the National Institute for Health and Clinical Excellence (NICE) for proof of cost-effectiveness has fuelled interest in long-term health economic evaluation of interventions, requiring substantial attention to statistical issues concerning models of disease processes, uncertainty in parameter estimates, and communicating uncertainty about cost-effectiveness.
All these developments present statistical challenges in design, analysis and presentation. In addition, classic problems such as missing data and measurement error apply in almost all areas and continue to present methodological challenges.
The Unit aims to maintain a balance between methodological development and application. The most useful new methodology is stimulated by applied studies, because attention is then focused on real problems rather than artificial abstractions, but this can still lead to generic ideas that are transferable between application areas. In turn, the Unit's involvement in applied studies is stimulated and enhanced by links to its methodological research programmes. Finally, efficient and wide-ranging dissemination is vital.
An additional generic research theme stems from the increasing reliance on 'probabilistic models' as a means of synthesizing all available evidence concerning a biomedical research problem or a health policy decision. Such models are particularly appropriate for complex policy decisions, and should encourage the use of all relevant information, and provide reasoned predictions and inferences about quantities of interest. A range of issues arises concerning, for example, the appropriate complexity of the model, incorporating parameter uncertainty, explanation of the conclusions in an accountable manner, ensuring robustness, and so on. The Unit is taking a leading role in the development of methods and software for handling both the construction and criticism of such models.
The Unit is an internationally acclaimed research institution with a primary emphasis on methodological research across a wide range of topics in medical statistics. It is also one of the largest groupings of biostatisticians in Europe. Its focus on medical applications makes it distinct from the vast majority of general statistical departments in UK universities. The Unit's methodologies are cross-disciplinary, and other jurisdictions that impact on public health are not ignored. Internationally, the Unit is one of only a few comparable centres for biostatistics research.
Historically, the strength of the UK medical research community has been based on sound collaboration between clinicians and statisticians, and the MRC (or its predecessor, the Medical Research Committee) has had a Statistical Unit, in various forms, since its inception in 1913-1914. We look forward to celebrating our centenary in 2013-2014.