Log in
The statistical analysis of large datasets has contributed to the rehabilitation of the Ross procedure (the replacement of a failing aortic valve with the patient's own pulmonary valve) for specific patient groups, such as those above 50 years old who want to avoid daily anticoagulation treatment, and those with a reduced life span, especially patients on dialysis. The results of the research have (a) contributed to changes in the current practice guidelines of the European Society of Cardiologists and (b) have shown that, in contrast to previous beliefs, the Ross procedure can still be safely performed when the aortic valve malfunctions.
Professor Hutton has applied her research on statistical models for survival analysis to cerebral palsy, a neurological disorder which afflicts around 1 in 500 of newborn children globally. The body of research has established medically-accepted norms for the life expectancy of people with cerebral palsy. Her research extends to the study of life expectancy for patients suffering from spinal cord injuries.
The impact of this work has been internationally substantial, influencing medical and legal professionals, and informing lay people with involvement in cerebral palsy. Her work is also widely cited by patient-networks and textbooks.
Hutton is regularly called by both defence and plaintiff lawyers, as an expert witness worldwide, assessing life expectancy for damages arising from negligence in obstetric or paediatric care, or from accidents. Her expertise is also used in brain and spinal cord injury cases, which also result in substantial awards. The award of appropriate damages in legal cases ensures that patients receive the best care for the rest of their lives. From Jan 2008 to July 2013 Hutton has provided expert evidence in 103 such cases around the world, which had impact on decisions about compensation totalling in the range £100M-450M.
Patients are more likely to get the most effective healthcare, at affordable cost to the NHS, as a result of research methodology, developed by researchers at the University of Bristol, that allows the efficacy and cost-effectiveness of multiple treatment options to be compared, based on all the available evidence, much more efficiently than in the past. Since 2008, these methods have been used to inform Clinical Guidelines issued by the National Institute for Health and Care Excellence (NICE) and in submissions to NICE's Technology Appraisals. Guidance in NICE's Technology Appraisals is mandatory and therefore impacts directly on clinical practice. The methodology is used in decision making by NICE's equivalents in other countries including Canada, Germany, and South Korea, and by consultancy firms that conduct analyses for pharmaceutical companies.
The WinBUGS software (and now OpenBUGS software), developed initially at Cambridge from 1989-1996 and then further at Imperial from 1996-2007, has made practical MCMC Bayesian methods readily available to applied statisticians and data analysts. The software has been instrumental in facilitating routine Bayesian analysis of a vast range of complex statistical problems covering a wide spectrum of application areas, and over 20 years after its inception, it remains the leading software tool for applied Bayesian analysis among both academic and non-academic communities internationally. WinBUGS had over 30,000 registered users as of 2009 (the software is now open-source and users are no longer required to register) and a Google search on the term `WinBUGS' returns over 205,000 hits (over 42,000 of which are since 2008) with applications as diverse as astrostatistics, solar radiation modelling, fish stock assessments, credit risk assessment, production of disease maps and atlases, drug development and healthcare provider profiling.
Reversible Jump Markov chain Monte Carlo, introduced by Peter Green [1] in 1995, was the first generic technique for conducting the computations necessary for joint Bayesian inference about models and their parameters, and it remains by far the most widely used, 18 years after its introduction. The paper has been (by September 2013) cited over 3800 times in the academic literature, according to Google Scholar, the vast majority of the citing articles being outside statistics and mathematics. This case study, however, focusses on substantive applications outside academic research altogether, in the geophysical sciences, ecology and the environment, agriculture, medicine, social science, commerce and engineering.
Cardiovascular disease is a major worldwide health issue and cholesterol has long been recognised as an important risk factor. The Robertson Centre for Biostatistics (RCB), led by Prof. Ian Ford, has played a central role in establishing for the first time the benefits of statins in preventing first-time heart attacks in men, with subsequent major influence on medical practice and guidelines for patient care. Innovative record linkage techniques used by the RCB have identified the long-term benefits of treatment, confirmed safety, and quantified the economic benefits.
The Computational Optimization Group (COG) in the Department of Computing produced new models, algorithms, and approximations for supporting confident decision-making under uncertainty — when computational alternatives are scarce or unavailable. The impact of this research is exemplified by the following:
Our research has been applied directly by Aviva plc. to develop improved products in the general insurance market (e.g. household and car) and in the more specialised area of enhanced pension annuities. As a result, Aviva has become more competitive in these markets and customers are enjoying better value for money. In the case of enhanced annuities, the benefits are in the form of higher pension income for those accurately identified as facing shortened life expectancies. Aviva is the largest insurance company in the UK and the sixth largest in the world.
Research on novel statistical methods for disease surveillance and influenza vaccine effectiveness has led to the development of a suite of automatic systems for detecting outbreaks of infectious diseases at Health Protection Scotland (HPS). This work has improved the public health response and helped to reduce costs in Scotland and also in the wider UK and EU by providing real-time early warning of disease outbreaks and timely estimates of the effectiveness of the influenza vaccine. This research, commissioned by the Scottish Government, through HPS, and also the UK National Institute for Health Research (NIHR) and the European Centres for Disease Control (ECDC), but used in a wider context by many others, formed the basis for the HPS response to the H1N1 Influenza Pandemic and monitoring of the effects of Influenza Vaccines.
In a series of papers published from 1999 on, Aitken (Maxwell Institute) and collaborators applied Bayesian statistics to develop a methodology for the quantification of judicial evidence derived from forensic analyses. They proposed and implemented procedures for (i) determining the optimal size of samples that should be taken from potentially incriminating material (such as drugs seized); and (ii) the estimation of likelihood ratios characterising evidence provided by multivariate hierarchical data (such as the chemical composition of crime-scene samples). Their procedures have been recommended in international guideline documents (including a 2009 publication by the United Nations Office on Drugs and Crime) and have been routinely used by forensic science laboratories worldwide since 2008. The research has therefore had an impact on the administration of justice, leading to a better use of evidence and accompanying judicial and economic benefits. Examples are given from laboratories in Australia, Sweden and The Netherlands.