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Department of Statistics

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Penny Montague
Tel: +44 (0)20 7955 7511

Email: p.montague@lse.ac.uk

 

Department of Statistics Impact Case Studies

Dr Wicher Bergsma
Social Statistics group
Email: w.p.bergsma@lse.ac.uk
Personal webpage
Social Statistics group research website

Title of case study: Making new drugs safer and faster to develop

Summary of the impact:
According to the Association of the British Pharmaceutical Industry (ABPI), on average it takes more than 12 years and £1 billion to research and develop a new medicine suitable for public use. Beginning in 2008, LSE Associate Professor of Statistics Wicher Bergsma led research with GlaxoSmithKline exploring how state-of-the-art statistical modelling techniques developed at LSE could improve the effectiveness of vaccine clinical trials. Applied to the meningococcal clinical trial data, Bergsma’s novel use of marginal modeling revealed significantly more information for more trial symptoms, including pain, redness and irritability, than did traditional methods. The results of Bergsma’s research could allow pharmaceutical companies to develop a more accurate ‘risk profile’ for vaccines. This includes a better understanding of side effects which can lead to benefits for patients; in particular, drug prescription can be better tailored to patients’ needs.

Please view the full impact case study here.
Please view a summary of the case study here and a list of relevant resources here (LSE Research Impacts website)


Professor Qiwei Yao
Time Series and Statistical Learning group
Email: q.yao@lse.ac.uk 
Personal webpage
Time Series group research website

Title of case study: Helping Barclays meet the new Basel III regulation rules

Summary of the impact:
In response to the deficiencies in bank risk management revealed following the 2008 financial crisis, one of the mandated requirements under the Basel III regulatory framework is for banks to backtest the internal models they use to price their assets and to calculate how much capital they require should a counterparty default. Qiwei Yao worked with the Quantitative Analyst - Exposure team at Barclays Bank, which is responsible for constructing the Barclays Counterpart Credit Risk (CCR) backtesting methodology. They made use of several statistical methods from Yao’s research to construct the newly developed backtesting methodology which is now in operation at Barclays Bank. This puts the CCR assessment and management at Barclays in line with the Basel III regulatory capital framework.

Please view the full impact case study here
Please view a summary of the case study here and a list of relevant resources here (LSE Research Impacts website)


Professor Leonard Smith
Centre for the Analysis of Time Series (CATS)
Email: l.smith@lse.ac.uk
CATS website

Title of case study: Improving weather forecasts to avoid disruption, damage and disaster

Summary of the impact:
Research by Professor Leonard Smith and the LSE Centre for the Analysis of Time Series (CATS) on forecasting in non-linear and often chaotic systems, with particular attention to weather, has led to advances in three areas: 1) national and international weather industry products and services that are built upon state-of-the-art research and knowledge, 2) dissemination of state-of-the-art practice in forecast production and verification to national, regional and local weather centres around the world, and 3) the introduction of, and new applications in, state-of-the-art forecasting methods in industries facing high uncertainty and risk, e.g. insurance and energy.

Please view the full impact case study here
Please view a summary of the case study here and a list of relevant resources here (LSE Research Impacts website)

News update 14/01/2015: The REF impact film for Improving weather forecasts to avoid disruption, damage and disaster has received the highest hit rate  of all LSE REF impact film. You can watch the film here.


Professor Leonard Smith
Centre for the Analysis of Time Series (CATS)
Email: l.smith@lse.ac.uk
CATS website

Title of case study: Ensuring the best science-based predictions of climate change

Summary of the impact:
As the realities of climate change have become more widely accepted over the last decade, decision makers have requested projections of future changes and impacts. Founded in 2002, the Centre for Analysis of Time Series (CATS) has conducted research revealing how the limited fidelity of climate models reduces the relevance of cost-benefit style management in this context: actions based on ill-founded projections (including probabilistic projections) can lead to maladaptation and poor policy choice. CATS’ conclusions were noted in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) report and led in turn to the toning down of the UK Climate Projections 2009 and the 2012 UK Climate Change Risk Assessment. Members of the insurance sector, energy sector, national security agencies, scientific bodies and governments have modified their approaches to climate risk management as a direct result of understanding CATS’ research. Attempts to reinterpret climate model output and design computer experiments for more effective decision support have also resulted.

Please view the full impact case study here
Please view a summary of the case study here and a list of relevant resources here (LSE Research Impacts website)


 

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