Angela Wood is a University Senior Lecturer in Biostatistics at the Department of Public Health and Primary care, University of Cambridge. She joined the department in 2006 after spending 6 years as a Research Scientist at the Medical Research Council's Biostatistics Unit in Cambridge. She gained her PhD in Medical Statistics from the University of Lancaster in 2001. Angela's research interests are centred on the development and application of statistical methods for advancing epidemiological research.
She has focused on developing statistical methodology for handling measurement error, using repeated measures of risk factors, missing data problems, multiple imputation, risk prediction and meta-analysis. She has developed statistical methods and led analyses for major population resources to advance the study of cardiovascular disease, including analyses of the 2.5 million-participant Emerging Risk Factors Collaboration and EPIC-CVD (the world's largest genomic case-cohort study of incident CVD).
The aim of Angela's research is to develop statistical and machine learning frameworks for the development of dynamic risk prediction models that leverage repeated measurements and handle informative observation times in routinely recorded risk factors in electronic health records (EHRs). Two established statistical methods for developing dynamic risk prediction models are joint and landmark modelling, which could be combined with machine-learning approaches to help address current limitations and achieve improved risk assessment. The methods will be used to identify individuals at future risk of experiencing cardiovascular disease (CVD).
Despite reductions in age-specific cardiovascular diseases death rates in the UK in recent decades, the overall future CVD burden is predicted to increase as a result of the ageing population and increasing prevalence of risk factors such as obesity and diabetes. Indeed it has been estimated that tens of thousands of additional CVD outcomes could be prevented per year by better screening and management of risk. These potential gains are more likely to be realised if more compelling evidence emerge to support future approaches to primary prevention of CVD. For example, although the UK has introduced a national CVD screening programme (NHS Health Check) for adults without a history of CVD many of its features have not been founded on robust evidence and could be optimised (or re-designed) with emergence of new data.
Key unresolved questions include: What is the value of using repeated information on CVD risk factors already available in primary care records? How can we identify individuals with a greatest risk of experiencing certain type of CVDs? Such questions are potentially important because, if one could earlier predict which individuals were likely to suffer certain types of CVD events, then preventive treatments could be better targeted on these people and avoided for those at low risk.