Professor of Health Data Science
Tel: +44 (0) 1223 748652
Angela received her BSc (Hons) in Mathematics and Statistics in 1998 and subsequently completed her doctorate on the subject of joint modeling longitudinal and time-to-event data with Profs Peter Diggle and Robin Henderson from the University of Lancaster in 2001. She carried out post-doctoral research with Dr Ian White at the MRC Biostatistics Unit, Cambridge and was appointed in 2006 to University Lecturer in Biostatistics in the Department of Public Health and Primary Care, Cambridge. She now holds the position as Professor of Health Data Science in the Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Cambridge.
Angela holds leadership roles for major national and local health data science initiatives, including BHF Data Science Centre Associate Director and Theme Lead for Structured Data; co-Lead of the NIHR Cambridge BRC Data Science and Population Health theme; Regional co-Lead for Health Data Research UK Cambridge; Programme Leader in the NIHR Blood and Transplant Research Unit in Donor Health and Behaviour and co-Lead of the Data-Analysis Work-package in BigData@Heart.
Angela’s research focuses on the frontiers of big data and epidemiology, underpinned by major population resources and informed by applied questions of major population health and clinical salience. She has developed novel methods and applied them in the analysis of large, complex datasets (e.g., ~67M individuals in CVD-COVID-UK consortium; >3M participants in Emerging Risk Factors Collaboration, EPIC-CVD and UK Biobank), providing new insights into chronic diseases, mainly cardiovascular disease, as well as COVID-19 and blood donation.
With the BHF Data Science Centre and in partnership with NHS Digital, she has helped to establish access to the England-wide Electronic Health Record resource on >55M people and is developing innovative and principled methods for reproducible analysis of the resource.
In biostatistics methodology research, she focuses largely on methods for utilising electronic health records to produce unbiased results for medical/epidemiological research, including handling measurement error, using repeated measures of risk factors, missing data problems, multiple imputation, risk prediction and meta-analysis.
BHF Data Science Centre: https://www.hdruk.ac.uk/helping-with-health-data/bhf-data-science-centre/
NIHR-BTRU Donor health and behaviour: http://www.donorhealth-btru.nihr.ac.uk/
NIHR BRC Cambridge: https://cambridgebrc.nihr.ac.uk/
Teaching and Training
Angela delivers lectures on biostatistics to undergraduates, postgraduates and in summer schools across Cambridge University and co-leads an annual 2-day course on Multiple Imputation at UCL. She supervises a number of MPhil and PhD students.
See google scholar: https://scholar.google.co.uk/citations?user=urLZUHwAAAAJ&hl=en
1. Knight, R., Walker, V., Ip, S,…, Whiteley, W.N., Wood, A.M., Sterne, J.A.C (2022). Association of COVID-19 With Major Arterial and Venous Thrombotic Diseases: A Population-Wide Cohort Study of 48 Million Adults in England and Wales. Circulation;146:892–906
2. Xu, Z., Arnold, M., Sun, L,…,Wood, A. M. (2022). Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records. Int J Epidemiol. doi:10.1093/ije/dyac140
3. Whiteley, W. N., Ip, S., Cooper, J. A.,…, Wood, A.M., Sterne, J.A.C., Sudlow, C. (2022). CVD-COVID-UK consortium. Association of COVID-19 vaccines ChAdOx1 and BNT162b2 with major venous, arterial, or thrombocytopenic events: A population-based cohort study of 46 million adults in England. PLoS Med.
4. SCORE2 working group and ESC Cardiovascular risk collaboration. (2021). SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J, 42(25), 2439-2454.
5. Wood, A., Denholm, R., Hollings,…, Sudlow, C., CVD-COVID-UK consortium (2021). Linked electronic health records for re-search on a nationwide cohort of more than 54 million people in England: data resource. BMJ, 373, n826.
6. Harshfield E.L, Pennells L., Schwartz J.E.,…, Wood AM, Danesh J, Di Angelantonio E, Davidson KW (2021). Association between depressive symptoms and incident cardiovascular diseases. JAMA. 324(23), 2396-2405.
7. Xu, Z., Arnold, M., Stevens, D., Kaptoge, S.,…, Wood, AM (2021). Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment. American Journal of Epidemiology.
8. Jochems SHJ, Stattin P, Häggström C,…, Wood AM, Stocks T (2020). Height, body mass index and prostate cancer risk and mortality by way of detection and cancer risk category. Int J Cancer, 147(12), 3328-3338. doi:10.1002/ijc.33150
9. Pennells, L., Kaptoge, S., Wood, A.M.,…,Emerging Risk Factors Collaboration. (2019). Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. Eur Heart J, 40(7), 621-631.
10. Keogh, R. H., Seaman, S. R., Bartlett, J. W., & Wood, A. M. (2018). Multiple imputation of missing data in nested case-control and case-cohort studies. Biometrics. doi:10.1111/biom.12910
11. Paige, E., Barrett, J., Stevens,…,Wood, A. M. (2018). Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk. Am J Epidemiol, 187(7), 1530-1538.
12. Ricci, C., Wood, A. M., Muller,…,Ferrari, P. (2018). Alcohol intake in relation to non-fatal and fatal coronary heart disease and stroke: EPIC-CVD case-cohort study. BMJ, 361, k934.
13. Wood, A. M., Kaptoge, S., Butterworth,…,Emerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group. (2018). Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. Lancet, 391(10129), 1513-1523. doi:10.1016/S0140-6736(18)30134-X
14. Wood, A. M., Royston, P., & White, I. R. (2015). The estimation and use of predictions for the assessment of model performance using large samples with multiply imputed data. Biometrical Journal, 57(4), 614-632. doi:10.1002/bimj.201400004
15. Wormser, D., Wood, A. M.,…, Di Angelantonio, E., Thompson, S. G., Danesh, J., & Emerging Risk Factors Collaboration. (2014). Metabolic mediators of body-mass index and cardiovascular risk. Lancet, 383(9934), 2042-2043.