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BSU Research OverviewDisease processes are often complex and dynamic, changing in response to time-varying risk factors. One approach to dealing with this complexity is to simultaneously model the disease process and the dynamic risk factors. These multi-outcome models can be used in dynamic risk prediction when we are interested in monitoring over time an individual’s risk of an event occurring. Jessica Barrett’s research centres on developing statistical methodology for modelling complex multi-outcome data. Applications include exploring the association between lung function and survival in cystic fibrosis patients and dynamic risk prediction of cardiovascular disease.
- Paige, E., Barrett, J., Pennells, L., Sweeting, M., Willeit, P., Di Angelantonio, E., …, Danesh, J., Thompson, S.G. & Wood, A. (2017) Repeated measurements of blood pressure and cholesterol improves cardiovascular disease risk prediction: an individual-participant-data meta-analysis. American Journal of Epidemiology 186: 899-907.
- Barrett, J.K., Sweeting, M.J. & Wood, A.M. (2017) Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC study. Handbook of Statistics 36: 47-65.
- Sweeting, M.J., Barrett, J.K., Thompson, S.G. & Wood, A.M. (2017) The use of repeated blood pressure measures for cardiovascular risk prediction. A comparison of statistical models in the ARIC study. Statistics in Medicine (epub ahead of print).
- Barrett, J. & Su, L. (2017) Dynamic predictions using flexible joint models of longitudinal and time-to-event data. Statistics in Medicine 36: 1447-1460.
- Barrett, J., Diggle, P., Henderson, R. & Taylor-Robinson, D. (2015) Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference. Journal of the Royal Statistical Society Series B 77: 131–148.
- Barrett, J.K., Henderson, R. & Rosthøj, S., (2014) Doubly robust estimation of optimal dynamic treatment regimes. Statistics in Biosciences 6: 244-260.