BSU Research Overview
Disease 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, particularly the statistical challenges of using electronic health records for prediction modelling. Her research is motivated by clinical applications including cystic fibrosis, cardiovascular disease and multimorbidity.
- Jeanselme V, Tom B, Barrett J. Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering. Proceedings of Machine Learning Research. 2022;174:11.
- McClure ME, Zhu Y, Smith RM, Gopaluni S, Tieu J, Pope T, et al. Long-term maintenance rituximab for ANCA-associated vasculitis: relapse and infection prediction models. Rheumatology. 2021 Mar 2;60(3):1491–501.
- Taylor-Robinson D, Schlüter DK, Diggle PJ, Barrett JK. Explaining the Sex Effect on Survival in Cystic Fibrosis: a Joint Modeling Study of UK Registry Data. Epidemiology. 2020 Nov;31(6):872–9.
- Barrett JK, Huille R, Parker R, Yano Y, Griswold M. Estimating the association between blood pressure variability and cardiovascular disease: An application using the ARIC Study. Statistics in Medicine. 2019;38(10):1855–68.
- Paige E, Barrett J, Stevens D, Keogh RH, Sweeting MJ, Nazareth I, et al. Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk. Am J Epidemiol. 2018 Jul 1;187(7):1530–8.
- Sweeting MJ, Barrett JK, Thompson SG, Wood AM. The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study. Statist Med. 2017 Dec 10;36(28):4514–28.
- Barrett J, Su L. Dynamic predictions using flexible joint models of longitudinal and time-to-event data. Statist Med. 2017 Apr 30;36(9):1447–60.
- Barrett J, Diggle P, Henderson R, Taylor-Robinson D. Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference. J R Stat Soc B. 2015 Jan;77(1):131–48.