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Cambridge Public Health

 

I am a statistician working at the Cardiovascular Epidemiology Unit in the Department for Public Health and Primary Care. My research interests include meta-analysis, survival analysis and longitudinal data analysis. I have worked on methods for the joint modelling of longitudinal and survival data, applied to lung function and survival in cystic fibrosis patients. Other research interests include the meta-analysis of survival data, network meta-analysis and multi-state models. I am currently working on statistical methods for incorporating repeat measurements of risk factors into risk prediction for cardiovascular disease.

Publications from Elements

Journal articles

2023 (No publication date)

  • Chung, R., 2023 (No publication date). Using polygenic risk scores for prioritising individuals at greatest need of a CVD risk assessment Journal of the American Heart Association,
    Doi: http://doi.org/10.1161/JAHA.122.029296
  • 2023 (Accepted for publication)

  • Barrett, J., 2023 (Accepted for publication). Modelling risk factors for intraindividual variability: a mixed-effects beta-binomial model applied to cognitive function in older people in the English Longitudinal Study of Ageing American Journal of Epidemiology,
  • Chung, R., Xu, Z., Arnold, M., Stevens, D., Barrett, J., Harrison, H., Pennells, L., Kim, L., Di Angelantonio, E., Usher-Smith, J. and Wood, A., 2023 (Accepted for publication). Prioritising cardiovascular disease risk assessment to high risk individuals based on primary care records PLoS One,
    Doi: 10.1371/journal.pone.0292240
  • 2023

  • Gasperoni, F., Luati, A., Paci, L. and D'Innocenzo, E., 2023. Score-Driven Modeling of Spatio-Temporal Data. J Am Stat Assoc, v. 118
    Doi: 10.1080/01621459.2021.1970571
  • Mota, BS., Bevilacqua, JLB., Barrett, J., Ricci, MD., Munhoz, AM., Filassi, JR., Baracat, EC. and Riera, R., 2023. Skin-sparing mastectomy for the treatment of breast cancer. Cochrane Database Syst Rev, v. 3
    Doi: http://doi.org/10.1002/14651858.CD010993.pub2
  • Verma, R., Chandarana, M., Barrett, J., Anandadas, C. and Sundara Rajan, S., 2023. Post-mastectomy radiotherapy for women with early breast cancer and one to three positive lymph nodes. Cochrane Database of Systematic Reviews, v. 6
    Doi: http://doi.org/10.1002/14651858.CD014463.pub2
  • Chen, S., Marshall, T., Jackson, C., Cooper, J., Crowe, F., Nirantharakumar, K., Saunders, CL., Kirk, P., Richardson, S., Edwards, D., Griffin, S., Yau, C. and Barrett, JK., 2023. Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England. PLoS Med, v. 20
    Doi: http://doi.org/10.1371/journal.pmed.1004310
  • 2022 (Accepted for publication)

  • McMenamin, M., Barrett, JK., Berglind, A. and Wason, JMS., 2022 (Accepted for publication). Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints Statistics in Medicine,
    Doi: http://doi.org/10.1002/sim.9356
  • Barrett, J., Richardson, S., Kiddle, S. and Kirk, P., 2022 (Accepted for publication). The optimum algorithms for multimorbidity clustering identified in simulated and health record data Journal of Clinical Epidemiology,
  • Xu, Z., Arnold, M., Sun, L., Stevens, D., Chung, R., Ip, HY., Barrett, J., Kaptoge, S., Pennells, L., Di Angelantonio, E. and Wood, A., 2022 (Accepted for publication). Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records International Journal of Epidemiology,
  • Jeanselme, V., Tom, B. and Barrett, J., 2022 (Accepted for publication). Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering Proceedings of Machine Learning Research,
  • Barrett, J., Richardson, S., Kiddle, S. and Kirk, P., 2022 (Accepted for publication). The optimum algorithms for multimorbidity clustering identified in simulated and health record data Journal of Clinical Epidemiology,
  • 2022

  • Nichols, L., Taverner, T., Crowe, F., Richardson, S., Yau, C., Kiddle, S., Kirk, P., Barrett, J., Nirantharakumar, K., Griffin, S., Edwards, D. and Marshall, T., 2022. In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm. J Clin Epidemiol, v. 152
    Doi: http://doi.org/10.1016/j.jclinepi.2022.10.011
  • Jeanselme, V., Barrett, J. and Tom, B., 2022. Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness Proceedings of Machine Learning Research, v. 193
  • 2021

  • Wan, EYF., Yu, EYT., Chin, WY., Barrett, JK., Wong, ICK., Chan, EWY., Chui, CSL., Chen, S. and Lam, CLK., 2021. Age-Specific Associations of Usual Blood Pressure Variability With Cardiovascular Disease and Mortality: 10-Year Diabetes Mellitus Cohort Study. J Am Heart Assoc, v. 10
    Doi: http://doi.org/10.1161/JAHA.120.019026
  • Verma, R., Chandarana, M., Barrett, J., Anandadas, C. and Sundara Rajan, S., 2021. Post-mastectomy radiotherapy for women with early breast cancer and one to three positive lymph nodes Cochrane Database of Systematic Reviews, v. 2021
    Doi: http://doi.org/10.1002/14651858.CD014463
  • McMenamin, M., Barrett, JK., Berglind, A. and Wason, JM., 2021. Employing a latent variable framework to improve efficiency in composite endpoint analysis. Stat Methods Med Res, v. 30
    Doi: http://doi.org/10.1177/0962280220970986
  • 2020 (Accepted for publication)

  • Xu, Z., Arnold, M., Stevens, D., Kaptoge, S., Pennells, L., Sweeting, M., Barrett, J., Di Angelantonio, E. and Wood, A., 2020 (Accepted for publication). Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment American Journal Of Epidemiology,
    Doi: http://doi.org/10.1093/aje/kwab031
  • Taylor-Robinson, D., Schlüter, D., Diggle, P. and Barrett, J., 2020 (Accepted for publication). Explaining the sex effect on survival in cystic fibrosis: a joint modelling study of UK registry data Epidemiology,
  • Sisk, R., Lin, L., Sperrin, M., Barrett, J., Tom, B., Diaz-Ordaz, K., Peek, N. and Martin, G., 2020 (Accepted for publication). Informative presence and observation in routine health data: A review of methodology for clinical risk prediction Journal of the American Medical Informatics Association,
  • 2020

  • McClure, ME., Zhu, Y., Smith, RM., Gopaluni, S., Tieu, J., Pope, T., Kristensen, KE., Jayne, DRW., Barrett, J. and Jones, RB., 2020. Long-term maintenance rituximab for ANCA-associated vasculitis: relapse and infection prediction models. Rheumatology (Oxford),
    Doi: http://doi.org/10.1093/rheumatology/keaa541
  • Wan, EYF., Yu, EYT., Chin, WY., Barrett, JK., Mok, AHY., Lau, CST., Wang, Y., Wong, ICK., Chan, EWY. and Lam, CLK., 2020. Greater variability in lipid measurements associated with cardiovascular disease and mortality: A 10-year diabetes cohort study. Diabetes Obes Metab, v. 22
    Doi: http://doi.org/10.1111/dom.14093
  • 2019 (Accepted for publication)

  • Su, L., Li, Q., Barrett, J. and Daniels, M., 2019 (Accepted for publication). A Sensitivity Analysis Approach for Informative Dropout using Shared Parameter Models Biometrics,
  • Marta, G., Barrett, J., Porfirio, G., Martimbianco, ALC., Bevilacqua, JLB., Poortmans, P. and Riera, R., 2019 (Accepted for publication). Effectiveness of different accelerated partial breast irradiation techniques for the treatment of breast cancer patients: systematic review using indirect comparisons of randomized clinical trials Reports of Practical Oncology and Radiotherapy,
  • Gasparini, A., Abrams, K., Barrett, J., Major, R., Sweeting, M., Brunskill, N. and Crowther, M., 2019 (Accepted for publication). Mixed effects models for healthcare longitudinal data with an informative visiting process: A Monte Carlo simulation study Statistica Neerlandica,
    Doi: http://doi.org/10.1111/stan.12188
  • Paes, VM., Barrett, J., Dunger, D., Gevers, E., Taylor-Robinson, D., Viner, R. and Stephenson, T., 2019 (Accepted for publication). Factors predicting poor glycaemic control in the first two years of childhood onset type 1 diabetes in a cohort from East London, UK: Analyses using mixed effects fractional polynomial models Pediatric Diabetes,
  • 2019

  • Mazarello Paes, V., Barrett, JK., Taylor-Robinson, DC., Chesters, H., Charalampopoulos, D., Dunger, DB., Viner, RM. and Stephenson, TJ., 2019. Effect of early glycaemic control on HbA1c tracking and development of vascular complications after 5 years of childhood onset type 1 diabetes: Systematic Review and Meta-analysis. Pediatric Diabetes,
    Doi: http://doi.org/10.1111/pedi.12850
  • 2018 (Accepted for publication)

  • Wood, AM., Stevens, D., Barrett, J., Paige, E., Keogh, R., Sweeting, M., Nazareth, I. and Petersen, I., 2018 (Accepted for publication). Landmark models for optimizing the use of repeated measurements of risk factors in electronic health records to predict future disease risk American Journal of Epidemiology,
    Doi: http://doi.org/10.1093/aje/kwy018
  • Grootes, I., Barrett, JK., Ulug, P., Rohlffs, F., Laukontaus, S., Tulamo, R., Venermo, M., Greenhalgh, R. and Sweeting, MJ., 2018 (Accepted for publication). Predicting risk of rupture and rupture-preventing re-interventions utilising repeated measures on aneurysm sac diameter following endovascular abdominal aortic aneurysm repair British Journal of Surgery, v. 105
    Doi: http://doi.org/10.1002/bjs.10964
  • Seaman, S., Keogh, R., Barrett, J., Taylor-Robinson, D. and Szczesniak, R., 2018 (Accepted for publication). Dynamic prediction of survival in cystic fibrosis: A landmarking analysis using UK patient registry data Epidemiology,
  • Barrett, JK., Huille, R., Parker, R., Yano, Y. and Griswold, M., 2018 (Accepted for publication). Estimating the association between blood pressure variability and cardiovascular disease: An application using the ARIC Study Statistics in Medicine,
  • 2018

  • Patel, R., Powell, JT., Sweeting, MJ., Epstein, DM., Barrett, JK. and Greenhalgh, RM., 2018. The UK EndoVascular Aneurysm Repair (EVAR) randomised controlled trials: long-term follow-up and cost-effectiveness analysis. Health Technology Assessment, v. 22
    Doi: http://doi.org/10.3310/hta22050
  • 2017

  • Paige, E., Barrett, J., Pennells, L., Sweeting, M., Willeit, P., Di Angelantonio, E., Gudnason, V., Nordestgaard, BG., Psaty, BM., Goldbourt, U., Best, LG., Assmann, G., Salonen, JT., Nietert, PJ., Verschuren, WM., Brunner, EJ., Kronmal, RA., Salomaa, V., Bakker, SJ., Dagenais, GR., Sato, S., Jansson, J-H., Willeit, J., Onat, A., de la Cámara, AG., Roussel, R., Völzke, H., Dankner, R., Tipping, RW., Meade, TW., Donfrancesco, C., Kuller, LH., Peters, A., Gallacher, J., Kromhout, D., Iso, H., Knuiman, M., Casiglia, E., Kavousi, M., Palmieri, L., Sundström, J., Davis, BR., Njølstad, I., Couper, D., Danesh, J., Thompson, SG. and Wood, A., 2017. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis American Journal of Epidemiology,
    Doi: http://doi.org/10.1093/aje/kwx149
  • 2016 (Accepted for publication)

  • Mota, BS., Riera, R., Ricci, MD., Barrett, JK., de Castria, TB., Atallah, Á. and Bevilacqua, JLB., 2016 (Accepted for publication). Nipple- and areola-sparing mastectomy for the treatment of breast cancer Cochrane Database of Systematic Reviews,
    Doi: http://doi.org/10.1002/14651858.CD008932.pub3
  • Barrett, J. and Su, L., 2016 (Accepted for publication). Dynamic predictions using flexible joint models of longitudinal and time-to-event data. Statistics in Medicine,
    Doi: http://doi.org/10.1002/sim.7209
  • 2016

  • Jackson, D., Law, M., Barrett, JK., Turner, R., Higgins, JPT., Salanti, G. and White, IR., 2016. Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects. Stat Med, v. 35
    Doi: http://doi.org/10.1002/sim.6752
  • Sweeting, MJ., Barrett, JK., Thompson, SG. and Wood, AM., 2016. The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study. Stat Med,
    Doi: http://doi.org/10.1002/sim.7144
  • 2015

  • Barrett, J., Diggle, P., Henderson, R. and Taylor-Robinson, D., 2015. Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference. J R Stat Soc Series B Stat Methodol, v. 77
    Doi: http://doi.org/10.1111/rssb.12060
  • Jenkins, V., Fallowfield, L., Langridge, C., Barrett, J., Ryan, A., Jacobs, I., Kilkerr, J., Menon, U. and Farewell, V., 2015. Psychosocial Factors Associated With Withdrawal From the United Kingdom Collaborative Trial of Ovarian Cancer Screening After 1 Episode of Repeat Screening. Int J Gynecol Cancer, v. 25
    Doi: http://doi.org/10.1097/IGC.0000000000000507
  • 2014

  • Salani, B., Barrett, J., Ricci, MD., Bevilacqua, JLB. and Riera, R., 2014. Skin-sparing mastectomy for the treatment of breast cancer Cochrane Database of Systematic Reviews, v. 2014
    Doi: http://doi.org/10.1002/14651858.CD010993
  • Jackson, D., Barrett, JK., Rice, S., White, IR. and Higgins, JPT., 2014. A design-by-treatment interaction model for network meta-analysis with random inconsistency effects. Stat Med, v. 33
    Doi: http://doi.org/10.1002/sim.6188
  • Barrett, J., Jenkins, V., Farewell, V., Menon, U., Jacobs, I., Kilkerr, J., Ryan, A., Langridge, C., Fallowfield, L. and UKCTOCS trialists, , 2014. Psychological morbidity associated with ovarian cancer screening: results from more than 23,000 women in the randomised trial of ovarian cancer screening (UKCTOCS). BJOG, v. 121
    Doi: http://doi.org/10.1111/1471-0528.12870
  • Barrett, JK., Henderson, R. and Rosthøj, S., 2014. Doubly Robust Estimation of Optimal Dynamic Treatment Regimes. Stat Biosci, v. 6
    Doi: http://doi.org/10.1007/s12561-013-9097-6
  • Rosthøj, S., Henderson, R. and Barrett, JK., 2014. Optimal Dynamic Treatment Strategies with Protection Against Missed Decision Points Statistics in Biosciences, v. 6
    Doi: 10.1007/s12561-013-9107-8
  • 2013

  • Lunn, D., Barrett, J., Sweeting, M. and Thompson, S., 2013. Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis. J R Stat Soc Ser C Appl Stat, v. 62
    Doi: http://doi.org/10.1111/rssc.12007
  • Pollock, RA., Chandran, V., Pellett, FJ., Thavaneswaran, A., Eder, L., Barrett, J., Rahman, P., Farewell, V. and Gladman, DD., 2013. The functional MICA-129 polymorphism is associated with skin but not joint manifestations of psoriatic disease independently of HLA-B and HLA-C. Tissue Antigens, v. 82
    Doi: http://doi.org/10.1111/tan.12126
  • Barker, RA., Barrett, J., Mason, SL. and Björklund, A., 2013. Fetal dopaminergic transplantation trials and the future of neural grafting in Parkinson's disease. Lancet Neurol, v. 12
    Doi: http://doi.org/10.1016/S1474-4422(12)70295-8
  • 2012

  • Higgins, JPT., Jackson, D., Barrett, JK., Lu, G., Ades, AE. and White, IR., 2012. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods, v. 3
    Doi: http://doi.org/10.1002/jrsm.1044
  • White, IR., Barrett, JK., Jackson, D. and Higgins, JPT., 2012. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Res Synth Methods, v. 3
    Doi: http://doi.org/10.1002/jrsm.1045
  • Barrett, JK., Farewell, VT., Siannis, F., Tierney, J. and Higgins, JPT., 2012. Two-stage meta-analysis of survival data from individual participants using percentile ratios. Stat Med, v. 31
    Doi: http://doi.org/10.1002/sim.5516
  • 2011

  • Pollock, R., Chandran, V., Barrett, J., Eder, L., Pellett, F., Yao, C., Lino, M., Shanmugarajah, S., Farewell, VT. and Gladman, DD., 2011. Differential major histocompatibility complex class I chain-related A allele associations with skin and joint manifestations of psoriatic disease. Tissue Antigens, v. 77
    Doi: http://doi.org/10.1111/j.1399-0039.2011.01670.x
  • Barrett, JK., Siannis, F. and Farewell, VT., 2011. A semi-competing risks model for data with interval-censoring and informative observation: an application to the MRC cognitive function and ageing study. Stat Med, v. 30
    Doi: http://doi.org/10.1002/sim.4071
  • 2010

  • Siannis, F., Barrett, JK., Farewell, VT. and Tierney, JF., 2010. One-stage parametric meta-analysis of time-to-event outcomes. Stat Med, v. 29
    Doi: http://doi.org/10.1002/sim.4086
  • Fallowfield, L., Fleissig, A., Barrett, J., Menon, U., Jacobs, I., Kilkerr, J., Farewell, V. and UKCTOCS Trialists, , 2010. Awareness of ovarian cancer risk factors, beliefs and attitudes towards screening: baseline survey of 21,715 women participating in the UK Collaborative Trial of Ovarian Cancer Screening. Br J Cancer, v. 103
    Doi: http://doi.org/10.1038/sj.bjc.6605809
  • 2009

  • Chandran, V., Barrett, J., Schentag, CT., Farewell, VT. and Gladman, DD., 2009. Axial psoriatic arthritis: update on a longterm prospective study. J Rheumatol, v. 36
    Doi: http://doi.org/10.3899/jrheum.090412
  • Conference proceedings

    2022 (Accepted for publication)

  • Jeanselme, V., Tom, B. and Barrett, J., 2022 (Accepted for publication). Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering Proceedings of Machine Learning Research,
  • 2011

  • Pollock, R., Chandran, V., Barrett, J., Eder, L., Pellett, F., Yao, C., Lino, M., Shanmugarajah, S., Farewell, V. and Gladman, D., 2011. The Functional MICA-129 Polymorphism is Associated with Psoriatic Disease Independently of HLA-B and C JOURNAL OF RHEUMATOLOGY, v. 38
  • 2008

  • Chandran, V., Barrett, J., Schentag, CT., Farewell, V. and Gladman, DD., 2008. Progression of spondylitis in patients with psoriatic arthritis (PsA) ARTHRITIS AND RHEUMATISM, v. 58
  • Book chapters

    2017 (Published online)

  • Barrett, JK., Sweeting, MJ. and Wood, AM., 2017 (Published online). Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study
    Doi: http://doi.org/10.1016/bs.host.2017.05.004