M. Sperrin

First name
Last name
Morgan, C., Ashcroft, D. M., Chew-Graham, C. A., Sperrin, M., Webb, R. T., Francis, A., et al. (2024). Identifying prior signals of bipolar disorder using primary care electronic health records: a nested case-control study. Br J Gen Pract, 74, e165-73. http://doi.org/10.3399/bjgp.2022.0286
Pate, A., Sperrin, M., Riley, R. D., Sergeant, J. C., Van Staa, T., Peek, N., et al. (2023). Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques. Stat Med. http://doi.org/10.1002/sim.9771
Sheppard, T., Tamblyn, R., Abrahamowicz, M., Lunt, M., Sperrin, M., & Dixon, W. G. (2017). A comparison of methods for estimating the temporal change in a continuous variable: Example of HbA1c in patients with diabetes. Pharmacoepidemiol Drug Saf. http://doi.org/10.1002/pds.4273
Pate, A., Barrowman, M., Webb, D., Pimenta, J. M., Davis, K. J., Williams, R., et al. (2018). Study investigating the generalisability of a COPD trial based in primary care (Salford Lung Study) and the presence of a Hawthorne effect. BMJ Open Respir Res. http://doi.org/10.1136/bmjresp-2018-000339
Li, Y., Molter, A., White, A., Welfare, W., Palin, V., Belmonte, M., et al. (2019). Relationship between prescribing of antibiotics and other medicines in primary care: a cross-sectional study. Br J Gen Pract. http://doi.org/10.3399/bjgp18X700457
Li, Y., Sperrin, M., Belmonte, M., Pate, A., Ashcroft, D. M., & van Staa, T. P. (2019). Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?. Sci Rep. http://doi.org/10.1038/s41598-019-47712-5
Li, Y., Sperrin, M., Martin, G. P., Ashcroft, D. M., & van Staa, T. P. (2020). Examining the impact of data quality and completeness of electronic health records on predictions of patients\textquoteright risks of cardiovascular disease. Int J Med Inform. http://doi.org/10.1016/j.ijmedinf.2019.104033
Pate, A., Emsley, R., Sperrin, M., Martin, G. P., & Van Staa, T. (2020). Impact of sample size on the stability of risk scores from clinical prediction models: a case study in cardiovascular disease. Diagn Progn Res. http://doi.org/10.1186/s41512-020-00082-3
Li, Y., Sperrin, M., Ashcroft, D. M., & van Staa, T. P. (2020). Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar. Bmj. http://doi.org/10.1136/bmj.m3919
van Bodegraven, B., Palin, V., Mistry, C., Sperrin, M., White, A., Welfare, W., et al. (2021). Infection-related complications after common infection in association with new antibiotic prescribing in primary care: retrospective cohort study using linked electronic health records. BMJ Open. http://doi.org/10.1136/bmjopen-2020-041218