M. Sperrin

First name
M.
Last name
Sperrin
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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