Interests
As a medical statistician, I focus on risk prediction modelling. In healthcare, improving lives and administering available resources are of maximum importance. Risk prediction tools can help clinicians to identify patients who would benefit most from particular interventions. These tools can be used to quantify the likelihood of
- a patient developing a disease, e.g. diabetes (risk modelling)
- the patient having the disease (diagnostic modelling), or
- the patient’s future outcomes (e.g. survival within 1 year, disease progression) once the disease is diagnosed (prognostic modelling).
Many studies focus on these issues, but few of them follow rigorous methodological development and validation processes that would allow the models to be used in practice. I disseminate good practice and create examples of well-developed and well-validated models.
Expertise
I have provided statistical expertise in medical studies in mental health, online learning, ophthalmology, chronic diseases (diabetes, kidney disease, cancer), and heart failure, using a variety of statistical methods (e.g., statistical process control, latent class analysis, multilevel modelling, propensity score methods, survival analysis, systematic review and meta-analysis, prediction modelling research).
My teaching includes coordinating the online module Introduction to Statistics for Health Care Research on the Oxford Postgraduate Programme in Evidence-Based Health Care (EBHC), tutoring for the Human Sciences Prelims module Introduction to Probability Theory and Statistics and for other modules in the EBHC MSc Medical Statistics programme.
Biography
Before joining the Nuffield Department of Primary Care Health Sciences, I completed a PhD in Statistics at the University of Warwick (UK), an MSc in Statistics at Centro de Investigación en Matemáticas/Universidad de Guanajuato (Mexico), and a BSc in Mathematics at the Universidad de Yucatan (Mexico).
Selected publications
- Lagerberg et al. (2026). External validation, recalibration and updating of the OxSATS risk model for suicide after self-harm in England. BMJ Mental Health, 29(1), e302568. doi:1136/bmjment-2026-302568
- Vazquez-Montes et al. (2026). Defining recurrent urinary tract infection and reinfection risk: electronic health record study. British Journal of General Practice Open. doi:3399/BJGPO.2025.0239
- Smith et al (2025). Natriuretic peptides testing and survival prediction models for chronic heart failure: a systematic review of added prognostic value. Diagnostic and Prognostic Research, 9(1). doi:1186/s41512-025-00210-x
- Honda et al. (2025). The impact of inter-infection time on antimicrobial resistance profiles in women with multiple urinary tract infections over time. Journal of Antimicrobial Chemotherapy. doi:1093/jac/dkaf194
- Fazel et al. (2024). Risk of repeat self-harm among individuals presenting to healthcare services: development and validation of a clinical risk assessment model (OxSET). BMJ Mental Health, 27(1). doi:1136/bmjment-2024-301180
- Vazquez-Montes et al. (2024). Epidemiology and microbiology of recurrent UTI in women in the community in Oxfordshire, UK. JAC-Antimicrobial Resistance, 6(1). doi:1093/jacamr/dlad156
- Fazel et al. (2023). Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS). BMJ Mental Health, 26(1). doi:1136/bmjment-2023-300673
- Fazel et al. (2021). Modifiable risk factors for inpatient violence in psychiatric hospital: prospective study and prediction model. Psychological Medicine. doi:1017/s0033291721002063
- Vazquez-Montes et al. (2020). Umbrella protocol: Systematic reviews of multivariable biomarker prognostic models developed to predict clinical outcomes in patients with heart failure. Diagnostic and Prognostic Research, 4(1). doi:1186/s41512-020-00081-4
