Dr Lucinda Archer BSc (Hons) MSc PhD MPH (HTA) PGCHE FHEA

Dr Lucinda Archer

Department of Applied Health Sciences
Assistant Professor in Biostatistics

Contact details

Address
Department of Applied Health Sciences
Public Health Building
University of Birmingham
Edgbaston
Birmingham
B63 4HA

Lucinda is an Assistant Professor in Biostatistics, in the Tests and Prediction group at the University of Birmingham. Her research focuses on prediction modelling research, both prognostic and diagnostic, with a particular interest in statistical methods development and improving methodological quality of published research.

ORCiD iD: 0000-0003-2504-2613 

Google Scholar profile 

Qualifications

  • PhD Primary Care Sciences, Keele University, 2024
  • Postgraduate Certificate in Higher Education (PGCHE), Keele University, 2020
  • Master of Public Health (MPH), University of Birmingham, 2018
  • MSc Operational Research and Applied Statistics, Cardiff University, 2015
  • BSc (Hons) Mathematics, Cardiff University, 2014

Teaching

Under/Post-graduate modules:

  • Public Health MPH/Diploma/Certificate – Epidemiology, Statistics and Research Methods (ESRM), Practical Epidemiology and Statistics (PEaS), Dissertation supervision.
  • Medicine and Surgery (MBChB) — Medical Statistics component in Professional and Academic Skills 2
  • Mathematics BSc/MSci – Medical Statistics

 Continuing Professional Development

  • Statistical Methods for Risk Prediction and Prognostic Models
  • Prognosis Research in Healthcare, International Summer School
  • Statistical Methods for IPD Meta-Analysis
  • Systematic reviews of prediction and prognosis studies

Publications

Recent publications

Article

Riley, RD, Collins, GS, Whittle, R, Archer, L, Snell, KIE, Dhiman, P, Kirton, L, Legha, A, Liu, X, Denniston, AK, Harrell, FE, Wynants, L, Martin, GP & Ensor, J 2025, 'A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk-part 1: binary outcomes', Diagnostic and Prognostic Research, vol. 9, no. 1, 14. https://doi.org/10.1186/s41512-025-00193-9

Riley, R, Collins, G, Archer, L, Whittle, R, Legha, A, Kirton, L, Dhiman, P, Sadatsafavi, M, Adderley, N, Alderman, J, Martin, GP & Ensor, J 2025, 'A decomposition of Fisher’s information to inform sample size for developing or updating fair and precise clinical prediction models - Part 2: time-to-event outcomes', Diagnostic and Prognostic Research.

Menon, V, Shimelash, N, Rutunda, S, Nshimiyimana, C, Archer, L, Emmanuel-Fabula, M, Berhe, DF, Gill, J, Hezagira, E, Remera, E, Riley, R, Wong, R, Denniston, AK, Mateen, BA & Liu, X 2025, 'Assessing the potential utility of large language models for assisting community health workers: protocol for a prospective, observational study in Rwanda', BMJ open, vol. 15, no. 10, e110927. https://doi.org/10.1136/bmjopen-2025-110927

STRATIFY investigators, Wang, A, Koshiaris, C, Archer, L, Riley, RD, Snell, KIE, Stevens, R, Banerjee, A, Usher-Smith, JA, Swain, S, Clegg, A, Clark, CE, Payne, RA, Hobbs, RFD, Mcmanus, RJ & Sheppard, JP 2025, 'Developing prediction models for electrolyte abnormalities in patients indicated for antihypertensive therapy: evidence-based treatment and monitoring recommendations', Journal of Hypertension. https://doi.org/10.1097/HJH.0000000000004032

Whittle, R, Ensor, J, Archer, L, Collins, GS, Dhiman, P, Denniston, A, Alderman, J, Legha, A, van Smeden, M, Moons, KG, Cazier, J-B, Riley, RD & Snell, KIE 2025, 'Extended sample size calculations for evaluation of prediction models using a threshold for classification', BMC Medical Research Methodology, vol. 25, no. 1, 170. https://doi.org/10.1186/s12874-025-02592-4

Riley, RD, Ensor, J, Snell, KIE, Archer, L, Whittle, R, Dhiman, P, Alderman, J, Liu, X, Kirton, L, Manson-Whitton, J, van Smeden, M, Nirantharakumar, K, Denniston, AK, Van Calster, B & Collins, G 2025, 'Importance of sample size on the quality and utility of AI-based prediction models for healthcare', The Lancet Digital Health. https://doi.org/10.1016/j.landig.2025.01.013

Koshiaris, C, Wang, A, Archer, L, Riley, RD, Snell, KIE, Stevens, RJ, Banerjee, A, Swain, S, Clegg, A, Clark, CE, Payne, RA, Hobbs, FDR, Ogden, M, McManus, RJ, Sheppard, JP & STRATIFY investigators 2025, 'Predicting hypotension, syncope, and fracture risk in patients indicated for antihypertensive treatment: the STRATIFY models', Nature Communications, vol. 16, 9371. https://doi.org/10.1038/s41467-025-64408-9

Martin, J, Scandrett, K, Easter, C, Whittle, R, Legha, A, Hillier, B, Thompson, J & Archer, L 2025, 'The Role of Generative AI in Data Analysis Assessments', Education in Practice, vol. 6, no. 1, pp. 107-116. <https://bham.sharepoint.com/sites/aseddev/Shared%20Documents/EiP%20Journal%20Vol%206.1/10_Martin_EIP_6.1%20Spring%202025.pdf?csf=1&web=1&e=zOUylv>

Riley, RD, Collins, G, Kirton, L, Snell, KIE, Ensor, J, Whittle, R, Dhiman, P, van Smeden, M, Liu, X, Alderman, J, Nirantharakumar, K, Manson-Whitton, J, Westwood, AJ, Cazier, J-B, Moons, KGM, Martin, GP, Sperrin, M, Denniston, AK, Jr, FEH & Archer, L 2025, 'Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches', BMJ, vol. 388, e080749. https://doi.org/10.1136/bmj-2024-080749

Archer, L, Hattle, M, Riley, RD & The eFI+ investigators 2024, 'Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults', Age and Ageing, vol. 53, no. 3, afae057. https://doi.org/10.1093/ageing/afae057

Preprint

Riley, RD, Collins, GS, Whittle, R, Archer, L, Snell, KIE, Dhiman, P, Kirton, L, Legha, A, Liu, X, Denniston, A, Harrell Jr, FE, Wynants, L, Martin, GP & Ensor, J 2025 'A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes' arXiv. https://doi.org/10.48550/arXiv.2407.09293

Riley, RD, Collins, GS, Archer, L, Whittle, R, Legha, A, Kirton, L, Dhiman, P, Sadatsafavi, M, Adderley, NJ, Alderman, J, Martin, GP & Ensor, J 2025 'A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- Part 2: time-to-event outcomes' arXiv. <https://arxiv.org/abs/2501.14482>

Whittle, R, Riley, RD, Archer, L, Collins, GS, Legha, A, Snell, KIE & Ensor, J 2025 'A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models -- Part 3: continuous outcomes' arXiv. https://doi.org/10.48550/arXiv.2507.2354

Legha, A, Ensor, J, Whittle, R, Archer, L, Van Calster, B, Christodoulou, E, Snell, KIE, Sadatsafavi, M, Collins, GS & Riley, RD 2025 'Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals' arXiv. https://doi.org/10.48550/arXiv.2509.15134

Review article

Tsegaye, B, Snell, KIE, Archer, L, Kirtley, S, Riley, RD, Sperrin, M, Van Calster, B, Collins, G & Dhiman, P 2025, 'Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review', Journal of Clinical Epidemiology, vol. 180, 111675. https://doi.org/10.1016/j.jclinepi.2025.111675

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