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Matthew Sperrin

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Professor of Biostatistics and Health Data Science

The University of Manchester

United Kingdom

Research Interests

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About

Matthew Sperrin is a Professor of Biostatistics and Health Data Science at the University of Manchester, UK. His research focuses on clinical prediction models, biostatistics, health data science, and the application of machine learning in oncology. Recent contributions include the development of an R package for calibration plots in multistate models and studies assessing the generalizability of research using electronic health record data. His work aims to enhance data-driven decision-making in healthcare through innovative methodologies and systematic reviews.

Recent Grants

Grant: Close

DynAIRx AI for Dynamic prescribing optimisation and care integration in multimorbidity

Open Date: 2022-09-01

Close Date: 2024-12-01

Grant: Close

CONNECT: Digital markers to predict psychosis relapse

Open Date: 2021-10-01

Close Date: 2026-02-28

Grant: Close

The Wearable Clinic: Connecting Health, Self and Care

Open Date: 2017-03-01

Close Date: 2021-02-28

Grant: Close

Multistate clinical prediction models in renal replacement therapy

Open Date: 2016-09-30

Close Date: 2020-12-31

Grant: Close

MICA: Health e-Research Centre

Open Date: 2013-03-01

Close Date: 2018-12-31

Positions (1)

Matthew Sperrin

University Name

University of Manchester

1 year ago

PhD in Fairness-Aware Cancer Data-Driven Detection at University of Manchester

A funded PhD opportunity is available at the University of Manchester as part of the CRUK Cancer Data-Driven Detection Programme. The project aims to develop, validate, and evaluate fairness-aware methods for cancer risk stratification, with a focus on ensuring equitable outcomes for diverse patient populations. The successful candidate will join a multidisciplinary supervisory team including Matthew Sperrin (Manchester), Sian Bladon (Manchester), Angela Wood (Cambridge), and Gary Abel (Exeter), as well as others across the CD3 network. The student will be based in a vibrant research group in Manchester with interests in causality and prediction. Applicants should have a strong background in mathematics, statistics, computer science, or machine learning. The position is funded for UK students, but specific details on stipend or tuition are not provided. The application deadline is 3 December 2025, with a start date in September/October 2026. For more information and to apply, visit the provided link.

1 year ago

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Collaborators (49)

Catherine Fullwood

Lead Medical Statistician

Manchester University NHS Foundation Trust

UNITED KINGDOM

Matthew Sperrin

Research Interests

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About

Recent Grants

Positions (1)

PhD in Fairness-Aware Cancer Data-Driven Detection at University of Manchester

Articles (20)

Predictors of treatment REsponse to inhaled corticosteroids (ICS) in Chronic Obstructive pulmonary disease

Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches.

Replicating a COVID-19 study in a national England database to assess the generalisability of research with regional electronic health record data

An automation framework for clinical codelist development validated with UK data from patients with multiple long-term conditions

A systematic review of passive data for remote monitoring in psychosis and schizophrenia

Updating methods for artificial intelligence–based clinical prediction models: a scoping review

A Decision-Analytical Perspective on Incorporating Multiple Outcomes in the Production of Clinical Prediction Models: defining a taxonomy of risk estimands

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review

calibmsm: An R package for calibration plots of the transition probabilities in a multistate model

Six-year performance of risk-based selection for lung cancer screening in the Manchester Lung Health Check cohort

Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques

Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study

Healthcare utilisation in patients with long-term conditions during the COVID-19 pandemic: a population-based observational study of all patients across Greater Manchester, UK.

Invitation strategies and participation in a community-based lung cancer screening programme located in areas of high socioeconomic deprivation

Association between nebuliser therapies adherence and visit-to-visit variability of FEV1 in patients with cystic fibrosis

Mortality in People with Type 2 Diabetes Following SARS-CoV-2 Infection: A Population Level Analysis of Potential Risk Factors

Prognostic value of patient-reported outcome measures (PROMs) in adults with non-small cell Lung Cancer

Heightened COVID-19 Mortality in People With Severe Mental Illness Persists After Vaccination

Targeted validation: validating clinical prediction models in their intended population and setting

Link Between Obesity and Early-Onset Colorectal Cancers (EOCRC)

Collaborators (49)

Catherine Fullwood

Martin O'Flaherty

Christina Van der Feltz-Cornelis

Shona Kirtley

Rebecca Whittle

Harry Hemingway

Stuart Grant

Jamie Sergeant

Angela Wood

Richard D Riley

Gayan Perera

Paula Dhiman

Alexander G Mathioudakis

Benedict Michael

Jørgen Vestbo

Nicola Cooper-Moss

Sandra Bucci

Lamiece Hassan

Michail Katsoulis

David Wong

Markus Fally

Lucinda Archer

Maurice O'Connell

Anne de Hond

Fatemeh Torabi

Joseph Alderman

Seb Bate

Sian Bladon

John Ainsworth

Kimberley Kavanagh

David Jenkins

chelsea sawyer

Harriet Cant

Julia Townson

Richard Williams

Serban Stoica

Glen Martin

Jan Hansel

Emily Eisner

Paula Lorgelly

Nadin Hawwash

Niels Peek

Mark Simmonds

Jung Yin Tsang

Alexander Pate

martin gibson

Patrick Goodley

Kym Snell

Riyaz Patel

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