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Matthew Sperrin

  • PhD
  • Senior Lecturer in Health Data Science
  • University of Manchester
  • 12h-indexImpact measure calculated using publication and citation counts. Updated daily.
  • 543CitationsNumber of citations received by Matthew's publications. Updated daily.

Research interests

EpidemiologyStatistics

About

Dr Sperrin researches new statistical methodology to make inference with observational health data, collaborating closely with clinicians, epidemiologists, health informaticians, software engineers and statisticians. His research can be categorised in three areas: 1. Understanding the observation process. When data are observational it is crucial to understand why data are present (and why not present). For example, a blood pressure measurement in a medical record implies both that the patient has made contact with a healthcare professional, and the professional has deemed it appropriate to measure blood pressure. In other words, the presence of the measurement (or the absence) can be just as important as the measurement itself. This is often ignored in statistical analysis, which can lead to biased results. 2. Inferring trends and patterns from data. This can range from simple applied questions (is X associated with Y?) through to advanced statistical techniques to uncover hidden structure in a population. For example, a disease such as asthma may actually consist of a number of subdiseases (endotypes) that may have different outcomes and require different treatment. 3. Making predictions and decisions. Given what we know about a patient now, what do we think will happen to them in the future, and (therefore) what should happen next? For example, this involves developing prediction models for disease incidence and mortality, and complex simulation models to understand how disease may progress under different scenarios, both at an individual and population level.

Co-authors (233)

  • jeff renna
  • Deirdre Hollingsworth
  • Hannah Lennon
  • Iain Buchan
  • Evangelos Kontopantelis

Publications (5)

  • A review of statistical updating methods for clinical prediction models

    • Su T
    • Jaki T
    • Hickey G
    • et al.
    N/AReaders
    3Citations
    Get full text
  • Features of asthma which provide meaningful insights for understanding the disease heterogeneity

    • Deliu M
    • Yavuz T
    • Sperrin M
    • et al.
    N/AReaders
    1Citations
    Get full text
  • Operator volume is not associated with mortality following percutaneous coronary intervention: Insights from the British Cardiovascular Intervention Society registry

    • Hulme W
    • Sperrin M
    • Curzen N
    • et al.
    N/AReaders
    2Citations
    Get full text
  • Impact of co-morbid burden on mortality in patients with coronary heart disease, heart failure, and cerebrovascular accident: A systematic review and meta-analysis

    • Rashid M
    • Kwok C
    • Gale C
    • et al.
    N/AReaders
    4Citations
    Get full text
  • Impact of operator volume for percutaneous coronary intervention on clinical outcomes: What do the numbers say?

    • Rashid M
    • Sperrin M
    • Ludman P
    • et al.
    N/AReaders
    3Citations
    Get full text

Professional experience

Senior Lecturer in Health Data Science

University of Manchester

May 2013 - Present