Glen Martin

Glen Martin, PhD

Dr, PhD

  • Rm 2.002, Vaughan House, Portsmouth Street, University of Manchester

    M13 9GB Manchester

    United Kingdom

Personal profile

Overview

  • Senior Lecturer in Health Data Science, University of Manchester
  • Research interests in clinical prediction models, using statistical methods in observational data, and evidence synthesis methodologies
  • Lecturer on the MSc Health Data Science, and MSc Health Informatics courses

Biography

Glen is a Senior Lecturer in Health Data Science at the Health e-Research Centre, University of Manchester. 

As a health data scientist, Glen undertakes multidisciplinary research at the intersection of mathematics, statistics, data science and clinical investigation. His research focuses on utilising routinely collected health data to improve healthcare by optimising the way in which predictive/ prognostic models are developed and used in clinical practice.

Qualifications

  • 2014-2017: PhD Medicine (Health Informatics), University of Manchester. Thesis Title: "Methodology in Developing Clinical Prediction Models within Local Populations: applications in transcatheter aortic valve implantation".
  • 2013-2014: MSc Statistics, Lancaster University
  • 2010-2013: BSc Mathematics, Lancaster University

Research interests

With an overarching goal of improving the way prognostic/ prediction models are used in routinely collected health data, Glen's research can be summarised into four pillars of research interests:

1) Understanding the mechanisms that drive and underpin observational data: this covers exploring why observational data are present (or missing) since the presence/absence of information might be informative of an individual’s health.

2) Optimising the ways in which risk models are developed. This focusses on statistical methods development to improve risk prediction modelling. Particular interests include multivariate (multi-outcome) risk prediction, penalisation methods and sample size.

3) Appropriate re-use of prediction models: how can we make better use of existing knowledge/ research in prognostic modelling? This includes both meta-analysis and validation studies

4) Apply existing and novel statistical methodologies into real-world clinical/applied investigations

Teaching

MSc Health Data Science:

  • Co-module lead for "Introduction to Health Data Science" module (15 credits)
  • Co-module lead for "Statistical Modelling and Inference for Health" module (15 credits)

MSc Health informatics (UCL/UoM joint Award):

  • Co-Module lead for "Principles of Health Data Analytics" module (15 credits)
  • Lecturer on "Digital Transformation Project" module (15 credits)

 

Further information

Email: glen.martin@manchester.ac.uk

Memberships of committees and professional bodies

Turing Fellow, The Alan Turing Institute, 2021 - 2022

Royal Statistical Society, GStat

Methodological knowledge

Statistics

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 4 - Quality Education
  • SDG 5 - Gender Equality
  • SDG 16 - Peace, Justice and Strong Institutions

Research Beacons, Institutes and Platforms

  • Digital Futures
  • Christabel Pankhurst Institute
  • Healthier Futures

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