Introduction to Generalised Linear Models (GLME01)
Are you working with disease counts, incidence rates, or binary health
outcomes? Generalised Linear Models (GLMs) are a powerful and flexible
statistical framework ideal for epidemiologists analysing real-world
data—from infection presence to risk factor modelling.
Who should attend?
-
Public health researchers modelling disease occurrence, prevalence, or
exposure data
-
Epidemiologists working with count or binary outcome data (e.g. cases
vs. controls, infected vs. uninfected)
-
Data analysts in health agencies or NGOs needing interpretable,
reproducible models
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Postgraduate students or early-career professionals looking to
strengthen their statistical modelling in R
What you'll learn
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The theory and practical application of GLMs, including logistic
regression for binary outcomes and Poisson/negative binomial models for
counts
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How to handle common epidemiological challenges like overdispersion,
zero-inflated data, and non-normal error structures
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R implementation using the glm() function, with a strong focus on
interpretation, diagnostics, and communication of results
Course format
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Live, online sessions, 10 days, 4 hours per day, with real-time
instruction and hands-on coding in R
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All sessions recorded for flexible learning across time zones
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Next session: September 8-12 & 15-19, 2025
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Course fee: *First 10 places £400* - Normal price £450
Prerequisites
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Basic knowledge of R and RStudio (e.g., importing data, working with
data frames, and basic plotting)
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Understanding of core statistical concepts like means, variance and
correlation.
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No prior experience with GLMs required—all methods are taught from first
principles, focusing on application over theory
------------------------------
Apply statistical modelling with confidence in your epidemiological research
Register or find out more on the PR stats course page or email
oliverhoo...@prstatistics.com.
--
Oliver Hooker PhD.
PR stats
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