This course takes you beyond descriptive epidemiology and into the practical world of data analysis. You will learn how to build, interpret, and critically evaluate regression models for a wide range of health outcomes—from continuous measures like blood pressure to binary events, counts, survival times, and repeated observations. Along the way you will tackle real analytical challenges: choosing the right model, handling confounding and interaction, diagnosing model fit, and accounting for the clustered and longitudinal structures that are common in health research. Each lesson pairs conceptual understanding with hands-on reasoning so you can confidently apply these techniques to real datasets.
Select a lesson below to begin. Each module includes interactive content, knowledge checks, reflections, and a final assessment.
1
Modelling Concepts
Introduction to statistical modelling, general linear models, maximum likelihood estimation, and model-building foundations.
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2
Linear Regression
Regression analysis, hypothesis testing, X-variable coding, collinearity detection, and interaction effects.
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3
Model-Building Strategies
Purposeful selection, change-in-estimate, stepwise procedures, and multi-level model building for epidemiologic research.
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4
Logistic Regression
Binary outcomes, odds ratios, maximum likelihood estimation, goodness-of-fit, and ROC analysis.
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5
Ordinal & Multinomial Models
Proportional odds models, multinomial logistic regression, and methods for multi-category outcomes.
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6
Count & Rate Data
Poisson regression, overdispersion, negative binomial models, and zero-adjusted count models.
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7
Survival Data
Kaplan-Meier estimation, Cox proportional hazards, parametric survival models, and frailty models.
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8
Introduction to Clustered Data
Hierarchical data structures, ICC, design effects, and methods for handling clustering in epidemiologic analyses.
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9
Mixed Models for Continuous Data
Random intercepts, random slopes, contextual effects, REML estimation, and model diagnostics.
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10
Mixed Models for Discrete Data
GLMMs, logistic and Poisson random effects models, SS vs PA interpretation, and estimation methods.
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11
Repeated Measures Data
Correlation structures, trend models, transition models, GEE, and analysis of longitudinal data.
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