Original plain-language educational syntheses prepared by Omokhodion Alfred Eriakha from cited chapters in the Handbook of Epidemiology. The diagrams and charts are author-created illustrations.
Continuous covariates & dose-effect analysis
Understanding Dose & Risk
How epidemiologists analyze continuous exposures, model dose-response relationships and avoid misleading categorization.
Foundations of epidemiology
Basic Concepts: Cause and Effect
A plain-language guide to causal reasoning, measures of disease and effect, confounding, bias and epidemiologic inference.
Advanced quantitative methods
Bayesian Methods in Epidemiology
An accessible introduction to priors, likelihoods, posterior inference, model checking and spatial disease mapping.
Causal inference
Causal Directed Acyclic Graphs
How DAGs make causal assumptions explicit and guide decisions about confounding, colliders and adjustment sets.
Causal inference
Causal Reasoning and Inference in Epidemiology
How epidemiologists move from association to intervention-based causal questions and assess whether an estimate can support a causal interpretation.
Observational study design
Cohort Studies
How researchers define exposure groups, follow populations over time and estimate disease occurrence and comparative risk.
Bias and effect heterogeneity
Confounding and Interaction
How confounding can distort comparisons and how interaction reveals when an exposure's effect differs across populations or settings.
Measurement methods
Exposure Assessment
How researchers define and measure exposures, evaluate error and translate complex contact histories into analyzable variables.
Longitudinal and clustered data
Generalized Estimating Equations
How researchers estimate population-average relationships when observations are repeated within people or clustered within groups.
Health systems and outcomes
Health Services Research
How researchers study access, quality, utilization, cost and outcomes across healthcare systems.