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Methods in Epidemiology

Office Hours — the companion podcast for HSCI 341

Each episode is a relaxed walkthrough of the week's lesson — audio, summary, and full transcript on every page.

Foundations & applied surveillance

Lessons 01 — 02

Introduction & Causal ConceptsFoundations of epidemiological thinking, causal inference, and the counterfactual framework.

Surveillance & Outbreak InvestigationRoutine surveillance systems, outbreak detection, and field-epidemiology response — case definitions, epi curves, and contact tracing.

Population & measurement

Lessons 03 — 04

SamplingSampling strategies, bias, and how sample design affects the validity of epidemiological findings.

Questionnaire DesignPrinciples of measurement, question construction, and minimizing information bias in surveys.

Measures & tests

Lessons 05 — 07

Measures of Disease FrequencyPrevalence, incidence, and mortality rates as tools for quantifying disease occurrence in populations.

Screening & Diagnostic TestsSensitivity, specificity, predictive values, and evaluating the accuracy of screening programs.

Measures of AssociationRisk ratios, odds ratios, and rate ratios for quantifying exposure–outcome relationships.

Study designs

Lessons 08 — 10

Review of Study Design ConceptsA refresher on observational study designs, cohort and case-control studies, ecological studies, and evidence synthesis.

Hybrid Study DesignsNested case-control, case-cohort, and case-crossover designs that combine elements of cohort and case-control studies.

Controlled StudiesRandomized controlled trials, quasi-experimental designs, allocation, blinding, and the role of experimental control in epidemiology.

Validity & causal inference

Lessons 11 — 12

Validity in Observational StudiesInternal and external validity, selection bias, information bias, and confounding as the three dominant threats to validity in observational research, with strategies to anticipate and mitigate each.

Confounding & Causal InferencePre-analysis design choices, detection through stratification and DAGs, multivariable adjustment, and the full causal-inference framework that closes the bias triad.