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HPG 6104 – Epidemiological Methods II

University of Nairobi
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Lecture List

All lectures for HPG 6104 this semester. Slides and supporting materials are posted in the portal and lecture folders.

Semester lectures

Order reflects the teaching sequence used in class.

Lecture 01: Measures of Disease Frequency and Association

Prevalence, incidence, mortality, RR/RD/OR, attributable fractions; interpretation and common pitfalls.

Suggested student task: complete the Week 1 worked examples + short problem set.

Lecture 02: Categorical Data Analysis & Contingency Tables

2×2 and r×c tables, chi-square, Fisher’s exact, association testing and interpretation.

Suggested student task: compute and interpret chi-square tests from given tables.

Lecture 03: Confounding and Effect Modification

Non-exchangeability, stratification, effect measure modification, interpretation discipline.

Suggested student task: stratified RR/OR analysis + interpret confounding vs modification.

Lecture 03b: Introducing the Practical Dataset in R

Dataset orientation, cleaning, simple summaries, and preparing for regression and design questions.

Suggested student task: reproduce the basic R workflow and generate summary tables.

Lecture 04: Causal Inference in Epidemiology

Counterfactual thinking, causal questions, assumptions, and where observational studies break.

Suggested student task: identify estimands and sketch causal pathways for case vignettes.

Lecture 05: Advanced Confounding Control Measures

Design and analysis strategies: matching, stratification/standardization, conceptual role of randomization.

Suggested student task: decide which control method fits a scenario and justify.

Lecture 06: Logistic Regression in Epidemiology

Binary outcomes, OR interpretation, adjusted effects, confounding control, output reading.

Suggested student task: interpret regression output (coefficients, ORs, CIs, p-values) in plain English.

Lecture 07: Epidemiologic Study Designs – Strengths, Biases & Causal Utility

Cross-sectional, case-control, cohort, RCT as benchmark; strengths/limitations and causal usefulness.

Suggested student task: map research questions to designs and anticipate biases.

Lecture 08 & 09: Bias, Validity, and Modern Methods IIn Epidemiology

Selection and information bias, measurement, validity threats, and modern analytic cautions (prediction vs causal; ML).

Suggested student task: critique a short study abstract for validity threats and interpretability.

Lecture 10: Integrating Epidemiologic Methods IIn Public Health Practice

How to connect methods to decisions: interpreting evidence, translating findings into practice and policy.

Suggested student task: “So what?” exercise — interpret results and propose an action plan.