VET411 Veterinary epidemiology I – Fundamental theory and study design

This course provides an introduction to designing and evaluating epidemiological studies. Students learn how to develop a study protocol for either a randomized controlled trial or an observational study, and how to critically assess study designs used in scientific research. The course covers core epidemiological measures, basic causal thinking, and the specification of aims and estimands. Students are introduced to common observational study designs as well as randomized trials, and diagnostic test evaluations.

Key topics include identifying major sources of bias, understanding sampling strategies, performing simple power and sample size calculations, and evaluating data quality. Students also learn how to plan data collection (including questionnaires), plan the stucture of datasets, and outline a target trial and its observational emulation. Directed acyclic graphs (DAGs) are used to describe causal assumptions and guide adjustment strategies. The counterfactual framework and assumptions required for causal effect estimation is introduced.

The overall learning objectives for this course are to enable students to develop a sound protocol for their own randomized controlled trial or observational study, and to critically evaluate the study design of studies reported in scientific publications. This entails learning to:

• Estimate and interpret measures of disease occurrence and measures of association.
• Describe and apply approaches to studying causality, including the counterfactual framework.
• Formulate well-defined aims for epidemiological studies.
• Specify estimands and target estimates that address the study aims.
• Describe the characteristics of randomized controlled trials, evaluations of measurements and diagnostic tests, and observational study designs—including prevalence surveys, cohort studies, case‑cohort and case‑control designs, cross-sectional studies, interrupted time-series designs, and self‑controlled case-series designs.
• Discuss the strengths and limitations of different study designs and justify the choice of design for one’s own study.
• Identify sources of bias (selection, information, confounding, immortal time) in both planned studies and published articles.
• Discuss how sampling strategies influence selection bias and generalizability, and design a reasonable sampling scheme for one’s study.
• Make simple estimations of statistical power and sample size and discuss when more advanced approaches may be necessary.
• Propose approaches for evaluating data quality—including the quality of registry and other secondary data—and discuss how data quality affects information bias.
• Plan well‑structured, analysis‑ready datasets.
• Plan a questionnaire‑based data collection, including questionnaire development.
• Specify a target trial (eligibility criteria, time zero, treatment strategies, outcome, follow‑up, causal estimand) and design an observational emulation that aligns with it.
• Use directed acyclic graphs (DAGs) to articulate causal assumptions, assess identifiability of causal estimands, and determine required data and appropriate adjustment sets.
• Consider whether the assumptions required for causal effect estimation (exchangeability, positivity, consistency, no unmeasured confounding, no interference) are likely to be satisfied in a planned study.