Course content

Study design, measures of disease occurrence, measures of effect, intern validity, Directed acyclic graphs (DAGs), interpretation of multivariable models, causal inference, causal interaction and effect measure modification, screening, instrumental variable estimation, family designs, case only designs.

Learning outcome

• have basic knowledge of various study designs that are most common in population-based and clinical research (cross-sectional studies, cohort studies, case-control studies and randomized controlled trials).
• be able to interpret different measures of disease occurrence (prevalence, incidence proportion and incidence rate).
• be able to interpret different measures of the relationship between exposure / treatment and disease based on absolute and relative risk (risk difference, incidence rate difference, numbers needed to treat versus risk ratio, incidence ratio, odds ratio, hazard ratio)
• have basic knowledge of common challenges in observational studies (selection bias, information bias and confounding).
• have basic knowledge of and be able to interpret Directed asyclyc graphs (DAGs)
• have a basic understanding of assessment of precision in epidemiological studies with emphasis on interpretation and use of p-values ​​and confidence intervals
• understand the principles and be able to interpret multivariable analyzes.
• be able to distinguish between and have knowledge of the concepts: causal effect (causality) and statistical association.
• be able to distinguish between causal interaction and effect measure modification
• have basic knowledge of screening
• know the principles of instrument variable analysis, different family designs and case only studies

Learning methods and activities

Teaching modalities: Lectures and problem solving.

Compulsory assignments

• Practice tasks
• Mandatory attendance at lectures

Recommended previous knowledge

Recommended background knowledge: KLMED8009 Clinical Research, KLMED8004 Medical statistics 1, KLMED8005 Medical statistics 2 Introductory course in epidemiology (eg. master course in epidemiology)

Required previous knowledge

Admission requirements: Master degree or similar. Medical students at The Student Research Programme. Candidates with a lower degree will be assessed individually.

Course materials

Books:

Hernan M, Robins J: Part I, Part II, chapter 16, https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Rothman K, Greenland S and Lash (2012): Chapter 2, 3, 5-10 in Modern epidemiology

Papers:

Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol 2002;155:176-184

Hernán MA, Hernández-Díaz S, Robins JM (2004): A structural approach to selection bias. Epidemiology. 2004 Sep;15(5):615-25.

JanszkyI, Ahlbom A, Svensson AC (2010): The Janus face of statistical adjustment: confounders versus colliders. Eur J Epidemiol. 2010 Jun;25(6):361-3. Epub 2010 May 7.

Glymour M, Weuve J, Berkman L, Kawachi I, Robins J. When is baseline adjustement usuful in analysis of change? An example with education and cognitive change. Am J Epid 2005; 162:267-78

Krieger N, Davey Smith G. The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology. Int J Epidemiol 2016; 45: 1787-808.