Om kursen
The course content is structured in six sections, each consisting of preparation through self-study of selected literature (3.5 hours preparation per session) and 3-hour in-person sessions that include a theoretical component (90 minutes per session) and a bioinformatics lab (90 minutes per session). The final exam consists of a 2-page structured essay (mini-review) about a pre-specified causal inference topic (workload 20 hours).
The specific contents of each course section is listed below.
Session 1: Introduction to causal thinking
Theory
Ø Objective: Introducing basic concepts of causality.
Ø Topics:
· History and philosophy of causality
· Introduction to causal reasoning and its importance in research
→ Preparation: Reding excerpts from Modern Epidemiology (Rothmann, et al.)
→ Lecture (45 minutes): Clemens Wittenbecher
→ Group work (45 minutes): Moderated discussion on the causal mechanism behind published associations making explicit use of different causal concepts and causal language.
Lab
Ø Objective: Describing univariable and multi-variable distributions.
Ø Topics: Introducing statistical measures and basic concepts of frequentist hypothesis testing.
· Measures of Location and Distribution
· Measures of Correlation and Association
· Interpretation and misconceptions concerning P-values
→ Preparation: Install R and software packages
→ Lecture (30 minutes): Jakub Morze
→ Group work (60 minutes): Generate histograms, violine plots, heat maps, and association tables.
Session 2: Causal concepts and statistical models
Theory
Ø Objective: Linking causal concepts to data analysis.
Ø Topics: Introduce counterfactual thinking and methodologies to formalize causal assumptions.
· Counterfactuals and Potential Outcomes
· Directed Acyclic Graphs (DAG)
→ Preparation: Reding excerpts from The Book of Why (Judea Pearl), Observation & Experiment (Paul Rosenbaum)
→ Lecture (45 minutes): Clemens Wittenbecher
→ Group work (45 minutes): Using DAGs to formalize and explain specific causal mechanisms.
Lab
Ø Objective: Understanding that a statistical model can encode causal assumptions.
Ø Topics:
· Multi-variable linear regression
· Derive sufficient adjustment sets from DAGs estimates.
→ Preparation: Download datasets and install R packages
→ Lecture (30 minutes): Jakub Morze
→ Group work (60 minutes): Analyze cross-sectional data and compare effect estimates across models with different adjustment sets.
Session 3: Study design I ecological and cross-sectional studies
Theory
Ø Objective: Understanding cross-sectional study designs and their strengths and limitations in etiological research.
Ø Topics: Introduce basic cross-sectional study design and highlight important contributions.
· Ecological studies
· Cross-sectional studies
· Reverse causation and other study design-specific sources of bias
→ Preparation: Paper I (Cross-sectional studies)
→ Lecture (45 minutes): Clemens Wittenbecher
→ Group work (45 minutes): Extracting the causal assumptions from Paper I and evaluating the validity of causal conclusions
Lab
Ø Objective: Learning to analyze cross-sectional data in linear and logistic regression models.
Ø Topics:
· Continuous outcomes and multi-variable linear regression models
· Categorical outcomes and logistic regression models
→ Preparation: Download datasets and install R packages
→ Lecture (30 minutes): Jakub Morze
→ Group work (60 minutes): Analyze cross-sectional data and visualize effect estimates and confidence intervals.
Session 4: Study design II longitudinal and prospective studies
Theory
Ø Objective: Understanding the design of prospective studies and the advantages of longitudinal data collection.
Ø Topics: Introduce basic longitudinal and prospective studies and highlight important contributions.
· Prospective cohort studies
· Nested case-control and case-cohort studies
· Examples of repeated exposure assessment
· Remaining sources of bias and the role of measurement error in prospective data analysis
→ Preparation: Paper II (Prospective studies) and Paper III (Repeated measurements)
→ Lecture (45 minutes): Clemens Wittenbecher
→ Group work (45 minutes): Extracting the causal assumptions and evaluating sources of bias from Paper II or Paper III and evaluating the validity of causal conclusions
Lab
Ø Objective: Learning to analyze matched case control studies in conditional logistic regression models and time-to-event data in Cox PH regression models.
Ø Topics:
· Conditional logistic regression models
· Cox PH regression models
→ Preparation: Download datasets and install R packages
→ Lecture (30 minutes): Jakub Morze
→ Group work (60 minutes): Analyze prospective cohort data in conditional logistic regression (nested matched case-control studies) and time-to-event data (Cox PH regression) visualize effect estimates and confidence intervals.
Session 5: Causal mediation analysis
Theory
Ø Objective: Understanding the design, assumptions, and interpretation of causal mediation analysis.
Ø Topics:
· Decomposition of total effects into direct and indirect effects
· Counterfactuals and do calculus
→ Preparation: Reding excerpts from Causality (Judea Pearl) and Paper III (applied mediation analysis)
→ Lecture (45 minutes): Clemens Wittenbecher
→ Group work (45 minutes): Evaluate the applied mediation analysis in Paper III against the assumptions for causal interpretation of the mediation analysis results
Lab
Ø Objective: Conducting causal mediation analysis.
Ø Topics:
· Statistical approaches and R-packages for causal mediation analysis
· Communicating results from causal mediation analysis
→ Preparation: Download datasets and install R packages
→ Lecture (30 minutes): Jakub Morze
→ Group work (60 minutes): Implement and report a pre-specified causal mediation analysis.
Session 6: Effect modification analysis
Theory
Ø Objective: Understanding the assumptions underlying causal inferences based on population averages and recognizing potential sources of effect heterogeneity.
Ø Topics:
· The concept of effect modification.
· Interaction in biomedical research: synergies and antagonistic effects.
→ Preparation: Paper IV and paper V (additive interaction analysis theory and example)
→ Lecture (45 minutes): Clemens Wittenbecher
→ Group work (45 minutes): Using DAG(s) to illustrate the causal mechanism(s) underlying known interactions and effect modifications in biomedical research
Lab
Ø Objective: Learning basic statistical approaches to detect and quantify effect heterogeneity.
Ø Topics:
· Stratification
· Additive vs. multiplicative interaction tests
· Reporting of model estimates in the presence of effect modifiers
→ Preparation: Download datasets and install R packages
→ Lecture (30 minutes): Jakub Morze
→ Group work (60 minutes): Conduct interaction tests and communicate study results when interactions were detected.
