1 Introduction to RWD and RWE for decision making in health care. 2 Case studies. 3 Validity control and quality assessment of real-world data and real-world evidence. 4 Introduction to Directed Acyclic Graphs (DAGs) for bias visualization. 5 Understanding and defining causal effects. 6 Estimands. 7 Confounding adjustment using propensity score methods. 8 Effect Modification in Non-Randomized Studies. 9 Confounding adjustment using prognostic score methods. 10 Dealing with missing data. 11 Principles of meta-analysis and indirect treatment comparisons. 12 Systematic review and meta-analysis of Real-World Evidence. 13 Individual Participant Data Meta-analysis of clinical trials and real-world data. 14 Dealing with irregular and informative visits. 15 Dealing with measurement error. 16 The role of machine learning in real-world evidence generation. 17 Introduction to methods for personalizing medicine. 18 Modeling Personalized Treatment Effects Using Multiple Data Sources. 19 Validation of prediction models for patient outcomes and individualized treatment effect. 20 Visualization and interpretation of individualized treatment rule results. 21 Digital Health in Real-World Data: Challenges and Opportunities. 22 RWE in regulatory and reimbursement decision-making. 23 Concluding remarks: Putting methods to practice.