Prerequisites | 1. Connect to Eunomia | 2. Create covariate settings | 3. Standalone usage — call the builder directly | Inspect the covariates table | Inspect the covariate reference | Inspect the analysis reference | 4. FeatureExtraction integration | 5. Combining with standard FeatureExtraction covariates | 6. Multiple domains and time windows | 7. Using concept-set features | Cleanup | Summary

Prerequisites | 1. Connect to Eunomia and create cohorts | 2. Define analysis windows | 3. Base feature — Condition Occurrence (start type) | 4. Base feature — Drug Exposure | 5. Base feature — Condition Era (overlap type) | 6. Base feature — Drug Era (overlap type) | 7. Base feature — Procedure Occurrence | 8. Base feature — Measurement | 9. Base feature — Observation | 10. Base feature — Visit Occurrence (overlap type) | 11. Non-aggregated (patient-level) output | 12. Multiple domains at once | 13. Cohort features — Using GiBleed cohort as a covariate | 14. Cohort features — Overlap type | 15. SQL rendering without execution | 16. Multiple time windows | 17. Combined — Base + Cohort features in one run | 18. Characterising a different cohort — Diclofenac | 19. Error handling with stopOnError = FALSE | 20. Cleanup | Session info

Overview | Setup | Create Example Cohorts with Eunomia | Run Post-Index Pathway Analysis | Run Pre-Index Pathway Analysis | Understanding the Returned Objects | Using Separate Target and Event Cohort Tables | Building an Event Sequence Graph | Quick visualization | Transition probabilities | Downstream igraph analysis

Overview | Typical Workflow | Step 1: Define Concept Sets as Data Frames | Step 2: Convert to Concept Set Expressions | Step 3: Build the Cohort | Parameters | Step 4: Export to JSON | End Strategies | Default: observation period end date | Fixed exit: offset from index | Drug exit: era-based persistence | Event Limits | Source Criteria | Structure of the Output

Overview | 1 Basic message levels | 2 Section structure: headers, rules, and blank lines | 3 Structured output: lists and key-value tables | Named list | Key-value table | 4 Iteration patterns | 4a Logging inside a loop | 4b Safe iteration with msg_try() | 4c Verbose mode — conditional logging inside helpers | 5 Timing expressions with msg_timed() | Timing an iteration | 6 Error and warning handling | Raising styled errors with msg_abort() | Raising styled warnings with msg_warning() | msg_try() on_error modes | 7 Debug messages | 8 Progress bar (interactive sessions) | 9 Spinner (interactive sessions) | 10 Putting it all together — annotated pipeline

Introduction | Example Input | Model Choices | Cox Model | Parametric Models | Comparing Models | Stratified Fitting | Working with Returned Curves | When to Use Which Model | Summary

Introduction | Package Surface | Connect to Eunomia | Prerequisite: Target and Outcome Cohorts | Build Survival Data | Fit a Kaplan-Meier Model | Inspect the Returned Data | Plot a Returned Curve | Fit Cox and Parametric Models | Cleanup | Summary

Introduction | Connect to Eunomia | Example 1: Build a Survival Dataset | Example 2: Overall Kaplan-Meier Summary | Example 3: Compare Gender Groups | Example 4: Compare Age Groups | Example 5: Compare Model Families | Example 6: Produce a Simple Report Table | Example 7: Plot from Returned Data | Cleanup | Summary

Introduction | Example Data | Gender Stratification | Age-Group Stratification | Using Both Stratifiers | Extract Stratum-Specific Curves | Plot Separate Strata | Stratified Cox and Parametric Fits | Summary