Introduction | Setup | Example Data | Define Variables | Validating Data | Catching Validation Errors | Summarising Missing Data | Missing by Visit | Subject Patterns | Summary by Treatment Group | Preparing ICE Data | Complete Workflow | Summary

Introduction | Prerequisites and Setup | Threshold-Based Responder (CHG > 3) | Analyse | Pool | Results | Clinical Cutoff Responder (CHG > 5) | Derive the New Endpoint | Pool and Display | Storing Results as ARD | Caveats

Introduction | Setup and Data | Data Preparation | Validation | Missingness Summary | rbmi Analysis Pipeline | Specify the Imputation Method | Fit the Imputation Model | Generate Imputed Datasets | Analyse Each Imputed Dataset | Pool Results | Tidying Results | Efficacy Table | Forest Plot | Treatment Difference Mode | LS Mean Display Mode | Binary/Responder Analysis

Introduction | Statistical Context | Step 1: Setup and Data Preparation | Step 2: Define Imputation Model | Step 3: Add Responder Variables | Step 4: Continuous Endpoint Analysis (CHG) | Step 5: Responder Endpoint Analysis (CRIT1FLN) | Define Analysis Function | Define Variables and Run Analysis | Final Notes | Efficient Storage | See Also

Introduction | The Storage Problem | Setup | Example with Package Data | Reducing Imputed Data | What's in the Reduced Data? | Expanding Back to Full Data | Verifying Data Integrity | Practical Workflow | Save Reduced Data | Load and Analyse | Storage Comparison | When to Use This Approach | Edge Cases | No Missing Data | Single Imputation | Summary

Introduction | Setup | Inspecting Draws with describe_draws() | Inspecting Imputations with describe_imputation() | MI Diagnostic Statistics in ARD | When Diagnostics Are Not Available | Learn More