Introduction | 1. Load CHD Dataset | 2. Merge with RoB Assessments | 3. Format & Validate Input | 4. Add Indirectness & Adjust Effect Estimate for It | 5. Apply Bias Priors & Estimate Adjusted Effects | 5.1 Define Custom Priors | 5.2 Append priors and prepare data, and estimate adjusted effects | 6. Final data adjusted for both bias and indirectness | 7. Generate Bias-Adjusted Plot

Absolute directions of bias/indirectness | Point estimate below NULL | Point estimate above NULL - Bias towards the NULL | Adding the adjustment values | Example | Additive - Favours comparator - right - positive sign | Additive - Favours intervention - left - negative sign | Proportional - Point estimate above NULL - Towards the NULL - left - negative sign | Proportional - Point estimate below NULL - Away from NULL - right - positive sign | Proportional - Point estimate below NULL - Towards the NULL - right - positive sign | Proportional - Point estimate below NULL - Away from NULL - left - negative sign

Introduction | Step 1: Create the example dataset | Step 2: Convert to long format | Step 3: Add absolute direction | Step 4: Append bias priors

Introduction | Example Data | Launch Interactive App | Conclusion

Introduction | Create example dataset | Define Default Bias and Indirectness Priors | Run bias adjustment | Stricter sensitivity scenario | Compare results | Conclusion

Introduction | Loading your data | Example data sets | Summary plots (rob_summary()) | Examples: | RoB2.0 tool for randomized controlled trials | ROBINS-I tool for non-randomized studies of interventions | QUADAS-2 tool for diagnostic test accuracy studies | rob_summary() options | Overall risk-of-bias judgments (overall) | Weighted or un-weighted bar plots (weighted) | Colour scheme (colour) | Traffic light plots (rob_traffic_light()) | rob_traffic_light() options | Point size (psize) | The "Generic" template | Motivation | Varying numbers of domains | Domain names