Introduction | Example 1: INLA piece-wise exponential and external knowledge | Prepare the models | Blend the curves | Example 2: Blending two HMC survHE models | Example 3: Blending HMC and frequentist models

{expertsurv} background fit | Using sample size formula

Introduction | 1. Robust Variance Estimation | 2. Model Diagnostics | MAIC Weight Diagnostics | Bayesian MCMC Diagnostics

Introduction | 1. Binary Outcome Data | Running the Analysis | 2. Continuous Outcome Data | 3. Count Outcome Data | 4. Comparing and Visualizing Results | 5. Alternative Outcome Scales

Introduction | Specifying Marginal Distributions | Manual Parameter Specification

Step 1: Create the Strategy Constructor | Step 2: Document the Class Fields | Step 3: Write the Core Calculation Function | Step 4: Register the S3 Dispatch Method | Step 5: Add a Print Method | (Optional) Step 6: Passing Random Seeds | Step 7: Automating Boilerplate with a Scaffolding Script (Optional) | How to Use It | Best Practices for Writing a Good Strategy | 1. Fail Fast and Validate Early | 2. Return a Rich model Object | 3. Handle Convergence Issues Gracefully | 4. Respect the verbose Flag | 5. Write Unit Tests for Your Method

Introduction | Matching-Adjusted Indirect Comparison (MAIC) | Outcome Regression Models | Parametric G-computation (Maximum Likelihood) | Parametric G-computation (Bayesian Inference) | Multiple Imputation Marginalization (MIM)