Introduction | Use Case: Multi-Hospital Mortality Prediction | Scenario Setup | The Federated Network | The Clinical Model | $$\text{logit}(\text{mortality}) | Federated Workflow | Step 1: Each Site Fits Independently | Step 2: Sites Share Summaries with Coordinator | Path A: Share Full Posteriors (if permitted) | Path B: Share Only Summary Statistics (requires assumptions) | Central Coordinator: Stage 2 Shrinkage | Path A: Using Full Posteriors | Specify Network-Level Priors | Fit Hierarchical Model | Path B: Using Only Summary Statistics | Step 1: Check Normality Assumption | When CLT Fails: A Counter-Example | Step 2: Fit Using CLT Approximation | Compare Paths | Results: Improved Site-Specific Estimates | Visualize Shrinkage Effect | Quantify Uncertainty Reduction | Visualize Uncertainty Reduction | Clinical Impact: Network-Calibrated Predictions | Stage 1: Independent Site Predictions | Stage 2: Network-Calibrated Predictions | Visualize Prediction Changes | Privacy-Preserving Benefits | What Gets Shared | Compliance Benefits | Advanced Federated Scenarios | Scenario 1: Heterogeneous Models | Scenario 2: Meta-Analysis of Published Studies | Scenario 3: Iterative Federated Updates | Federated Learning Best Practices | 1. Establish Data Governance | 2. Standardize Stage 1 Models | 3. Verify Normality if Using Summaries | 4. Quality Control | 5. Sensitivity Analysis | 6. Transparent Reporting | Advantages of shrinkr for Federated Learning | When to Use Federated shrinkr | Summary | Additional Resources | Session Info

What is shrinkr? | A Complete Example: Regional Clinical Trial | Stage 1: Fit Independent Models with Stan | Stage 2: Apply Hierarchical Shrinkage | Step 1: Fit Mixture Approximation | Step 2: Specify Hierarchical Priors | Step 3: Fit the Hierarchical Model | Step 4: Examine Results | Step 5: Visualize Shrinkage | Alternative Input: Using Summary Statistics Only | Complete Stan → shrinkr Workflow Summary | Key Concepts Checklist | Common Pitfalls to Avoid | Next Steps | Session Info

Overview | The historical evidence | Hierarchical meta-analysis with shrinkr | Building the MAP prior | Robustify and form the posterior with beastt | Summary | References

Overview | Setup | The Veteran Dataset | Approach 1: Two-Stage (brms + shrinkr) | Stage 1: Fit Cox Model | Stage 2: Apply Hierarchical Shrinkage | Step 1: Extract posterior samples | Step 2: Fit a Gaussian mixture approximation | Step 3: Apply a hierarchical prior | Approach 2: Full Hierarchical (brms) | Approach 3: Two-Stage (Frequentist + shrinkr) | Compare Three Approaches | Numerical comparison | Visual comparison | Sensitivity Analysis: Exploring Different Priors | Prior densities | Heterogeneity estimates | Impact on cell type estimates | Key Takeaways | Session Info

Overview | Example: Multi-Region Clinical Trial | Simulate Stage 1 Results | Fit shrinkr Model | Working with posterior Package | Extract Draws | Basic Summaries | Check Convergence | Diagnostic Plots with bayesplot | Trace Plots | Density Plots | Interval Plots | Area Plots | Tidy Analysis with tidybayes | Spread and Gather Draws | Point and Interval Summaries | Custom Summaries with dplyr | Computing Contrasts | Modern Visualizations with ggdist | Halfeye Plots | Slab + Interval | Quantile Dotplots | Gradient Intervals | Comparing Pre- and Post-Shrinkage | Extract Both Estimates | Custom Comparison Plot | Complete Workflow Example | Advanced: Custom Analyses | Probability Statements | Tail Probabilities | Ranking Analysis | Further Reading

Table sizing | Dynamic scaling | Manual scaling | Figure sizing | Future developments

Introduction | Use Case: Multi-Hospital Mortality Prediction | Scenario Setup | The Federated Network | The Clinical Model | $$\text{logit}(\text{mortality}) | Federated Workflow | Step 1: Each Site Fits Independently | Step 2: Sites Share Summaries with Coordinator | Path A: Share Full Posteriors (if permitted) | Path B: Share Only Summary Statistics (requires assumptions) | Central Coordinator: Stage 2 Shrinkage | Path A: Using Full Posteriors | Specify Network-Level Priors | Fit Hierarchical Model | Path B: Using Only Summary Statistics | Step 1: Check Normality Assumption | When CLT Fails: A Counter-Example | Step 2: Fit Using CLT Approximation | Compare Paths | Results: Improved Site-Specific Estimates | Visualize Shrinkage Effect | Quantify Uncertainty Reduction | Visualize Uncertainty Reduction | Clinical Impact: Network-Calibrated Predictions | Stage 1: Independent Site Predictions | Stage 2: Network-Calibrated Predictions | Visualize Prediction Changes | Privacy-Preserving Benefits | What Gets Shared | Compliance Benefits | Advanced Federated Scenarios | Scenario 1: Heterogeneous Models | Scenario 2: Meta-Analysis of Published Studies | Scenario 3: Iterative Federated Updates | Federated Learning Best Practices | 1. Establish Data Governance | 2. Standardize Stage 1 Models | 3. Verify Normality if Using Summaries | 4. Quality Control | 5. Sensitivity Analysis | 6. Transparent Reporting | Advantages of shrinkr for Federated Learning | When to Use Federated shrinkr | Summary | Additional Resources | Session Info

Overview | The historical evidence | Hierarchical meta-analysis with shrinkr | Building the MAP prior | Robustify and form the posterior with beastt | Summary | References

What is shrinkr? | A Complete Example: Regional Clinical Trial | Stage 1: Fit Independent Models with Stan | Stage 2: Apply Hierarchical Shrinkage | Step 1: Fit Mixture Approximation | Step 2: Specify Hierarchical Priors | Step 3: Fit the Hierarchical Model | Step 4: Examine Results | Step 5: Visualize Shrinkage | Alternative Input: Using Summary Statistics Only | Complete Stan → shrinkr Workflow Summary | Key Concepts Checklist | Common Pitfalls to Avoid | Next Steps | Session Info

Overview | Setup | The Veteran Dataset | Approach 1: Two-Stage (brms + shrinkr) | Stage 1: Fit Cox Model | Stage 2: Apply Hierarchical Shrinkage | Step 1: Extract posterior samples | Step 2: Fit a Gaussian mixture approximation | Step 3: Apply a hierarchical prior | Approach 2: Full Hierarchical (brms) | Approach 3: Two-Stage (Frequentist + shrinkr) | Compare Three Approaches | Numerical comparison | Visual comparison | Sensitivity Analysis: Exploring Different Priors | Prior densities | Heterogeneity estimates | Impact on cell type estimates | Key Takeaways | Session Info

Overview | Example: Multi-Region Clinical Trial | Simulate Stage 1 Results | Fit shrinkr Model | Working with posterior Package | Extract Draws | Basic Summaries | Check Convergence | Diagnostic Plots with bayesplot | Trace Plots | Density Plots | Interval Plots | Area Plots | Tidy Analysis with tidybayes | Spread and Gather Draws | Point and Interval Summaries | Custom Summaries with dplyr | Computing Contrasts | Modern Visualizations with ggdist | Halfeye Plots | Slab + Interval | Quantile Dotplots | Gradient Intervals | Comparing Pre- and Post-Shrinkage | Extract Both Estimates | Custom Comparison Plot | Complete Workflow Example | Advanced: Custom Analyses | Probability Statements | Tail Probabilities | Ranking Analysis | Further Reading

Specifying headers | One line header | Multi-line header | Specifying footers | One line footer | Multi-line footer | Available utilities for standard or automated information | Add automatic page numbering in upper left | Add file path in bottom left and datetime in bottom right | Putting it all together | Note about sizing

Introduction | Data Description | Propensity Scores and Inverse Probability Weights | Inverse Probability Weighted Power Prior | Inverse Probability Weighted Robust Mixture Prior | Posterior Distributions | Posterior Summary Statistics and Samples | Simulation Studies | References

Introduction | Data Description | Propensity Scores and Inverse Probability Weights | Approximate Inverse Probability Weighted Power Prior | Inverse Probability Weighted Robust Mixture Prior | Posterior Distributions | Posterior Summary Statistics | Simulation Studies | References

Vignette Build Datetime | Generate a Proc Contents style report of a data frame in R | Display the result

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Add Big N to ADAE | Display the Results of Adding Big N to ADAE

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Read in ADAE, Restrict to Population and Add Population Variables | Display the Results for AE Body System and Preferred Term

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Read in ADAE and Generate Frequency Counts and Percents | Select Page(s) of Table 1 (1) | DDDATA for Table 1 (First few obs) | Table 1 RTF | Table 1 PDF | Select Page(s) of Table 2 (1) | DDDATA for Table 2 (First few obs) | Table 2 RTF | Table 2 PDF | Select Page(s) of Table 3 (1) | DDDATA for Table 3 (First few obs) | Table 3 RTF | Table 3 PDF | Select Page(s) of Table 4 (1) | Note: There is no DDDATA for Table 4 | Table 4 RTF | Table 4 PDF | Select Page(s) of Table 5 (1) | DDDATA for Table 5 (First few obs) | Table 5 RTF | Table 5 PDF | Select Page(s) of Table 6 (1) | DDDATA for Table 6 (First few obs) | Table 6 RTF | Table 6 PDF | Select Page(s) of Table 7 (1) | DDDATA for Table 7 (First few obs) | Table 7 RTF | Table 7 PDF | Select Page(s) of Table 8 (1) | DDDATA for Table 8 (First few obs) | Table 8 RTF | Table 8 PDF | Select Page(s) of Table 9 (1) | DDDATA for Table 9 (First few obs) | Table 9 RTF | Table 9 PDF | Select Page(s) of Table 10 (1) | DDDATA for Table 10 (First few obs) | Table 10 RTF | Table 10 PDF | Select Page(s) of Table 11 (1) | DDDATA for Table 11 (First few obs) | Table 11 RTF | Table 11 PDF | Select Page(s) of Table 12 (1) | DDDATA for Table 12 (First few obs) | Table 12 RTF | Table 12 PDF | Select Page(s) of Table 13 (1) | DDDATA for Table 13 (First few obs) | Table 13 RTF | Table 13 PDF | Select Page(s) of Table 14 (1) | DDDATA for Table 14 (First few obs) | Table 14 RTF | Table 14 PDF | Clean up Temporary Files

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Make Lists for Coding and Decoding ADSL Variables | (Codes for Ordering Categories, Decodes for Category Labels) | Perform the Coding and Decoding | Iterate over ADSL and Process a List of Categorical (Counts/Percents) and Numeric (Summary Statistics) Variables | Add Big N Value for Column Headers | Select Page(s) of Table 15 (1) | DDDATA for Table 15 (First few obs) | Table 15 RTF | Table 15 PDF | Select Page(s) of Table 16 (1) | DDDATA for Table 16 (First few obs) | Table 16 RTF | Table 16 PDF | Select Page(s) of Table 17 (1) | DDDATA for Table 17 (First few obs) | Table 17 RTF | Table 17 PDF | Select Page(s) of Table 18 (2) | DDDATA for Table 18 (First few obs) | Table 18 RTF | Table 18 PDF | Select Page(s) of Table 19 (1) | DDDATA for Table 19 (First few obs) | Table 19 RTF | Table 19 PDF | Select Page(s) of Table 20 (1) | DDDATA for Table 20 (First few obs) | Table 20 RTF | Table 20 PDF | Select Page(s) of Table 21 (1) | DDDATA for Table 21 (First few obs) | Table 21 RTF | Table 21 PDF | Select Page(s) of Table 22 (1) | DDDATA for Table 22 (First few obs) | Table 22 RTF | Table 22 PDF | Select Page(s) of Table 23 (1) | DDDATA for Table 23 (First few obs) | Table 23 RTF | Table 23 PDF | Clean up Temporary Files

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Select Page(s) of Figure 1 (1) | DDDATA for Figure 1 | Figure 1 RTF | Figure 1 PDF | Select Page(s) of Figure 2 (1) | DDDATA for Figure 2 | Figure 2 RTF | Figure 2 PDF | Clean up Temporary Files

Vignette Build Datetime | Create Folder Structure on Windows

Vignette Build Datetime | Load Libraries | Create a vector of packages to load. | Unload all packages in the list above. | Reload all packages in the list above. | Confirm all packages in the list above are loaded.

Vignette Build Datetime | Load Libraries | Define data library | Read in Format Data Set | Make List from Format Data Set | Apply the List to Create a Decode Variable on ADSL | Display the Results

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Read in ADAE and Apply Population | Generate Counts and Percents for AE Body System and Preferred Term then Denormalize It | Add Simple Page Numbers then Display the Dataframe using 30 Rows in Body of Report | Add Paging using NoSplitVars when it Won't Work with 30 Rows in Body of Report | Add Paging using NoSplitVars when it Will Work with 35 Rows in Body of Report

Vignette Build Datetime | Load Libraries | Define data library | Read SDTM DM and SUPPDM domains | Show Supplemental Data to Append | Combine DM and SUPPDM data sets | Display the Results

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Read in ADAE and Apply Population | Generate Counts and Percents for AE Body System and Preferred Term | Denormalize the AE Counts and Percents Data Set | Align the Columns for TLF Reporting | Display the Aligned AE Counts and Percents Data Set | Generate Counts and Percents for Baseline Characteristics Data | Denormalize the Baseline Characteristics Summary Statistics Data | Display the Aligned Baseline Characteristics Summary Statistics Data Set

Vignette Build Datetime | Load Libraries | Invoke Setup Function | Convert RFMTDIR Format Data Set to List | Display the Results

Vignette Build Datetime | Load Libraries | Define library as a libname | Use Libname to Access SDTM.AE Data | Add times to the character dates | Convert character dates to formatted R date variables | Display the results

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Read in ADAE and Apply Population | Generate Counts and Percents for AE Body System and Preferred Term | Denormalize the AE Counts and Percents Data Set | Display the Denormalized AE Counts and Percents Data Set | Derive Summary Statistics for Baseline Characteristics Data | Denormalize the Baseline Characteristics Summary Statistics Data Set | Display the Denormalized Baseline Characteristics Summary Statistics Data Set

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Update ADSL and ADAE | Generate Counts and Percents for AE Body System and Preferred Term | Display the Results for AE Body System and Preferred Term | Generate Counts and Percents for AE Preferred Term Only | Display the Results for AE Preferred Term Counts and Percents | Generate Counts and Percents for Demographic Data | Display the Results for Counts and Percents of Demographic Data

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Read in ADAE and Apply Population | Generate Counts and Percents for AE Body System and Preferred Term then Denormalize It and Add Page Numbers | Example 1: Generate a List of Relative Column Widths for RTF Reporting with No Defaults | Example 2: Generate a List of Relative Column Widths for RTF Reporting with Defaults

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Update ADSL and ADVS | Generate Summary Statistics for Baseline Characteristics | Display the Results for Baseline Characteristics | Generate Summary Statistics for Vital Signs with Constant Precision | Display the Results for Vital Signs with Constant Precision | Generate Summary Statistics for Vital Signs with Varying Precision

Vignette Build Datetime | Load Libraries | Set Up the Global Environment | Use Libname to Access and Subset Data | Display the Result, add Study ID to Title

Vignette Build Datetime | Load Libraries | Define Libnames | Use Libname to Access and Subset Data | Display the Result

Vignette Build Datetime | Load Libraries | Set Up the Reporting Environment | Read in ADAE and Apply Population | Generate Counts and Percents for AE Body System and Preferred Term then Denormalize | Stack Body System and Preferred Term | Display the Data Set Containing the Stacked Column

Vignette Build Datetime | Load Libraries | Build Data Frames for Compare | Compare the Data Frames | Display the Results

Vignette Build Datetime | Load Libraries | Append data frames with different columns | Display the Results

Vignette Build Datetime | Before Adding Labels | After Adding Labels Using Base Method | After Adding Labels Using Hmisc Method

Vignette Build Datetime | Add Decode Variables to the Incoming List | Remove Decode Variables from the Incoming List

Vignette Build Datetime | Load Libraries | Before Filling Missings | After Filling Missings

Vignette Build Datetime | Load Libraries | Example 1: SAS Type Expansion of Variable List | Example 2: SAS Type Expansion of Variable List | Example 3: SAS Type Expansion of Variable List

Render to PDF | Render to RTF | Multi-render

Default sizing options | Advanced use

Introduction | Understanding the Structure of the Experimental Design | Stage 1. Identifying the experimental factors | Stage 2. Specifying the factor levels | Stage 3. Choosing the Experiment Scheme | Stage 4. Identifying the structure of the experimental design and constructing the Layout Structure | Stage 5. Assigning the levels and degrees of freedom | Example: Split-plot split-block experiment | Using the structure of the experimental design and the randomisation to construct a mixed model | Stage 6a. Performing the randomisation | Stage 6b. Describing the randomisation | Stage 7a. Defining the experimental factors as fixed or random | Stage 7b. Constructing the Restricted Layout Structure | Stage 7c. Illustrating the Restricted Layout Structure using a Hasse Diagram | Stage 8. Selecting the mixed model | Example: Split-plot split-block experiment (continued) | Disclaimer | References

Introduction | Data Description | Propensity Scores and Inverse Probability Weights | Inverse Probability Weighted Power Prior | Inverse Probability Weighted Robust Mixture Prior | Posterior Distributions | Posterior Summary Statistics and Samples | References