Introduction | Alpha Spending Functions | From Spending to Boundaries | Repeated and Sequential P-values | Case Study using repeated p-values and look_back = FALSE | Hypotheses and Graph | Observed P-values | Running the Procedure (look_back = FALSE) | Repeated p-values | Nominal boundaries | Comparison with Paper Values | Boundary table | Case Study using sequential p-values and look_back = TRUE | Trial Setup | Graph and Alpha Allocation | P-values and Information Fractions | Sequential P-values (look_back = TRUE) | Per-hypothesis look_back | When look_back makes a difference | Customizing Spending Functions: Spending Time | Monitoring: Adjusting for Changed Final Information | Additional Examples | Different Spending Functions per Hypothesis | Using Spending Functions from Other Packages | User-Defined Spending Functions | Summary | References

Introduction | Bonferroni-based procedures | Bonferroni test | Weighted Bonferroni test | Holm Procedure | Weighted Holm Procedure | Fixed sequence procedure | Fallback procedure | Serial gatekeeping procedure | Parallel gatekeeping procedure | Successive procedure | Hochberg-based procedures | Hochberg procedure | Simes-based procedures | Hommel procedure | Parametric procedures | Šidák test | Dunnett test | Weighted Dunnett test | Dunnett procedure | Reference

Introduction | Basic usage | Initial graph | Update graph | Perform graphical MCPs | Power simulations | References

Motivating example | Create the graph | Perform the graphical multiple comparison procedure | Adjusted p-values and rejections | Obtain final and intermediate graphs after rejections | Obtain possible orders of rejections | Obtain adjusted significance levels | Power simulation | Input: Marginal power for primary hypotheses | Input: Marginal power for secondary hypotheses | Input: Correlation structure to simulate test statistics | User-defined success criteria | Output: Simulate power | Reference

Introduction | 1. Alpha Spending Functions | Comparison with gsDesign | Comparison with rpact | Detailed Example | 2. Group Sequential Boundaries | 3. Correlation Matrix | 4. Repeated P-values | 5. Sequential P-values | Relationship Between Sequential and Repeated P-values | 6. Wang-Tsiatis Boundaries | Verification of boundary shape | Summary | References

Introduction | Power simulations | Bonferroni tests | Hochberg tests | Simes tests | Parametric tests | Mixed tests of Bonferroni, Hochberg and Simes | Mixed tests of parametric and one of Bonferroni, Hochberg and Simes | Conclusions

Motivating example | Create a graph | Perform the graphical multiple comparison procedure based on the closure principle | Bonferroni tests | Obtain the closure | Obtain adjusted significance levels | Mixed procedures for graphical approaches | Parametric tests for primary hypotheses | Parametric tests for primary hypotheses and Simes tests for secondary hypotheses | Power calculation | Input: Marginal power for primary hypotheses | Input: Marginal power for secondary hypotheses | Input: Correlation structure to simulate test statistics | User-defined success criteria | Output: Calculate power | Reference