Bioequivalence tests for AUC and Cmax | Estimating power at a fixed sample size | References

Overview | Parameters required for count outcomes | A published Poisson benchmark | A count outcome study example | Step 1: Define the study and theoretical targets | Step 2: Run a pilot with sampleSize() | Step 3: Validate the generated data and estimands | 3a. Arm-specific parameters | 3b. Endpoint correlations | 3c. Estimand direction and sampling distribution | Step 4: Check Monte Carlo stability and precision | Step 5: Check Type I error at the equivalence boundaries | Step 6: Run the final sample-size calculation | Repeat with a negative-binomial distribution | Use the same settings for a two-by-two crossover design

Step 1: Define the study and theoretical targets | Step 2: Run a pilot with sampleSize() | Step 3: Validate the generated data and estimands | 3a. Arm-specific parameters | 3b. Endpoint correlations | 3c. Estimand direction and sampling distribution | 3d. TOST t-statistics | Step 4: Check Monte Carlo stability and precision | Step 5: Check Type I error at the equivalence boundaries | One endpoint example | Multiple endpoints example | k = 3: all endpoints are required | k = 2: at least two endpoints are required | k = 1: at least one endpoint is required | Step 6: Run the final sample-size calculation | Step 7: Interpret and report the final result | References

Multiple independent co-primary endpoints | Approach 1: Using sampleSize_Mielke | Approach 2: Using sampleSize | Estimating power at a fixed sample size | Methods for inspecting the sample-size result | Multiple correlated co-primary endpoints | References

Hypotheses | Difference of Means (DOM) | Ratio of Means (ROM) | Count outcomes: event-rate ratio (RR) | Regulatory requirements | Testing of multiple endpoints | Testing multiple co-primary endpoints | Testing multiple primary endpoints | Bonferroni correction | Sidak correction | K adjustment: Mielke's weak k-out-of-m correction | T adjustment: Mielke's strong k-out-of-m correction | Hierarchical testing of multiple endpoints | Testing of multiple treatments | References

Purpose and scope | Assumptions shared by the simulation framework | Continuous outcomes | Normal outcomes | Log-normal outcomes | Count outcomes | Poisson outcomes | Negative-binomial outcomes | Dependence between endpoints | Count outcomes and the Gaussian copula | What cor_mat means in practice | Design-specific assumptions | Parallel design | 2x2 crossover design | Normal and log-normal outcomes | Poisson and negative-binomial outcomes | Practical sensitivity analyses

Overview | Study assumptions | Power for a fixed sample size | Sample-size estimation for all comparisons and endpoints | Joint sample-size estimation with k = 3 | Why joint planning is important | Negative-binomial sensitivity analysis

Introduction | Difference of means test | Scenario | Trial design | Objective | Implementation | References

Introduction | Testing multiple co-primary endpoints | Independent testing of co-primary endpoints | Simultaneous testing of independent co-primary endpoints | Simultaneous testing of correlated co-primary endpoints | Testing multiple primary endpoints | Simultaneous testing of primary endpoints | Hierarchical testing of endpoints | References

Introduction | Sample-size calculation for AUCinf: equivalence to EU Remicade | Hypotheses | Preparing the function arguments | Computing sample size | Sample-size calculation for AUCinf: equivalence to US Remicade and EU Remicade | Implementation details | Results and interpretation | References

Introduction | Input data | Simultaneous testing of independent co-primary endpoints | Different hypotheses across endpoints | Simultaneous testing of independent primary endpoints | Equivalence for at least 2 of the 3 endpoints with Mielke's t-adjustment | Unequal allocation rates across arms | Accounting for participant dropout | References