Adaptive Design Theory and Implementation Using SAS and R

Introduction Classic Design Theory of Hypothesis-Based Adaptive Design Method with Direct Combination of P-values Method with Inverse-Normal P-values Adaptive Non-Inferiority Design With Paired Binary Data Trial Design and Analysis with Incomplete Paired Data Implementation of N-Stage Adaptive Designs Conditional Error Function Method and Conditional Power Recursive Adaptive Design Unblinded Sample-Size Re-Estimation Design Blinded Sample Size Re-Estimation Adaptive Design with Co-Primary Endpoint Multiple-Endpoint Adaptive Design Pick-the-Winners Design The Add-Arms Design For Unimodal Response Biomarker-Adaptive Design Biomarker-Informed Adaptive Design Survival Modeling and Adaptive Treatment Switching Response-Adaptive Allocation Design Bayesian Adaptive Dose Finding Design Bayesian Phase I-II Adaptive Design Adaptive Design for Biosimilarity Trial Multi-Regional Adaptive Trial Design Bayesian Adaptive Design Planning, Execution, Analysis, and Reporting Data Analysis of Adaptive Design Debates in Adaptive Designs SAS Adaptive Design Modules: SEQDESIGN Procedure Appendix A: Random Number Generation Appendix B: A Useful Utility Appendix C: SAS Macros for Add-Arm Designs Appendix D: Implementing Adaptive Designs in R Bibliography Index

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