Description
Adaptive Design Theory and Implementation Using SAS and R (2nd Ed.)
Chapman & Hall/CRC Biostatistics Series
Author: Chang Mark
Language: EnglishSubjects for Adaptive Design Theory and Implementation Using SAS and R:
Keywords
Interim Analysis; SAS Macro; interim; Adaptive Design; analysis; Sample Size Reestimation; macro; Stopping Boundaries; Group Sequential Design; hypothesis; SAS Macro Call; sample; Error Spending Function; size; SAS Variable; group; Adaptive Trials; sequential; Conditional Error Function; proc; Futility Boundary; Conditional Power; Posterior Distribution; Phase Ii Trial; Conditional Error Rates; Coprimary Endpoints; Seamless Design; Final Test Statistic; Drop Loser Design; Sample Size Calculation; Expected Sample Sizes; Sample Size Adjustment; Adaptive Design Methods; Noninferiority Margin
Publication date: 10-2016
· 15.6x23.4 cm · Paperback
Publication date: 01-2015
664 p. · 15.6x23.4 cm · Hardback
Description
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Get Up to Speed on Many Types of Adaptive Designs
Since the publication of the first edition, there have been remarkable advances in the methodology and application of adaptive trials. Incorporating many of these new developments, Adaptive Design Theory and Implementation Using SAS and R, Second Edition offers a detailed framework to understand the use of various adaptive design methods in clinical trials.
New to the Second Edition
- Twelve new chapters covering blinded and semi-blinded sample size reestimation design, pick-the-winners design, biomarker-informed adaptive design, Bayesian designs, adaptive multiregional trial design, SAS and R for group sequential design, and much more
- More analytical methods for K-stage adaptive designs, multiple-endpoint adaptive design, survival modeling, and adaptive treatment switching
- New material on sequential parallel designs with rerandomization and the skeleton approach in adaptive dose-escalation trials
- Twenty new SAS macros and R functions
- Enhanced end-of-chapter problems that give readers hands-on practice addressing issues encountered in designing real-life adaptive trials
Covering even more adaptive designs, this book provides biostatisticians, clinical scientists, and regulatory reviewers with up-to-date details on this innovative area in pharmaceutical research and development. Practitioners will be able to improve the efficiency of their trial design, thereby reducing the time and cost of drug development.
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. Bayecian 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. Appendices. Bibliography. Index.