By Mark Chang
Get up to the mark on many varieties of Adaptive Designs
Since the ebook of the 1st version, there were impressive advances within the technique and alertness of adaptive trials. Incorporating lots of those new advancements, Adaptive layout thought and Implementation utilizing SAS and R, moment Edition bargains an in depth framework to appreciate using a number of adaptive layout tools in scientific trials.
New to the second one Edition
- Twelve new chapters masking blinded and semi-blinded pattern dimension reestimation layout, pick-the-winners layout, biomarker-informed adaptive layout, Bayesian designs, adaptive multiregional trial layout, SAS and R for team sequential layout, and masses more
- More analytical equipment for K-stage adaptive designs, multiple-endpoint adaptive layout, survival modeling, and adaptive remedy switching
- New fabric on sequential parallel designs with rerandomization and the skeleton strategy in adaptive dose-escalation trials
- Twenty new SAS macros and R functions
- Enhanced end-of-chapter difficulties that provide readers hands-on perform addressing matters encountered in designing real-life adaptive trials
Covering much more adaptive designs, this ebook presents biostatisticians, medical scientists, and regulatory reviewers with updated information in this cutting edge region in pharmaceutical study and improvement. Practitioners might be capable of enhance the potency in their trial layout, thereby lowering the time and value of drug development.
Read Online or Download Adaptive Design Theory and Implementation Using SAS and R, Second Edition PDF
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Additional info for Adaptive Design Theory and Implementation Using SAS and R, Second Edition
152 Three-Stage Adaptive Design . . . . . . 166 Four-Stage Adaptive Design . . . . . . 172 Adaptive Design with Survival Endpoint . . . 5 Recursive Two-Stage Adaptive Design . . . Application of Recursive Combination Method . Myocardial Infarction Prevention Trial . . . Adaptive Design with Farrington-Manning NI Margin . . . . . . . . . . . . Blinded Sample-Size Reestimation for Binary Endpoint . . . . . . . . . . . . Power and Sample Size for Two-Arm Trial with Coprimary Endpoints .
5: Sample-Size Reestimation with Shih–Zhao Method . . . . . . . . . . . 1: Power of Two Coprimary Endpoints . . . 2: Power of Two Coprimary Endpoints by Simulation . . . . . . . . . . 3: Overall Conditional Power for One-Group Design with Two Coprimary Endpoints . . 4: Overall Conditional Power for Two-Group Design with Two Coprimary Endpoints . . 1: Pick-the-Winner Design . . . . . . 1: 4+1 Add-Arm Design . . . . . . . 2: 4+1 Add-Arm Design for Finding MED .
Bayesian Power . . . . . . . . . . 1 Trial Design Using Bayesian Power . . . . Simon Two-Stage Optimal Design . . . . . Bayesian Optimal Design . . . . . . . Adaptive Dose-Finding for Prostate Cancer Trial Biosimilar Diabetic Trial Design . . . . . Multiregional ACS Clinical Trial . . . . . Adaptive Multiregional ACS Clinical Trial . . Paradox of Binomial and Negative Binomial Distribution . . . . . . . . . . . 2: Equivalence Trial with Normal Endpoint .