TUM Library

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Cover; Half Title; Title Page; Copyright Page; Table of Contents; Preface; Author Bio; 1: Overview of Drug Development; 1.1 Introduction; 1.2 Drug Discovery; 1.2.1 Target Identification and Validation; 1.2.2 Irrational Approach; 1.2.3 Rational Approach; 1.2.4 Biologics; 1.2.5 NanoMedicine; 1.3 Preclinical Development; 1.3.1 Objectives of Preclinical Development; 1.3.2 Pharmacokinetics; 1.3.3 Pharmacodynamics; 1.3.4 Toxicology; 1.3.5 Intraspecies and Interspecies Scaling; 1.4 Clinical Development; 1.4.1 Overview of Clinical Development; 1.4.2 Classical Clinical Trial Paradigm

1.4.3 Adaptive Trial Design Paradigm1.4.4 New Drug Application; 1.5 Summary; 2: Clinical Development Plan and Clinical Trial Design; 2.1 Clinical Development Program; 2.1.1 Unmet Medical Needs & Competitive Landscape; 2.1.2 Therapeutic Areas; 2.1.3 Value proposition; 2.1.4 Prescription Drug Global Pricing; 2.1.5 Clinical Development Plan; 2.2 Clinical Trials; 2.2.1 Placebo, Blinding and Randomization; 2.2.2 Trial Design Type; 2.2.3 Confounding Factors; 2.2.4 Variability and Bias; 2.2.5 Randomization Procedure; 2.2.6 Clinical Trial Protocol; 2.2.7 Target Population; 2.2.8 Endpoint Selection

2.2.9 Proof of Concept Trial2.2.10 Sample Size and Power; 2.2.11 Bayesian Power for Classical Design; 2.3 Summary; 3: Clinical Development Optimization; 3.1 Benchmarks in Clinical Development; 3.1.1 Net Present Value and Risk-Adjusted NPV Method; 3.1.2 Clinical Program Success Rates; 3.1.3 Failure Rates by Reason; 3.1.4 Costs of Clinical Trials; 3.1.5 Time-to-Next Phase, Clinical Trial Length and Regulatory Review Time; 3.1.6 Rates of Competitor Emerging; 3.2 Optimization of Clinical Development Program; 3.2.1 Local Versus Global Optimizations

3.2.2 Stochastic Decision Process for Drug Development3.2.3 Time Dependent Gain g4,; 3.2.4 Determination of Transition Probabilities; 3.2.5 Example of CDP Optimization; 3.2.6 Updating Model Parameters; 3.2.7 Clinical Development Program with Adaptive Design; 3.3 Summary; 4: Globally Optimal Adaptive Trial Designs; 4.1 Common Adaptive Designs; 4.2 Group Sequential Design; 4.2.1 Test Statistics; 4.2.2 Commonly Used Stopping Boundaries; 4.3 Sample Size Reestimation Design; 4.3.1 Test Statistic; 4.3.2 Rules of Stopping and Sample-Size Adjustment; 4.3.3 Simulation Examples; 4.4 Pick-Winner-Design

4.4.1 Shun-Lan-Soo Method for Three-Arm Design4.4.2 K-Arm Pick-Winner Design; 4.5 Global Optimization of Adaptive Design -- Case Study; 4.5.1 Medical Needs for COPD; 4.5.2 COPD Market; 4.5.3 Indacaterol Trials; 4.5.4 US COPD Phase II Trial Results; 4.5.5 Optimal Design; 4.6 Summary & Discussions; 5: Trial Design for Precision Medicine; 5.1 Introduction; 5.2 Overview of Classical Designs with Biomarkers; 5.2.1 Biomarker-enrichment Design; 5.2.2 Biomarker-Stratified Design; 5.2.3 Sequential Testing Strategy Design; 5.2.4 Marker-based Strategy Design; 5.2.5 Hybrid Design

5.3 Overview of Biomarker-Adaptive Designs

"This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development. Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously." Jay Herson, Johns Hopkins University The pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However, these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost, time, or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization, including clinical trial simulations. Provides a statistical framework for achieve global optimization in each phase of the drug development process. Describes specific techniques to support optimization including adaptive designs, precision medicine, survival-endpoints, dose finding and multiple testing. Gives practical approaches to handling missing data in clinical trials using SAS. Looks at key controversial issues from both a clinical and statistical perspective. Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book. Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R). It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons, this book incorporates both statistical and "clinical/medical" perspectives.

OCLC-licensed vendor bibliographic record.