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ISBN 10: 1439839883
ISBN 13: 9781439839881
Author: Mark Chang, Shein Chung Chow
With new statistical and scientific issues arising in adaptive clinical trial design, including the U.S. FDA's recent draft guidance, a new edition of one of the first books on the topic is needed. Adaptive Design Methods in Clinical Trials, Second Edition reflects recent developments and regulatory positions on the use of adaptive designs in clini
Chapter 1: Introduction
1.1 What Is Adaptive Design
1.2 Regulatory Perspectives
1.3 Target Patient Population
1.4 Statistical Inference
1.5 Practical Issues
Moving Target Patient Population
Adaptive Randomization
Adaptive Hypotheses
Adaptive Dose Finding (Escalation) Trials
Group Sequential Design
Seamless Phase II/III Adaptive Design
Adaptive Sample Size Adjustment
Adaptive Treatment Switching
Bayesian and Hybrid Approaches
Biomarker Adaptive Trials
Target Clinical Trials
Sample Size and Power Estimation
Clinical Trial Simulation
Case Studies
1.6 Aims and Scope of the Book
Chapter 2: Protocol Amendment
2.1 Introduction
2.2 Moving Target Patient Population
2.3 Analysis with Covariate Adjustment
Continuous Study Endpoint
Binary Response
2.4 Assessment of Sensitivity Index
Case where ε is random and C is fixed
Case where ε is fixed and C is random
2.5 Sample Size Adjustment
2.6 Concluding Remarks
Chapter 3: Adaptive Randomization
3.1 Conventional Randomization
Simple Randomization
Stratified Randomization
Cluster Randomization
3.2 Treatment-Adaptive Randomization
Block Randomization
Efron’s Biased-Coin Model
Lachin Urn Model
Friedman-Wei’s Urn Model
Remarks
3.3 Covariate-Adaptive Randomization
Zelen’s Model
Pocock-Simon’s Model
Wei’s Marginal Urn Design
Imbalance Minimization Model
Atkinson Optimal Model
3.4 Response-Adaptive Randomization
Play-the-Winner Model
Randomized Play-the-Winner Model
Optimal RPW Model
Bandit Model
Bandit Model for Finite Population
Adaptive Models for Ordinal and Continuous Outcomes
3.5 Issues with Adaptive Randomization
Accrual Bias
Accidental Bias
Selection Bias
Inferential Analysis
Power and Sample Size
3.6 Summary
Chapter 4: Adaptive Hypotheses
4.1 Modifications of Hypotheses
4.2 Switch from Superiority to Noninferiority
Noninferiority Margin
Statistical Inference
4.3 Concluding Remarks
Chapter 5: Adaptive Dose-Escalation Trials
5.1 Introduction
5.2 CRM in Phase I Oncology Study
Dose Toxicity Modeling
Dose Level Selection
Reassessment of Model Parameters
Assignment of Next Patient
5.3 Hybrid Frequentist-Bayesian Adaptive Design
Adaptive Model
Utility-based Unified CRM Adaptive Approach
Construction of Utility Function
Prior Distribution of Parameter Tensor a
Likelihood Function
Assessment of Parameter a
Determination of Next Action
5.3.1 Simulations
Design Settings
Response Model
Prior Distribution
Reassessment Method
Utility-Adaptive Randomization
Rules of Dropping Losers and Stopping Rule
Simulation Results
5.4 Design Selection and Sample Size
Criteria for Design Selection
Sample Size Justification
5.5 Concluding Remarks
Chapter 6: Adaptive Group Sequential Design
6.1 Sequential Methods
Basic Concepts
6.2 General Approach for Group Sequential Design
6.3 Early Stopping Boundaries
Early Efficacy Stopping
Early Futility Stopping
Early Efficacy-Futility Stopping
6.4 Alpha Spending Function
6.5 Group Sequential Design Based on Independent P-values
6.6 Calculation of Stopping Boundaries
K-Stage Design
Trial Examples
6.7 Group Sequential Trial Monitoring
Data Monitoring Committee
Principles for Monitoring a Sequential Trial
6.8 Conditional Power
Comparing Means
Comparing Proportions
6.9 Practical Issues
Chapter 7: Statistical Tests for Seamless Adaptive Designs
7.1 Why a Seamless Design is Efficient
7.2 Step-wise Test and Adaptive Procedures
Stopping Rules
7.3 Contrast Test and Naive P-value
7.4 Comparisons of Seamless Design
7.5 Drop-the-Loser Adaptive Design
7.6 Summary
Chapter 8: Adaptive Sample Size Adjustment
8.1 Sample Size Re-Estimation without Unblinding Data
8.2 Cui-Hung-Wang’s Method
Example 8.2.1
Remarks
8.3 Proschan-Hunsberger’s Method
8.4 Müller-Schafer Method
8.5 Bauer-Köhne Method
Two-stage Design
8.6 Generalization of Independent P-value Approaches
General Approach
Test Based on Individual P-values
Test Based on Sum of P-values
Test Based on Product of P-values
Operating Characteristics
Remarks
8.7 Inverse-Normal Method
Example 8.7.1
8.8 Concluding Remarks
Chapter 9: Two-Stage Adaptive Design
9.1 Introduction
9.2 Practical Issues
Flexibility and Efficiency
Validity and Integrity
Regulatory Perspectives/Concerns
9.3 Types of Two-Stage Adaptive Designs
9.4 Analysis for Seamless Design with Same Study Objectives/Endpoints
Early Efficacy Stopping
Early Efficacy or Futility Stopping
Conditional Power
9.5 Analysis for Seamless Design with Different Endpoints
Continuous Endpoint
Binary Response
Time-to-Event Data
Remarks
9.6 Analysis for Seamless Design with Different Objectives/Endpoints
Non-adaptive Version
Adaptive Version
Example: Hepatitis C Virus Trial
9.7 Concluding Remarks
Chapter 10: Adaptive Treatment Switching
10.1 Latent Event Times
10.2 Proportional Hazard Model with Latent Hazard Rate
Simulation Results
10.3 Mixed Exponential Model
Biomarker-Based Survival Model
Effect of Patient Enrollment Rate
Hypothesis Test and Power Analysis
Application to Trials with Treatment Switch
10.4 Concluding Remarks
Chapter 11: Bayesian Approach
11.1 Basic Concepts of Bayesian Approach
Bayesian Power
11.2 Multiple-Stage Design for Single-Arm Trial
Classical Approach for Two-Stage Design
Bayesian Approach
11.3 Bayesian Optimal Adaptive Designs
11.4 Concluding Remarks
Chapter 12: Biomarker Adaptive Trials
12.1 Introduction
12.2 Types of Biomarkers and Validation
Types of Biomarkers
Biomarker Validation
Translation among Biomarker, Treatment, and True-Endpoint
Multiplicity and False Positive Rate
Remarks
12.3 Design with Classifier Biomarker
Enrichment Process
Classic Design with Classifier Biomarker
Adaptive Design with Classifier Biomarker
Example: Biomarker-Adaptive Design
12.4 Adaptive Design with Prognostic Biomarker
Optimal Design
Prognostic Biomarker in Designing Survival Trial
12.5 Adaptive Design with Predictive Marker
12.6 Concluding Remarks
12.7 Appendix
SAS Macro for Two-Stage Design and Classic Single-Stage Design
SAS Macro for Biomarker-Adaptive Trials with Two Parallel Groups
Chapter 13: Target Clinical Trials
13.1 Introduction
13.2 Potential Impact and Significance
13.3 Evaluation of Treatment Effect
Study Design
Statistical Methods
Simulation Results
13.4 Other Study Designs and Models
FDA Recommended Study Designs
Statistical Methods
13.5 Concluding Remarks
Chapter 14: Sample Size and Power Estimation
14.1 Framework and Model/Parameter Assumptions
Simulation Framework
Stopping Rules
14.2 Method Based on Sum of P-values
Algorithm: K-Stage Group Sequential with MSP
14.3 Method Based on Product of P-values
Algorithm: K-Stage Group Sequential with MPP
14.4 Method with Inverse-Normal P-values
Algorithm: K-Stage Group Sequential with MINP
Operating Characteristics
14.5 Sample Size Re-estimation
Algorithm: Two-Stage Sample-Size Re-estimation with MSP
Algorithm: Two-Stage Sample-Size Re-estimation with MPP
Algorithm: K-Stage Sample-Size Re-estimation with MINP
14.6 Summary
Chapter 15: Clinical Trial Simulation
15.1 Introduction
15.2 Software Application of ExpDesign Studio
Overview of ExpDesign Studio
How to Design a Trial with ExpDesign Studio
How to Design a Classical Trial
How to Design a Group Sequential Trial
How to Design an Adaptive Trial with SSR
How to Design an Adaptive Dose-Escalation Trial
15.3 Early Phases Development
15.4 Late Phases Development
15.5 Concluding Remarks
Chapter 16: Regulatory Perspectives – A Review of FDA Draft Guidance
16.1 Introduction
16.2 The FDA Draft Guidance
16.3 Well-Understood Designs
Adaptive Entry Criteria Based on Baseline Data
Sample Size Adjustment without Unblinding
Adaptations Based on Outcomes Unrelated to Efficacy
Group Sequential Futility Design
Adaptations Independent of Treatment Differences
16.4 Less Well-Understood Designs
Less Well-Understood Adaptive Designs
Statistical Considerations
16.5 Adaptive Design Implementation
Content of an Adaptive Design Protocol
Adequate Documentation in Adaptive Trials
Interactions with FDA
Special Protocol Assessments
Execution and Documentation
16.6 Concluding Remarks
Chapter 17: Case Studies
17.1 Basic Considerations
Dose and Dose Regimen
Study Endpoints
Treatment Duration
Logistical Considerations
Independent Data Monitoring Committee
17.2 Adaptive Group Sequential Design
Group Sequential Design
Adaptation
Statistical Methods
Case Study: An Example
17.3 Adaptive Dose-Escalation Design
Traditional Dose-Escalation Design
Adaptation
Statistical Methods
Case Study: An Example
17.4 Two-Stage Phase II/III Adaptive Design
Seamless Phase II/III Design
Adaptation
Methods
Case Study: Some Examples
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Tags: Mark Chang, Shein Chung Chow, Adaptive, Methods
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