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Index
Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Editors
Contributors
I General Issues
1 Endpoints for Cancer Clinical Trials
1.1 Introduction
1.2 Overall Survival
1.3 Endpoints Based on Tumor Measurements
1.3.1 RECIST Criteria
1.3.2 Response Rate as Primary Endpoints
1.3.3 Tumor Response as Continuous Variable
1.4 Progression-free Survival and Other Composite Endpoints
1.5 Surrogate Endpoints
1.5.1 Definition
1.5.2 Surrogate Endpoint Validation
1.5.3 Remaining Issues
1.6 Patient-reported Outcomes
1.6.1 Patient-reported Outcomes
1.6.2 Types of PRO for Treatment Comparisons
1.6.3 Health Status, Functional and Symptoms Outcomes
1.6.4 General and Cancer-specific Quality of Life Outcomes
1.6.5 Criteria Used for PRO Instruments Selection
1.6.6 Reliability
1.6.7 Validity
1.6.8 Responsiveness of Instruments to Change
1.7 Promising New Approaches
1.7.1 Limitations of Traditional Endpoints
1.7.2 Pharmacokinetic and Pharmacodynamics Responses
1.7.3 Imaging Techniques
1.7.4 Immune Biomarkers-based Endpoints
1.7.5 Criteria for Evaluating Biomarker-based Endpoints
1.8 Summary
2 Use of Historical Data
2.1 Introduction
2.2 Overview of Approaches for Incorporating Historical Data
2.2.1 Introduction
2.2.2 Meta-analytic Approaches
2.2.3 Robust Meta-analytic-predictive Priors and Prior-data Conflict
2.2.4 Prior Effective Sample Size
2.3 Applications
2.3.1 Application 1: A Randomized Phase II Trial Using Historical Control Data
2.3.2 Application 2: Design of a Japanese Dose Escalation Study Incorporating Data from Western Patients
2.3.3 Application 3: Non-inferiority and Biosimilar Trials
2.4 Discussion
2.5 Appendix
3 Multiplicity
3.1 Introduction to Multiplicity Issues
3.1.1 Sources of Multiplicity
3.1.2 Types of Error Rates
3.1.3 Why Multiplicity Adjustment
3.1.4 A Motivating Example
3.2 Common Multiple Comparison Procedures
3.2.1 General Concepts
3.2.2 Methods Based on Univariate p-Values
3.2.2.1 Methods Based on the Bonferroni Test
3.2.2.2 Methods Based on the Simes Test
3.2.2.3 Numerical Illustration
3.2.3 Parametric Methods
3.2.3.1 Dunnett Test
3.2.3.2 Multiple Testing in Linear Models
3.3 Advanced Multiple Comparison Procedures
3.3.1 Graphical Approaches
3.3.2 Gatekeeping Procedures
3.3.3 Group Sequential Procedures
3.3.3.1 Group Sequential Procedures with Multiple Hypotheses
3.3.3.2 Group Sequential Procedures with a Time-to-event Endpoint
3.3.4 Adaptive Designs
3.4 Applications
3.4.1 Multiple Comparison Procedure in the BELLE-2 Trial
3.4.2 Comparison with a Common Control in Time-to-event Trials
3.5 Concluding Remarks
4 Analysis of Safety Data
4.1 Introduction
4.2 Phase I Clinical Trials
4.2.1 Phase I Designs
4.3 Planning Safety Analyses
4.3.1 Events of Interest
4.3.2 The Statistical Analysis Plan (SAP)
4.3.3 The Program Safety Analysis Plan (PSAP)
4.3.4 Data Monitoring Committee (DMC)
4.4 Safety Signal Detection
4.4.1 Classifying Adverse Events
4.4.2 Statistical Methods for Late Phase Trials
4.4.3 Post-marketing Signal Detection
4.4.4 Single-arm Trials and Combination Studies
4.4.5 Safety Non-inferiority Trials
4.5 Collecting, Summarizing, and Displaying Safety Data
4.5.1 Data Collection
4.5.2 Reporting Safety Information
4.5.3 Graphical Approaches
4.6 Meta-analysis of Safety Data
4.7 Benefit-risk Analysis
4.8 Summary
II Early Phase Clinical Trials
5 Development and Validation of Predictive Signatures
5.1 Introduction
5.1.1 Prognostic and Predictive Omics Signatures
5.2 Signature Development
5.2.1 Assay Development and Validation
5.2.2 Statistical Development
5.2.3 Iteration and Refinement
5.2.4 Performance Metrics
5.2.5 Estimation of Performance Metrics
5.2.6 Computational Reproducibility
5.2.7 Practical Considerations
5.3 Clinical Utility Assessment
5.3.1 How Omics Signatures Are Used in Clinical Trials
5.3.2 Evaluating Clinical Utility
5.3.3 Power and Sample Size Considerations
5.4 Summary
6 Phase I Trials and Dose-finding
6.1 Background
6.2 Methods for a Single Cytotoxic Agent
6.2.1 Rule-based Designs
6.2.1.1 The Standard or 3+3 Design
6.2.1.2 Storer’s 2-s tage Designs
6.2.1.3 Biased Coin Designs
6.2.2 Methods Based on Toxicity Probability Intervals
6.3 Model-based Methods
6.3.1 The Continual Reassessment Method
6.3.2 Escalation with Overdose Control (EWOC)
6.3.3 EWOC and CRM
6.3.4 Bayesian 2-Parameter Logistic Models
6.3.5 Which Method to Use?
6.4 Time-to-event Toxicity Outcomes
6.5 Ordinal Outcomes
6.5.1 Rule-based Methods
6.5.2 Model-based Methods
6.5.3 Toxicity Scores
6.6 Dose Expansion Cohorts
6.7 Dose-finding Based on Safety and Efficacy
6.8 Combinations of Agents
6.8.1 Assumption of a Single Ordering
6.8.2 Specifying Multiple Possible Orderings
6.8.3 Use of More Flexible Models
6.8.4 Finding Multiple MTDCs
6.9 Patient Heterogeneity
6.10 Non-cytotoxic Agents
6.10.1 Locating the OBD
6.11 Summary
7 Design and Analysis of Phase II Cancer Clinical Trials
7.1 Introduction
7.2 Single-arm Phase II Trials
7.2.1 Optimal Two-stage Designs
7.2.2 Estimation of Response Rate
7.2.3 Confidence Interval
7.2.4 p-Value Calculation
7.3 Randomized Phase II Trials
7.3.1 Single-stage Design
7.3.2 Two-stage Design
7.3.2.1 Choice of a1 and a
7.3.2.2 Choice of n1 and n2
7.3.3 Numerical Studies
7.4 Discussion
III Late Phase Clinical Trials
8 Sample Size for Survival Trials in Cancer
8.1 Introduction
8.2 Departures from Proportionality
8.2.1 Treatment Lag
8.2.2 Treatment Anti-lag
8.2.3 Both Lag and Anti-lag
8.2.4 Sample Size Implications
8.2.4.1 Implications for Treatment Lag — Real World Example
8.2.4.2 Exploring the Implications of Treatment Lag and Anti-lag in a Controlled Setting
8.2.4.3 Sample Size and Power Calculations
8.2.4.4 Treatment Anti-lag
8.3 Two Paradigms for Which Conventional Wisdom Fails
8.3.1 Event-driven Trial
8.3.2 Group-sequential Sample Size Inflation Factor
8.4 Sample Size Re-estimation and Futility
8.4.1 Estimating the Treatment Effect in a Trial with a Threshold Treatment Lag
8.4.2 Increasing the Sample Size When There Is a Treatment Lag
8.4.3 Interaction between Weighted Statistics and Non-proportional Hazards
8.4.4 Estimating the Treatment Effect in a Trial with a Threshold Treatment Anti-lag
8.4.5 Sample Size Re-estimation in the Presence of Treatment Lag or Anti-lag: Concluding Remark
8.4.6 Conditional Power, Current Trends, and Non-proportional Hazards
8.5 How the Markov Model Works
8.5.1 Introduction
8.5.2 The Exponential Model for Calculating Cumulative Survival Probabilities
8.5.3 The Life-table Approach to Calculating Cumulative Survival Probabilities
8.5.4 The Markov Model Approach to Calculating Cumulative Survival Probabilities
8.5.4.1 2-State Markov Model: At Risk, Failure
8.5.4.2 3-State Markov Model: At Risk, Failure, Loss
8.5.4.3 4-State Markov Model: At Risk, Failure, Loss, ODIS (Non-compliance)
8.5.5 Using the Markov Model to Calculate Sample Sizes for the Log-rank Statistic
8.5.6 Speed and Accuracy
8.6 Discussion and Conclusions
9 Non-inferiority Trials
9.1 Introduction
9.2 Endpoint Selection
9.3 Methods for Evaluating the Active Control Effect and Selecting the Non-inferiority Margin
9.3.1 Fixed Margin
9.3.2 Synthesis Approach
9.3.3 Bayesian Approach
9.3.4 Placebo-controlled Approach
9.4 Sample Size Determination
9.4.1 Ratio of Proportions
9.4.2 Survival Endpoints
9.5 Interim Monitoring and Analyses
9.6 Multiple Comparisons
9.6.1 Testing of Non-inferiority to Superiority and Superiority to Non-inferiority
9.7 Missing Data and Non-compliance
9.8 Statistical Inference and Reporting
9.9 Summary
10 Quality of Life
10.1 Introduction
10.2 Measures of HRQoL
10.3 QOL as an Endpoint in Cancer Trials
10.4 Multiple Endpoints
10.4.1 Summary Measures and Statistics
10.4.2 Multiple Comparisons Adjustments and Gate-keeping Strategies
10.5 Informative Missing Data Due to Dropout
10.5.1 Methods to Be Avoided
10.5.2 Recommended Approach
10.5.3 Sensitivity Analyses
10.5.4 QOL after Death
10.5.5 QALYs and Q-TWiST
10.5.6 How Much Data Can Be Missing?
10.6 Sample Size or Power Estimation
10.7 Summary
IV Personalized Medicine
11 Biomarker-based Clinical Trials
11.1 Introduction
11.2 Analytic Performance of a Biomarker
11.3 Prognostic and Predictive Biomarkers
11.4 Biomarkers in Phase I Trials
11.5 Biomarkers in Phase II Trials
11.5.1 Trials without a Control Arm
11.5.2 Randomized Screening Trials with a Control Arm
11.6 Biomarkers in Phase III Trials
11.6.1 Biomarkers with Compelling Credentials
11.6.2 Biomarkers with Strong Credentials
11.6.2.1 Subgroup-specific Testing Strategies
11.6.2.2 Biomarker-positive and Overall Strategies
11.6.2.3 Marker Sequential Test Design
11.6.2.4 Sample Size Considerations
11.6.3 Biomarkers with Weak Credentials
11.6.4 Interim Monitoring
11.6.5 Retrospective Biomarker Analysis of Phase III Trial Data
11.6.6 Biomarker-strategy Designs
11.7 Summary
12 Adaptive Clinical Trial Designs in Oncology
12.1 Introduction
12.2 History of Adaptive Designs
12.3 Bayesian Framework and Its Use in Clinical Trials
12.4 Adaptive Dose-finding Designs for Identifying Optimal Biologic Dose
12.5 Multi-stage Designs, Group Sequential Designs, Interim Analysis, Early Stopping for Toxicity, Efficacy, or Futility
12.6 Sample Size Re-estimation
12.7 Adaptive Randomization, Individual Ethics versus Group Ethics
12.8 Seamless Designs
12.9 Biomarker-guided Adaptive Designs
12.10 Multi-arm Adaptive Designs
12.11 Master Protocols, Umbrella Trials, Basket Trials, and Platform-based Designs
12.12 Examples of Trials with Adaptive Designs — Lessons for Design and Conduct
12.13 Software for Adaptive Designs
12.14 Discussion
13 Dynamic Treatment Regimes
13.1 Introduction
13.2 Characterization of Treatment Regimes
13.2.1 Decision Rules and Regimes
13.2.2 Classes of Treatment Regimes
13.3 Potential Outcomes Framework
13.3.1 Single Decision
13.3.2 Multiple Decisions
13.4 Sequential, Multiple Assignment, Randomized Trials
13.4.1 Data for Studying Dynamic Treatment Regimes
13.4.2 Considerations for SMARTs
13.4.3 Inference on Embedded Regimes in a SMART
13.5 Thinking in Terms of Dynamic Treatment Regimes
13.6 Optimal Treatment Regimes for Personalized Medicine
13.6.1 Characterizing an Optimal Regime
13.6.2 Regression-based Estimation of an Optimal Regime
13.6.3 Alternative Methods
13.7 Discussion
Index
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