Single-Arm Phase II Survival Trial Design

✍ Scribed by Jianrong Wu

• Publisher: Routledge
• Year: 2021
• Tongue: English
• Leaves: 274
• Series: Chapman & Hall/CRC Biostatistics Series
• Edition: 1
• Category: Library

✦ Synopsis

Single-Arm Phase II Survival Trial Design provides a comprehensive summary to the most commonly- used methods for single-arm phase II trial design with time-to-event endpoints. Single-arm phase II trials are a key component for successfully developing advanced cancer drugs and treatments, particular for target therapy and immunotherapy in which time-to-event endpoints are often the primary endpoints. Most test statistics for single-arm phase II trial design with time-to-event endpoints are not available in commercial software.

Key Features:

• Covers the most frequently used methods for single-arm phase II trial design with time-to-event endpoints in a comprehensive fashion.

• Provides new material on phase II immunotherapy trial design and phase II trial design with TTP ratio endpoint.

• Illustrates trial designs by real clinical trial examples

• Includes R code for all methods proposed in the book, enabling straightforward sample size calculation.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
1. Introduction of Single-Arm Phase II Trial Design
1.1. Why a Single-Arm Phase II Trial?
1.2. Primary Endpoint of a Single-Arm Phase II Trial
1.3. Hypothesis of a Single-Arm Phase II Trial
1.4. Test Statistic and Study Design
1.5. Choice of Alpha and Beta
1.6. Time-to-Event Endpoints
1.7. Literature Review
1.8. Scope and Motivation
2. Phase II Trial Design under Parametric Model
2.1. Introduction
2.2. Weibull Model
2.3. Log Transformed MLE Test
2.4. Quadratic Transformed MLE Test
2.5. Sample Size Calculation
2.6. Accrual Duration Calculation
2.7. Comparison
2.8. Study Design and Data Analysis
2.9. R Code
3. One-Stage Design Evaluating Survival Probabilities
3.1. Introduction
3.2. Nelson-Aalen Estimate Based Tests
3.2.1. Test Statistics
3.2.2. Sample Size Formula
3.2.3. Sample Size Calculation
3.2.4. Comparison
3.3. Kaplan-Meier Estimate Based Tests
3.3.1. Test Statistics
3.3.2. Comparison
3.3.3. Study Design and Data Analysis
3.4. Test Median Survival Time
3.4.1. Test Statistics
3.4.2. Sample Size Formula
4. Two-Stage Design Evaluating Survival Probabilities
4.1. Introduction
4.2. Two-Stage Design Based on Log-Log Test
4.2.1. Test Statistics
4.2.2. Optimal Two-Stage Design
4.2.3. Optimization Algorithm
4.2.4. Two-Stage Trial Data
4.2.5. Simulation
4.2.6. Study Design and Data Analysis
4.2.7. R Code for Two-Stage Design
4.3. Two-Stage Design Based on Arcsin-Square Root Test
4.3.1. Test Statistics
4.3.2. Optimal Two-Stage Design
4.3.3. Study Design and R Code
4.3.4. Simulation
5. One-Stage Design Evaluating Survival Distributions
5.1. Introduction
5.2. One-Sample Log-Rank Test
5.2.1. Test Statistics
5.2.2. Sample Size Formula
5.2.3. Accrual Duration Calculation
5.2.4. Simulation
5.3. Modified One-Sample Log-Rank Test
5.3.1. Test Statistics
5.3.2. Sample Size Formula
5.3.3. Comparison
5.3.4. Sample Size vs. Length of Follow-up
5.3.5. R Code
5.4. Transformed OSLRT under PH Model
5.4.1. Transformed OSLRT
5.4.2. Number of Events Formulae
5.4.3. Comparison and R Code
5.5. General Hypothesis Testing
5.5.1. Test Statistics
5.5.2. Sample Size Formula
5.5.3. Simulation
6. Two-Stage Design Evaluating Survival Distributions
6.1. Introduction
6.2. Two-Stage Design Using OSLRT with Full Follow-up
6.2.1. Two-Stage Test Statistics
6.2.2. Optimal Two-Stage Procedure
6.2.3. R Code for Two-Stage Design
6.2.4. Simulation
6.3. Two-Stage Design with Restricted Follow-up
6.3.1. Two-Stage Test Statistics
6.3.2. Optimal Two-Stage Procedure
6.3.3. R Code for Two-Stage Design with Restricted Follow-up
6.3.4. Simulation
7. Phase II Immunotherapy Trial Design
7.1. Introduction
7.2. Weighted Modi ed One-Sample Log-Rank Test
7.3. Phase II Trial Design with Delayed Treatment Effect
7.3.1. Random Delayed E ect Model
7.3.2. Test Statistics
7.3.3. R Code
7.4. Phase II Trial Design with Long-Term Survivors
7.4.1. Mixture Cure Model
7.4.2. Change Sign Weighted MOSLRT
7.4.3. Study Design and Data Analysis
7.4.4. R Code
7.4.5. Comparison
7.4.6. Sample Size vs. Length of Follow-up
8. Phase II Trial Design with GMI Endpoint
8.1. Introduction
8.2. Von-Hoff's Design
8.3. Kaplan-Meier Estimate of GMI Distribution
8.4. Kaplan-Meier Estimate Based Design
8.5. Test Quantile of GMI
8.5.1. Study Design under GBVE Model
8.5.2. Study Design under Weibull Frailty Model
8.5.3. Comparison
8.5.4. R Code
8.6. Score Test-Based Design
8.6.1. Score Test Statistics
8.6.2. Sample Size Formula
8.6.3. Study Design under GBVE Model
8.6.4. Study Design under Weibull Frailty Model
8.6.5. Study Design Using Generalized E ect Size
8.7. Counting Censoring
8.7.1. Simple Adjustment
8.7.2. Uniform Accrual on Current Therapy
8.8. Simulation
8.9. Example and R Code
8.10. Discussion
9. Bayesian Single-Arm Phase II Trial Design
9.1. Introduction
9.2. Transformed Time-to-Event Model
9.3. Bayesian One-Stage Design
9.4. Bayesian Two-Stage Design
9.5. Simulation
9.6. Discussion
9.7. R Code
A. Probability of Failure under Uniform Accrual
B. Asymptotic Distribution of Nelson-Aalen Estimate of the Cumulative Hazard
C. Derivation Asymptotic Distribution of the OSLRT
D. Derivation of Equations (6.8) and (6.9)
E. Crossing Point for the Mixture Cure Model
F. Derivation Asymptotic Distribution of the Score Test Q
G. Generate Random Variables from GBVE Model
H. Derivation Censoring Survival Distribution of GMI
I. Proof of Monotonicity of the Posterior Probability
J. Relationship between Frequentist and Bayesian Type I Error Rates
K. R Code
K.1 Two-Stage Design Using Arcsin-Square Root Transformed Test
K.2 Two-Stage Design Using MOSLRT with Restricted Follow-up
K.3 Two-Stage Design Using MOSLRT without Restricted Follow-up
Bibliography
Index