Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods

Name: Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods | Classical, Adaptive, and Bayesian Methods
Brand: SAS Institute
Price: 54.99 EUR
Availability: OnlineOnly

Classical, Adaptive, and Bayesian Methods

Sandeep Menon Richard C. Zink(Editor)

SAS Institute (Publisher)

Published on 9. December 2015

364 pages

E-Book

PDF with Adobe-DRM

System requirements

978-1-62960-084-0 (ISBN)

€54.99incl. 7% vat

System requirements

for PDF with Adobe-DRM

E-Book Single Licence

Available for download

Description

More details

Other editions

Content

Intro
Contents
Foreword
About This Book
Purpose
Is This Book for You?
Prerequisites
About the Examples
Software Used to Develop the Book's Content
Example Code and Data
Output and Graphics Used in This Book
Additional Resources
Keep in Touch
About the Authors
Acknowledgments
Overview of Clinical Trials in Support of Drug Development
1.1 Introduction
1.2 Evolution of Clinical Trials and the Emergence of Guidance Documents
1.3 Emergence of Group Sequential Designs in the 70s and 80s
1.4 Emergence of Adaptive Designs in the 90s
1.5 Widespread Research on Adaptive Designs Since the Turn of the 21st Century
1.5.1 Early Phase Oncology Designs
1.5.2 Multiplicity in Adaptive Designs
1.5.3 Formation of the Adaptive Design Working Group
1.5.4 Opportunities in the Learning Phase
1.5.5 Software
1.6 Opportunities and Challenges in Designing, Conducting, and Analyzing Adaptive Trials
1.6.1 Logistics in Trial Execution
1.6.2 Open Research Questions
1.7 The Future of Adaptive Trials in Clinical Drug Development
References
Authors
Designing and Monitoring Group Sequential Clinical Trials
2.1 Introduction
2.2 Examples of Classical Fixed-Sample Designs
2.3 Theories of Group Sequential Tests
2.3.1 General Framework
2.3.2 Normal Endpoint
2.3.3 Binomial Endpoint
2.3.4 Survival Endpoint
2.4 Types of Stopping Boundaries
2.4.1 Calculation of Stopping Boundaries
2.4.2 P-value or Haybittle-Peto Boundaries
2.4.3 Unified Family Boundaries
2.4.4 Spending Function Boundaries
2.5 Special Issues
2.6 Summary
References
Authors
Sample Size Re-estimation
3.1 Introduction
3.1.1 Motivation. Why Sample Size Re-estimation?
3.1.2 Overview of Considerations and Issues
3.2 Blinded SSR Methods
3.3 Unblinded SSR Methods
3.3.1 Overview
3.3.2 Classification of Methods Used to Control Type I Error Rate
3.3.3 Methods Based on Combination Tests
3.3.4 Sample Size Re-estimation Based on Promising Zone
3.3.5 Methods Based on the Conditional Type I Error Principle
3.4 Information-Based Design
3.5 Summary and Conclusions
References
Authors
Bayesian Survival Meta-Experimental Design Using Historical Data
4.1 Introduction
4.2 Meta Design Setting
4.2.1 Notation
4.2.2 Input Data for a Meta-Trial Design Variable
4.2.3 Input Historical Data
4.3 Meta-Regression Survival Models
4.3.1 Model for Future Survival Data
4.3.2 Model for Historical Survival Data
4.4 Bayesian Meta-Experimental Design
4.5 Specification of Prior Distributions
4.5.1 Fitting Priors
4.5.2 Sampling Priors
4.6 Computational Algorithms
4.6.1 Predictive Data Generation
4.6.2 Sampling from the Posterior Distributions
4.6.3 Power Calculation
4.7 SAS MACRO BSMED
4.7.1 Implementation of BSMED
4.7.2 Illustrative Examples
4.8 Summary
References
Authors
Appendix
Macro Call
Inputs
Output
Running Time
Continual Reassessment Methods
5.1 Dose Finding in Oncology
5.2 Continual Reassessment Method
5.3 Bayesian Model Averaging Continual Reassessment Method
5.4 Fractional Continual Reassessment Method
5.5 Time-to-Event Continual Reassessment Method
5.6 Summary
References
Authors
Classical Dose-Response Study
6.1 Introduction
6.2 Statistical Design Considerations in a Classical Dose-Response Study
6.2.1 Dose-Response Relationship
6.2.2 Finding the Minimum Effective Dose
6.2.3 Determining the Dose Range to Study
6.2.4 Was the Concept Proven?
6.2.5 How to Space the Test Dosages
6.2.6 Number of Doses and Control Groups
6.2.7 Sample Size Considerations
6.2.8 Example of the Design Stage
6.3 Considerations in the Design and Final Analysis Stages
6.4 Analysis Examples Using Simulated Data
6.4.1 Brief Introduction to the Emax Model
6.4.2 Continuous Endpoint
6.4.3 Binary Endpoint
6.5 Summary
References
Authors
Implementing the MCP-Mod Procedure for Dose-Response Testing and Estimation
7.1 Introduction
7.2 Methodology
7.2.1 Original MCP-Mod Approach
7.2.2 MCP-Mod for General Parametric Models
7.3 Considerations for MCP-Mod at the Design Stage
7.4 Further Considerations on MCP-Mod
7.5 Analysis Examples Using SAS
7.5.1 The optcont Macro
7.5.2 Example 1: biom Data with Normally Distributed Outcome and Model Selection
7.5.3 Example 2: neurodeg Data for Generalized MCP-Mod Approach with Model Averaging
7.6 Conclusions
References
Authors
Appendix
Appendix A
Appendix B
Appendix C
Appendix D
Bayesian Dose Response
8.1 Introduction and Background
8.2 Statistical Model for Bayesian Dose-Response
8.3 Analysis Examples for a Continuous Endpoint
8.3.1 Asthma Trial with 75 Patients Per Group
8.3.2 Asthma Trial with 100 Patients Per Group
8.4 Analysis Example for a Binary Endpoint
8.5 Summary
References
Authors
Overview of Adaptive Randomization
9.1 Introduction
9.2 Simple Randomization
9.3 Restricted Randomization
9.3.1 Permuted Block Randomization
9.3.2 Biased Coin Design
9.4 Covariate-Adaptive Randomization
9.4.1 Stratified Randomization
9.4.2 Covariate-Adaptive Randomization by Minimization
9.4.3 Model-Based Optimal Covariate-Adaptive Randomization
9.5 Response-Adaptive Randomization
9.5.1 Play-the-Winner Rule
9.5.2 Randomized Play-the-Winner Rule
9.5.3 Optimal Adaptive Randomization
9.5.4 Bayesian Response-Adaptive Randomization
9.6 Summary
References
Authors
Optimal Response-Adaptive Randomization Designs in Binary Outcome Clinical Trials
10.1 Introduction
10.1.1 Randomized Clinical Trials
10.1.2 Response-Adaptive Randomization
10.1.3 A Classification of Response-Adaptive Randomization Designs
10.1.4 Organization of This Chapter
10.2 Optimal Allocation
10.3 Response-Adaptive Randomization for Implementing Optimal Allocation
10.3.1 Preliminaries
10.3.2 Sequential Maximum Likelihood Estimation Design
10.3.3 Doubly Adaptive Biased Coin Design
10.3.4 Efficient Randomized-Adaptive Design
10.3.5 Theoretical Comparison of Various RAR Designs
10.4 Simulation of Optimal Response-Adaptive Randomization Procedures
10.4.1 Simulating a Single RAR Sequence
10.4.2 Operating Characteristics of RAR designs via Monte Carlo Simulation
10.5 Power and Sample Size for Response-Adaptive Randomization
10.6 Additional Considerations
10.6.1 More Than Two Treatments and Non-Binary Outcomes
10.6.2 Delayed Response
10.6.3 Misclassified Response
10.6.4 Time Trends
10.7 Examples
10.7.1 Redesigning a Real Clinical Trial
10.7.2 Implementing DBCD
10.8 Summary
References
Authors
Population Enrichment Designs
11.1 Introduction
11.1.1 Background
11.1.2 Issues to Consider
11.2 Classical Designs
11.2.1 Retrospective Design
11.2.2 Prospective Design
11.3 Efficiency of Classical Enrichment Designs
11.4 Adaptive Enrichment Designs
11.4.1 Introduction
11.4.2 Frequentist Methods
11.4.3 Bayesian Methods
11.5 Summary
References
Authors
Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods

Description

More details

Other editions

Additional editions

Content

System requirements