Dose Finding by the Continual Reassessment Method

Dose Finding by the Continual Reassessment Method

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As clinicians begin to realize the important role of dose-finding in the drug development process, there is an increasing openness to "novel" methods proposed in the past two decades. In particular, the Continual Reassessment Method (CRM) and its variations have drawn much attention in the medical community, though it has yet to become a commonplace tool. To overcome the status quo in phase I clinical trials, statisticians must be able to design trials using the CRM in a timely and reproducible manner.

A self-contained theoretical framework of the CRM for researchers and graduate students who set out to learn and do research in the CRM and dose-finding methods in general, Dose Finding by the Continual Reassessment Method features:

Real clinical trial examples that illustrate the methods and techniques throughout the book
Detailed calibration techniques that enable biostatisticians to design a CRM in timely manner
Limitations of the CRM are outlined to aid in correct use of method

This book supplies practical, efficient dose-finding methods based on cutting edge statistical research. More than just a cookbook, it provides full, unified coverage of the CRM in addition to step-by-step guidelines to automation and parameterization of the methods used on a regular basis. A detailed exposition of the calibration of the CRM for applied statisticians working with dose-finding in phase I trials, the book focuses on the R package `dfcrm' for the CRM and its major variants.

The author recognizes clinicians' skepticism of model-based designs, and addresses their concerns that the time, professional, and computational resources necessary for accurate model-based designs can be major bottlenecks to the widespread use of appropriate dose-finding methods in phase I practice. The theoretically- and empirically-based methods in Dose Finding by the Continual Reassessment Method will lessen the statistician's burden and encourage the continuing development and implementation of model-based dose-finding methods.
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Product details

  • Hardback | 200 pages
  • 160.02 x 236.22 x 15.24mm | 430.91g
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New.
  • N/A; N/A CR Book; 40 Tables, black and white; 29 Illustrations, black and white
  • 1420091514
  • 9781420091519
  • 1,927,152

Table of contents

Dose Finding in Clinical Trials
The Maximum Tolerated Dose
An Overview of Methodology
Bibliographic Notes
Exercises and Further Results
The Continual Reassessment Method
One-Stage Bayesian CRM
Two-Stage CRM
Simulating CRM Trials
Practical Modifications
Bibliographic Notes
Exercises and Further Results
One-Parameter Dose-ToxicityModels
ï ¹-Equivalent Models
Model Assumptions
Proof of Theorem 4.1
Exercises and Further Results
Theoretical Properties
Large-Sample Properties
Exercises and Further Results
Empirical Properties
Operating Characteristics
A Nonparametric Optimal Benchmark
Exercises and Further Results

Design Calibration
Specifications of a CRM Design
Specifying the Clinical Parameters
A Roadmap for Choosing the Statistical Component
The Trial-and-Error Approach: Two Case Studies
Initial Guesses of Toxicity Probabilities
Half-width (ï ¤ï  ) of Indifferent Interval
Calibration of ï ¤ï  77
Case Study: The Bortezomib Trial
Exercises and Further Results
Least Informative Normal Prior
Least Informative Prior
Calibration of ï ³ï ¢ï  93
Optimal Least Informative Model
Revisiting the Bortezomib Trial
Initial Design
Ordering of Dose Sequences
Building Reference Initial Designs
Practical Issues
Case Study: NeuSTART
Exercises and Further Results

CRM and Beyond
The Time-to-Event CRM
The Basic Approach
Numerical Illustration
Enrollment Scheduling
Theoretical Properties
Two-Stage Design
Exercises and Further Results
CRM withMultiparameter Models
Curve-Free Methods
Two-Parameter CRM
Exercise and Further Results
When the CRM Fails
Trade-Off Perspective of MTD
Bivariate Dose Finding
Stochastic Approximation
The Past Literature
The Present Relevance
The Future Challenge
Assumptions on M(x) and Y (x)
Exercises and Further Results
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About Ying Kuen Cheung

Department of Biostatistics, Columbia University, USA
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