264 pages | 59 B/W Illus.
The third edition of the bestselling Clinical Trials in Oncology provides a concise, nontechnical, and thoroughly up-to-date review of methods and issues related to cancer clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. In addition, the book has been restructured to have separate chapters and expanded discussions on general clinical trials issues, and issues specific to Phases I, II, and III. New sections cover innovations in Phase I designs, randomized Phase II designs, and overcoming the challenges of array data.
Although this book focuses on cancer trials, the same issues and concepts are important in any clinical setting. As always, the authors use clear, lucid prose and a multitude of real-world examples to convey the principles of successful trials without the need for a strong statistics or mathematics background. Armed with Clinical Trials in Oncology, Third Edition, clinicians and statisticians can avoid the many hazards that can jeopardize the success of a trial.
"This book provides a very clear and concise overview of the main issues in the design, data management, and analysis of clinical trials. Although the examples used are from oncology trials, the principles apply to all clinical trials and so will be of use to a wide audience. The book is well written and easy to read … recommended reading to anyone involved in the design and running of clinical trials, not just statisticians, although some familiarity with statistical terminology would help. … a very useful and accessible reference, which covers the essential statistical elements of designing and running clinical trials all in one book, which is extensively illustrated with real examples."
—ISCB News, 57, June 2014
Praise for Previous Editions:
"The dedication of the authors to enhancing the quality of clinical trials in oncology is evident from this book. … This book will be useful to students, clinical research nurses and medical statisticians involved in oncology trials. … I also recommend it to libraries and clinical institutions."
—Clinical Trials, 2004
"With over 60 years combined experience, the authors are ideally positioned to discuss the various statistical issues apparent in clinical trials, identifying alternative solutions, providing logical arguments for and against the various solutions. This book is also recommended for statisticians actively involved in the design, conduct, and analysis of clinical trial data (not only cancer clinical trials)."
—Journal of Biopharmaceutical Statistics
"A concise, easily readable, and thorough summary…ALL medical oncology, radiation oncology, surgical oncology, and clinical research nurse academic training programs should provide this important text to trainees on Day 1."
—Charles R. Thomas Jr., MD, University of Texas Health Science Center at San Antonio, USA
"Succinct and focused…[This book] is clear, cogent, and practical. It is structured so that statisticians can use specific sections as starting point to develop shared understandings with investigators, study coordinators, and data managers…It has been useful to me and my clients, and I look forward to the second edition."
—Marlene Egger, University of Utah, USA
A Brief History of Clinical Trials
The Southwest Oncology Group (SWOG)
The Reason for This Book
The Single-Arm Phase II Trial—Estimation
The Randomized Phase III Trial—Hypothesis Testing
The Proportional Hazards Model
Sample Size Calculations
The Design of Clinical Trials
Randomized Treatment Assignment
Differences to be Detected or Precision of Estimates and Other Assumptions
Use of Independent Data Monitoring Committees
Phase I and Phase I/II Trials
Phase I Trials
Phase I/II Designs
Phase II Trials
Single-Arm Phase II Designs
Multi-Arm Phase II Trials
Other Phase II Designs
Randomized versus Single-Arm: The Pros and Cons
Phase III Trials
Other Design Considerations
Equivalence or Noninferiority Trials
Designs for Targeted Agents
Phase II/III Trials
Data Management and Quality Control
Introduction: Why Worry?
Quality Assurance Audits
Reporting of Results
Timing of Report
Outcome by Outcome Analyses
Some Background and Notation
Identification of Prognostic Factors
Forming Prognostic Groups
Analysis of Microarray Data
Summary and Conclusions