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Clinical Trials in Oncology, Third Edition 3rd edition [Kietas viršelis]

4.00/5 (3 ratings by Goodreads)
(University of Washington and Fred Hutchinson Cancer Research Center, Seattle, USA), (Cancer Research and Biostatistics, Seattle, Washington, USA), (Senior Director, Pfizer, Inc., Groton, Connecticut, USA), (Chief Executive Officer, Ca)
  • Formatas: Hardback, 264 pages, aukštis x plotis: 234x156 mm, weight: 498 g, 28 Tables, black and white; 59 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Interdisciplinary Statistics
  • Išleidimo metai: 09-May-2012
  • Leidėjas: CRC Press Inc
  • ISBN-10: 1439814481
  • ISBN-13: 9781439814482
Kitos knygos pagal šią temą:
Clinical Trials in Oncology, Third Edition 3rd editionDidesnis paveikslėlis
  • Formatas: Hardback, 264 pages, aukštis x plotis: 234x156 mm, weight: 498 g, 28 Tables, black and white; 59 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Interdisciplinary Statistics
  • Išleidimo metai: 09-May-2012
  • Leidėjas: CRC Press Inc
  • ISBN-10: 1439814481
  • ISBN-13: 9781439814482
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781439814482.html
Raktažodžiai:
Four statisticians explain efforts to improve the methodology of randomized clinical trials designed to discover whether treatments for cancer work or not. Too often, they say, the motivations for such efforts are misunderstood by clinicians. Most of their examples come from the Southwest Oncology Group, an organization of researchers the four are associated with. Among their topics are statistical concepts, the design of clinical trials, phase II trials, data management and quality control, and exploratory analyses. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

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.

Recenzijos

"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 summaryALL 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 managersIt has been useful to me and my clients, and I look forward to the second edition." Marlene Egger, University of Utah, USA "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 the 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 summaryALL 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 managersIt has been useful to me and my clients, and I look forward to the second edition." Marlene Egger, University of Utah, USA

1 Introduction
1(6)
1.1 A Brief History of Clinical Trials
1(4)
1.2 The Southwest Oncology Group (SWOG)
5(1)
1.3 The Reason for This Book
6(1)
2 Statistical Concepts
7(28)
2.1 Introduction
7(7)
2.2 The Single-Arm Phase II Trial---Estimation
14(4)
2.3 The Randomized Phase III Trial---Hypothesis Testing
18(12)
2.3.1 Response as the Outcome
18(5)
2.3.2 Survival as the Outcome
23(7)
2.4 The Proportional Hazards Model
30(2)
2.5 Sample Size Calculations
32(1)
2.6 Concluding Remarks
33(2)
3 The Design of Clinical Trials
35(22)
3.1 Objectives
36(1)
3.2 Eligibility
36(1)
3.3 Treatment Arms
37(2)
3.3.1 Single Arm
37(1)
3.3.2 Two or More Treatment Arms
38(1)
3.4 Randomized Treatment Assignment
39(2)
3.4.1 Blinding
39(2)
3.5 Endpoints
41(5)
3.5.1 Survival
41(1)
3.5.2 Progression-Free Survival (PFS)
42(1)
3.5.3 Response
43(1)
3.5.4 Toxicity Criteria
44(1)
3.5.5 Quality of Life
45(1)
3.6 Differences to be Detected or Precision of Estimates and Other Assumptions
46(1)
3.7 Use of Independent Data Monitoring Committees
47(5)
3.7.1 Composition
49(2)
3.7.2 Concluding Remarks on Monitoring Committees
51(1)
3.8 Ethical Considerations
52(3)
3.9 Conclusion
55(2)
4 Phase I and Phase I/II Trials
57(16)
4.1 Phase I Trials
57(10)
4.1.1 The Traditional 3 + 3 Design
57(2)
4.1.2 Improving Phase I Designs
59(1)
4.1.2.1 Start at a Higher Dose?
60(1)
4.1.2.2 Modify the Traditional Algorithm?
60(3)
4.1.2.3 Use Model-Based Designs?
63(3)
4.1.2.4 Alternative Approaches for Biologic Agents?
66(1)
4.1.3 Phase I Conclusion
66(1)
4.2 Phase I/II Designs
67(6)
4.2.1 Combining Phase I and Phase II
67(1)
4.2.2 Phase I/II
68(2)
4.2.3 Phase I/II Conclusion
70(3)
5 Phase II Trials
73(14)
5.1 Single-Arm Phase II Designs
73(4)
5.1.1 The Standard SWOG Phase II Design
74(2)
5.1.2 Other Single-Arm Phase II Designs
76(1)
5.1.3 Alternative Endpoints
77(1)
5.1.4 Single-Arm Pilot Designs
77(1)
5.2 Multi-Arm Phase II Trials
77(5)
5.2.1 Nonrandomized Phase II Designs with a Control
77(1)
5.2.2 Randomized Phase II Designs with a Control
78(1)
5.2.3 Randomized Selection Designs
79(2)
5.2.4 Other Randomized Designs
81(1)
5.3 Other Phase II Designs
82(1)
5.3.1 Multiple Endpoint Designs
82(1)
5.3.2 Multi-Strata Trials
83(1)
5.4 Randomized versus Single Arm: The Pros and Cons
83(3)
5.5 Conclusion
86(1)
6 Phase III Trials
87(42)
6.1 Randomization
87(5)
6.1.1 Stratification Factors
88(2)
6.1.2 Timing of Randomization
90(2)
6.2 Other Design Considerations
92(3)
6.2.1 One-Sided or Two-Sided Tests
92(1)
6.2.2 Significance Level, Power, and Sample Size
92(2)
6.2.3 Multiple Endpoints
94(1)
6.3 Equivalence or Noninferiority Trials
95(3)
6.3.1 Designing an Equivalence or Noninferiority Trial
97(1)
6.4 Designs for Targeted Agents
98(4)
6.5 Multi-Arm Trials
102(11)
6.5.1 Types of Multi-Arm Trials
102(1)
6.5.2 Significance Level
103(1)
6.5.3 Power
104(2)
6.5.4 Interaction
106(4)
6.5.5 Other Model Assumptions
110(1)
6.5.6 Sequential Randomization
110(2)
6.5.7 Concluding Remarks on Multi-Arm Trials
112(1)
6.6 Interim Analyses
113(12)
6.6.1 Examples of Interim Analyses
117(1)
6.6.1.1 Stopping Early for Positive Results
118(1)
6.6.1.2 Stopping Early for Negative Results
118(3)
6.6.1.3 Stopping an Equivalence Trial Early for Positive Results
121(2)
6.6.1.4 Stopping Based on Toxicity and Lack of Compliance
123(1)
6.6.1.5 Emergency Stopping Based on Unexpected Toxic Deaths
124(1)
6.6.1.6 Concluding Remarks on Interim Analyses
125(1)
6.7 Phase II/III Trials
125(2)
6.8 Concluding Remark
127(2)
7 Data Management and Quality Control
129(26)
7.1 Introduction: Why Worry?
129(4)
7.2 Protocol Development
133(6)
7.2.1 Objectives
133(1)
7.2.2 Background
133(1)
7.2.3 Drug Information
134(1)
7.2.4 Stage Definitions
134(1)
7.2.5 Eligibility Criteria
134(1)
7.2.6 Stratification Factors and Subsets
135(1)
7.2.7 Treatment Plan
135(1)
7.2.8 Treatment Modification
135(1)
7.2.9 Study Calendar
136(1)
7.2.10 Endpoint Definitions
136(1)
7.2.11 Statistical Considerations
137(1)
7.2.12 Discipline Review
137(1)
7.2.13 Registration Instructions
137(1)
7.2.14 Data Submission Instructions
138(1)
7.2.15 Special Instructions
138(1)
7.2.16 Regulatory Requirements
138(1)
7.2.17 Bibliography
138(1)
7.2.18 Forms
138(1)
7.2.19 Appendix
139(1)
7.3 Data Collection
139(5)
7.3.1 Basic Data Items
140(2)
7.3.2 Case Report Form Design
142(2)
7.4 Data Submission
144(3)
7.4.1 Registration
144(1)
7.4.2 Case Report Forms
145(1)
7.4.3 Specimens
146(1)
7.4.4 Data Submission Enforcement
146(1)
7.5 Data Evaluation
147(3)
7.6 Publication
150(1)
7.7 Quality Assurance Audits
151(1)
7.8 Training
151(1)
7.9 Database Management
152(1)
7.9.1 Database Structures
152(1)
7.10 Conclusion
153(2)
8 Reporting of Results
155(14)
8.1 Timing of Report
155(2)
8.1.1 Phase II Trials
156(1)
8.1.2 Phase III Trials
157(1)
8.2 Required Information
157(2)
8.2.1 Objectives and Design
157(1)
8.2.2 Eligibility and Treatment
158(1)
8.2.3 Results
158(1)
8.3 Analyses
159(8)
8.3.1 Exclusions, Intent to Treat
159(2)
8.3.2 Summary Statistics: Estimates and Variability of Estimates
161(3)
8.3.3 Interpretation of Results
164(1)
8.3.3.1 One-Sided versus Two-Sided Tests
164(1)
8.3.3.2 Positive, Negative, and Equivocal Trials
165(1)
8.3.3.3 Multiple Endpoints
166(1)
8.3.4 Secondary Analyses
166(1)
8.4 Conclusion
167(2)
9 Pitfalls
169(28)
9.1 Introduction
169(1)
9.2 Historical Controls
169(8)
9.3 Competing Risks
177(5)
9.4 Outcome by Outcome Analyses
182(8)
9.4.1 Survival by Response Comparisons
183(2)
9.4.2 "Dose Intensity" Analyses
185(5)
9.5 Subset Analyses
190(3)
9.6 Surrogate Endpoints
193(4)
10 Exploratory Analyses
197(22)
10.1 Introduction
197(1)
10.2 Some Background and Notation
197(3)
10.3 Identification of Prognostic Factors
200(6)
10.3.1 Scale of Measurement
200(4)
10.3.2 Choice of Model
204(2)
10.4 Forming Prognostic Groups
206(3)
10.5 Analysis of Microarray Data
209(4)
10.6 Meta-Analysis
213(4)
10.6.1 Some Principles of Meta-Analyses
213(1)
10.6.2 An Example Meta-Analysis: Portal Vein Infusion
214(1)
10.6.2.1 Inclusion of Trials
214(1)
10.6.2.2 Use of Raw Data
215(1)
10.6.2.3 Lumping Interventions
215(1)
10.6.2.4 Quality of Trials
215(1)
10.6.3 Conclusions from the Portal Vein Meta-Analysis
216(1)
10.6.4 Some Final Remarks on Meta-Analysis
217(1)
10.7 Concluding Remarks
217(2)
11 Summary and Conclusions
219(4)
References 223(20)
Index 243
Stephanie Green, Jacqueline Benedetti