Since the third edition of this Handbook, significant advances have transformed the field of oncology. Most cancer types now offer multiple treatment options, with immunotherapies and targeted therapies becoming the standard of care. Master protocols, which allow the addition of new treatment arms without requiring new protocols, have gained popularity-not only to expedite the approval process for new therapies but also to ensure that patients receive the most beneficial treatments tailored to their individual needs. This revised edition features contributions from leading cancer trial statisticians, providing expert insights into modern oncology trial design and methodology. The Handbook is structured into five key sections:
Part 1: Cancer prevention and screening trial designs, including risk prediction models and prevention trials.
Part 2: Early-phase trial designs, covering dose-finding studies, selection designs, and multi-strata trials.
Part 3: Late-stage trial designs, including approaches for IO therapies, cure-rate models, targeted agents, and considerations for pediatric oncology trials.
Part 4: Trial conduct and operations, addressing best practices for Data Monitoring Committees (DMCs), SWOG/CRAB calculators, pragmatic trials, and clinical trial innovation.
Part 5: Beyond primary endpoints, exploring surrogate endpoints, microbiome research, patient-reported outcomes (PROs), and tree-based partitioning methods.
This updated edition provides a comprehensive resource for researchers, clinicians, and statisticians involved in the evolving landscape of oncology clinical trials.
Key Features:
Practical guidance on designing and conducting oncology clinical trials
Advanced statistical methodologies tailored oncology clinical trials
Best practices for trial execution and management
Key considerations for primary, secondary, and ancillary endpoint considerations
Auflage
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Für höhere Schule und Studium
Academic
Illustrationen
94 s/w Abbildungen, 14 s/w Photographien bzw. Rasterbilder, 80 s/w Zeichnungen
80 Line drawings, black and white; 14 Halftones, black and white; 94 Illustrations, black and white
Maße
Höhe: 254 mm
Breite: 178 mm
ISBN-13
978-1-032-59695-2 (9781032596952)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Antje Hoering is the President and CEO of Cancer Research And Biostatistics (CRAB) and leads a team of approximately eighty talented and dedicated oncology research professionals, all united in our mission to help conquer cancer. She also serves as the lead statistician of the SWOG Myeloma Committee and holds affiliate appointments at the University of Washington Biostatistics department and the Fred Hutch Cancer Center.
Megan Othus is a Professor of Biostatistics at the Fred Hutchinson Cancer. She serves as the lead statistician of the SWOG Leukemia and Rare Cancer Committees. Her research interests focus on the design and endpoints in oncology clinical trials.
John Crowley received his master's and doctorate degrees (in 1970 and 1973, respectively) in Biomathematics from the University of Washington, and was the Director of the SWOG Statistical Center from 1984 to 2012. In 1997 Dr. Crowley founded Cancer Research And Biostatistics (CRAB), was the President and CEO of CRAB until 2014, and currently serves as the Chief of Strategic Alliances. Dr. Crowley's research interests focus on the design and analysis of cancer clinical and translation trials.
Herausgeber*in
Cancer Research and Biostatistics, Washington, USA
Fred Hutchinson Cancer Research Center, USA
Fred Hutchinson Cancer Center, USA
SECTION 1 Cancer Prevention & Screening
Chapter 1 Cancer Screening Trials
Chapter 2 Cancer Risk Prediction Models
Chapter 3 Incorporating External Registry Data Into Cohort-based Cancer Risk Prediction Tools
SECTION 2 Trial Design | Early-Phase Trials
Chapter 4 Phase I - Overview and Recent Trial Designs
Chapter 5 Statistical and Machine Learning Methods for Phase I Dose-Finding
Chapter 6 Seamless Phase I/II Trial Design for Assessing Toxicity and Efficacy for Targeted Agents
Chapter 7 Designs Using Time to Event Endpoints / Single Arm versus Randomized Phase II Designs
Chapter 8 Phase II Selection Designs
Chapter 9 Phase II Trials with Multiple Strata
SECTION 3 Trial Design | Late-Phase Trials
Chapter 10 Cure Rate Survival Models
Chapter 11 Phase III Trials for Targeted Agents
Chapter 12 Phase II and III Clinical Trial Designs for Precision Medicine
Chapter 13 SMARTs in Oncology
Chapter 14 Statistical Considerations in the Design and Analysis of Cancer Trials with Immune-Oncology Therapies
Chapter 15 Alpha Splitting
Chapter 16 Early Stopping of Clinical Trials Evaluating Targeted Therapies
Chapter 17 Noninferiority Trials
Chapter 18 Considerations for Pediatric Oncology Trials
SECTION 4 Trial Conduct
Chapter 19 An Overview of Master Protocols
Chapter 20 On Use of Covariates in Randomization and Analysis of Clinical Trials
Chapter 21 Pragmatic Clinical Trials in Clinical Oncology: A Statistical Perspective
Chapter 22 Dynamic Treatment Regimens and Sequential Multiple Assignment Randomized Trial in Cancer Research
Chapter 23 Outcome-Adaptive Randomization
Chapter 24 Current Suggested Practices and Issues for Data and Safety Monitoring Committees in Cancer Clinical Trials
Chapter 25 Improving Data Collection: EHR-to-EDC Assisted Data Transfer
Chapter 26 Barriers and Disparities in Access to Cancer Clinical Trials - Causes and Implications
Chapter 27 SWOG Statistical Calculators for Design and Analysis of Clinical Trials
Chapter 28 Streamlining Data Collection and Trial Conduct
SECTION 5 Beyond the Primary Endpoint
Chapter 29 Use of Circulating Tumor DNA in Oncology - ctMoniTR
Chapter 30 Statistical Analysis of -Omics Data
Chapter 31 Principles of Design and Analysis for Patient-Reported Outcomes
Chapter 32 X Intermediate and Surrogate Endpoints in Phase III Randomized
Chapter 33 Prognostic Groups via Interpretable Function Approximation: Tree-based and Extreme Regression Models
