Yuan / Nguyen / Thall Bayesian Designs for Phase I–II Clinical Trials

Erscheinungsjahr 2016
ISBN: 978-1-4987-0956-9
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 324 Seiten

Reihe: Chapman & Hall/CRC Biostatistics Series

Reliably optimizing a new treatment in humans is a critical first step in clinical evaluation since choosing a suboptimal dose or schedule may lead to failure in later trials. At the same time, if promising preclinical results do not translate into a real treatment advance, it is important to determine this quickly and terminate the clinical evaluation process to avoid wasting resources.

Bayesian Designs for Phase I–II Clinical Trials describes how phase I–II designs can serve as a bridge or protective barrier between preclinical studies and large confirmatory clinical trials. It illustrates many of the severe drawbacks with conventional methods used for early-phase clinical trials and presents numerous Bayesian designs for human clinical trials of new experimental treatment regimes.

The first two chapters minimize the technical language to make them accessible to non-statisticians. These chapters discuss the severe drawbacks of the conventional paradigm used for early-phase clinical trials and explain the phase I–II paradigm for optimizing dose, or more general treatment regimes, based on both efficacy and toxicity. The remainder of the book covers a wide variety of clinical trial methodologies, including designs to optimize the dose pair of a two-drug combination, jointly optimize dose and schedule, identify optimal personalized doses, optimize novel molecularly targeted agents, and choose doses in two treatment cycles.

Written by research leaders from the University of Texas MD Anderson Cancer Center, this book shows how Bayesian designs for early-phase clinical trials can explore, refine, and optimize new experimental treatments. It emphasizes the importance of basing decisions on both efficacy and toxicity.

Autoren/Hrsg.

Yuan, Ying

Nguyen, Hoang Q.

Thall, Peter F.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

Why Conduct Phase I-II Trials?
The Conventional Paradigm
The Continual Reassessment Method
Problems with Conventional Dose-Finding Methods

The Phase I-II Paradigm
Efficacy and Toxicity
Elements of Phase I-II Designs
Treatment Regimes and Clinical Outcomes
Sequentially Adaptive Decision Making
Risk-Benefit Trade-Offs
Stickiness and Adaptive Randomization
Simulation as a Design Tool

Establishing Priors
Pathological Priors
Prior Effective Sample Size
Computing Priors from Elicited Values

Efficacy-Toxicity Trade-Off–Based Designs
General Structure
Probability Model
Admissibility Criteria
Trade-off Contours
Establishing a Prior
Steps for Constructing a Design
Illustration
Sensitivity to Target Contours
Sensitivity to Prior ESS
Trinary Outcomes
Time-to-Event Outcomes

Designs with Late-Onset Outcomes
A Common Logistical Problem
Late-Onset Events as Missing Data
Probability Model
Imputation of Delayed Outcomes
Illustration

Utility-Based Designs
Assigning Utilities to Outcomes
Subjectivity of Utilities
Utility-Based Sequential Decision Making
Optimizing Radiation Dose for Brain Tumors

Personalized Dose Finding
The EffTox Design with Covariates
Biomarker-Based Dose Finding

Combination Trials
Bivariate Binary Outcomes
Bivariate Ordinal Outcomes

Optimizing Molecularly Targeted Agents
Features of Targeted Agents
One Targeted Agent
Combining Targeted and Cytotoxic Agents
Combining Two Molecularly Targeted Agents

Optimizing Doses in Two Cycles
The Two-Cycle Problem
A Two-Cycle Model
Decision Criteria
Illustration
Simulation Study

Optimizing Dose and Schedule
Schedule Dependent Effects
Trinary Outcomes
Event Times Outcomes

Dealing with Dropouts
Dropouts and Missing Efficacy
Probability Model
Dose-Finding Algorithm
Simulations

Optimizing Intra-Arterial tPA
Rapid Treatment of Stroke
Probability Model
Decision Criteria and Trial Conduct
Priors
Simulations

Optimizing Sedative Dose in Preterm Infants
Respiratory Distress Syndrome in Neonates
Clinical Outcomes and Probability Model
Prior and Likelihood
Decision Criteria
Simulations

Bibliography

Über Autor(innen)

Ying Yuan is a professor and co-chief of the Section of Adaptive Clinical Trials in the Department of Biostatistics at the University of Texas MD Anderson Cancer Center. He is also an adjunct associate professor in the Department of Statistics at Rice University. Dr. Yuan has published over 100 peer-reviewed research papers in top statistical and medical journals. He is an associate editor of Biometrics and a board member of the International Chinese Statistical Association. He received his PhD in biostatistics from the University of Michigan. His research interests include Bayesian adaptive clinical trial design, statistical analysis of missing data, and Bayesian statistics.

Hoang Q. Nguyen is a senior computational scientist in the Department of Biostatistics at the University of Texas MD Anderson Cancer Center. He received his PhD in computational and applied mathematics from Rice University. His research interests include Bayesian clinical trial design, computational algorithms, regression modeling, and Bayesian data analysis.

Peter F. Thall is the Anise J. Sorrell Professor in the Department of Biostatistics at the University of Texas MD Anderson Cancer Center. He is also an adjunct professor in the Department of Statistics at Rice University. Dr. Thall is a fellow of the American Statistical Association (ASA) and the Society for Clinical Trials, an associate editor for Clinical Trials and Statistics in Biosciences, and an ASA Media Expert. He has published over 200 papers and book chapters in the statistical and medical literature. He received his PhD in statistics and probability from the Florida State University. His research interests include clinical trial design, dynamic treatment regimes, prior elicitation, Bayesian nonparametric statistics, and personalized medicine.