Bayesian Designs for Phase I–II Clinical Trials


Language: Anglais
Cover of the book Bayesian Designs for Phase I–II Clinical Trials

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310 p. · Hardback
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.

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
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

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
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

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

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


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.