Muutke küpsiste eelistusi

E-raamat: Bayesian Designs for Phase I-II Clinical Trials [Taylor & Francis e-raamat]

(the University of Texas MD Anderson Cancer Center, Houston, USA), (M.D. Anderson Cancer Center, Houston, Texas, USA), (University of Texas, USA)
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 156,95 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 224,21 €
  • Säästad 30%
Bayesian Designs for Phase I-II Clinical TrialsSuurem pilt
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781315371405

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.

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.



This book is the first to focus on Bayesian phase I–II clinical trials. It describes many problems with the conventional phase I–phase II paradigm and covers a large number of modern Bayesian phase I–II clinical trial designs.

Preface xiii
1 Why Conduct Phase I-II Trials?
1(28)
1.1 The Conventional Paradigm
1(4)
1.2 The Continual Reassessment Method
5(3)
1.3 Problems with Conventional Dose-Finding Methods
8(21)
1.3.1 3+3 Algorithms
8(2)
1.3.2 Some Comparisons
10(8)
1.3.3 Problems Going from Phase I to Phase II
18(1)
1.3.4 Consequences of Ignoring Information
19(3)
1.3.5 Late-Onset Outcomes
22(1)
1.3.6 Expansion Cohorts
23(2)
1.3.7 Guessing a Schedule
25(1)
1.3.8 Patient Heterogeneity
26(3)
2 The Phase I-II Paradigm
29(14)
2.1 Efficacy and Toxicity
29(1)
2.2 Elements of Phase I-II Designs
30(1)
2.3 Treatment Regimes and Clinical Outcomes
31(2)
2.4 Sequentially Adaptive Decision Making
33(2)
2.5 Risk-Benefit Trade-Offs
35(2)
2.6 Stickiness and Adaptive Randomization
37(4)
2.7 Simulation as a Design Tool
41(2)
3 Establishing Priors
43(16)
3.1 Pathological Priors
43(3)
3.2 Prior Effective Sample Size
46(4)
3.3 Computing Priors from Elicited Values
50(9)
3.3.1 Least Squares Algorithm
54(1)
3.3.2 Pseudo Sampling Algorithm
55(4)
4 Efficacy-Toxicity Trade-Off-Based Designs
59(30)
4.1 General Structure
59(1)
4.2 Probability Model
60(2)
4.3 Dose Admissibility Criteria
62(1)
4.4 Trade-Off Contours
63(1)
4.5 Establishing a Prior
64(3)
4.6 Steps for Constructing a Design
67(2)
4.7 Illustration
69(2)
4.8 Sensitivity to Target Contours
71(1)
4.9 Sensitivity to Prior ESS
71(3)
4.10 Trinary Outcomes
74(5)
4.11 Time-to-Event Outcomes
79(10)
5 Designs with Late-Onset Outcomes
89(16)
5.1 A Common Logistical Problem
89(2)
5.2 Late-Onset Events as Missing Data
91(5)
5.3 Probability Model
96(1)
5.4 Imputation of Delayed Outcomes
97(2)
5.5 Illustration
99(6)
6 Utility-Based Designs
105(24)
6.1 Assigning Utilities to Outcomes
105(6)
6.2 Subjectivity of Utilities
111(2)
6.3 Utility-Based Sequential Decision Making
113(6)
6.3.1 Utility Elicitation
113(1)
6.3.2 Computing Mean Utilities
114(1)
6.3.3 Regime Acceptability Criteria
115(1)
6.3.4 Design Evaluation Criteria
116(1)
6.3.5 Utility Sensitivity Analyses
117(1)
6.3.6 More Elaborate Utilities
118(1)
6.4 Optimizing Radiation Dose for Brain Tumors
119(10)
7 Personalized Dose Finding
129(20)
7.1 The EffTox Design with Covariates
129(9)
7.2 Biomarker-Based Dose Finding
138(11)
8 Combination Trials
149(30)
8.1 Bivariate Binary Outcomes
150(9)
8.2 Bivariate Ordinal Outcomes
159(20)
8.2.1 Generalized Continuation Ratio Model
159(1)
8.2.2 Generalized Aranda-Ordaz Link Model
160(6)
8.2.3 An mTOR Inhibitor Chemo Combination Trial
166(2)
8.2.4 Parametric Dose Standardization
168(5)
8.2.5 mTOR Inhibitor Trial Design
173(4)
8.2.6 Alternative Models
177(2)
9 Optimizing Molecularly Targeted Agents
179(24)
9.1 Features of Targeted Agents
179(1)
9.2 One Targeted Agent
180(6)
9.3 Combining Targeted and Cytotoxic Agents
186(9)
9.4 Combining Two Molecularly Targeted Agents
195(8)
10 Optimizing Doses in Two Cycles
203(20)
10.1 The Two-Cycle Problem
203(2)
10.2 A Two-Cycle Model
205(4)
10.3 Decision Criteria
209(3)
10.4 Illustration
212(3)
10.5 Simulation Study
215(8)
11 Optimizing Dose and Schedule
223(22)
11.1 Schedule-Dependent Effects
223(1)
11.2 Trinary Outcomes
224(7)
11.3 Event Times Outcomes
231(14)
12 Dealing with Dropouts
245(8)
12.1 Dropouts and Missing Efficacy
245(1)
12.2 Probability Model
246(2)
12.3 Dose-Finding Algorithm
248(1)
12.4 Simulations
249(4)
13 Optimizing Intra-Arterial tPA
253(14)
13.1 Rapid Treatment of Stroke
253(1)
13.2 Probability Model
254(5)
13.3 Decision Criteria and Trial Conduct
259(1)
13.4 Priors
260(1)
13.5 Simulations
261(6)
14 Optimizing Sedative Dose in Preterm Infants
267(16)
14.1 Respiratory Distress Syndrome in Neonates
267(3)
14.2 Clinical Outcomes and Probability Model
270(3)
14.3 Prior and Likelihood
273(1)
14.4 Decision Criteria
274(3)
14.5 Simulations
277(6)
Bibliography 283(18)
Index 301
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.
Püsilink: https://www.kriso.ee/db/9781315371405_pe.html
Märksõnad: