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E-raamat: Introductory Adaptive Trial Designs: A Practical Guide with R

(AMAG Pharmaceuticals, Inc, Lexington, Massachusetts, USA)
  • Formaat: 232 pages
  • Ilmumisaeg: 21-May-2015
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781040070949
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Introductory Adaptive Trial Designs: A Practical Guide with RSuurem pilt
  • Formaat: 232 pages
  • Ilmumisaeg: 21-May-2015
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781040070949
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All the Essentials to Start Using Adaptive Designs in No Time





Compared to traditional clinical trial designs, adaptive designs often lead to increased success rates in drug development at reduced costs and time. Introductory Adaptive Trial Designs: A Practical Guide with R motivates newcomers to quickly and easily grasp the essence of adaptive designs as well as the foundations of adaptive design methods.





The book reduces the mathematics to a minimum and makes the material as practical as possible. Instead of providing general, black-box commercial software packages, the author includes open-source R functions that enable readers to better understand the algorithms and customize the designs to meet their needs. Readers can run the simulations for all the examples and change the input parameters to see how each input parameter affects the simulation outcomes or design operating characteristics.





Taking a learning-by-doing approach, this tutorial-style book guides readers on planning and executing various types of adaptive designs. It helps them develop the skills to begin using the designs immediately.
Preface xiii
1 Introduction 1(10)
1.1 Motivation
1(1)
1.2 Adaptive Designs in Clinical Trials
1(6)
1.2.1 Group Sequential Design
2(1)
1.2.2 Sample-Size Reestimation Design
2(1)
1.2.3 Drop-Arm Design
3(2)
1.2.4 Add-Arm Design
5(1)
1.2.5 Adaptive Randomization Design
5(1)
1.2.6 Biomarker-Enrichment Design
5(1)
1.2.7 Adaptive Dose-Escalation Design
6(1)
1.3 Clinical Trial Simulation
7(1)
1.4 Characteristics of Adaptive Designs
7(1)
1.5 FAQs about Adaptive Designs
8(3)
2 Classical Design 11(14)
2.1 Introduction
11(1)
2.2 Two-Group Superiority
12(2)
2.3 Two-Group Noninferiority Trial
14(2)
2.4 Two-Group Equivalence Trial
16(2)
2.5 Trial with Any Number of Groups
18(5)
2.6 Multigroup Dose-Finding Trial
23(1)
2.7 Summary and Discussion
24(1)
3 Two-Stage Adaptive Confirmatory Design Method 25(12)
3.1 General Formulation
25(1)
3.2 Method Based on Sum of p-Values
26(2)
3.3 Method with Product of p-Values
28(2)
3.4 Method with Inverse-Normal p-Values
30(2)
3.5 Comparisons of Adaptive Design Methods
32(5)
4 K-Stage Adaptive Confirmatory Design Methods 37(8)
4.1 Test Statistics
37(1)
4.2 Determination of Stopping Boundary
37(3)
4.2.1 Analytical Formulation for MSP
38(1)
4.2.2 Analytical Formulation for MPP
39(1)
4.2.3 Stopping Boundaries for MINP
39(1)
4.3 Error-Spending Function
40(1)
4.4 Power and Sample Size
41(3)
4.5 Error Spending Approach
44(1)
5 Sample-Size Reestimation Design 45(10)
5.1 Sample Size Reestimation Methods
45(6)
5.1.1 The Blinded Approach
45(1)
5.1.2 The Unblinded Approach
46(4)
5.1.3 The Mixed Approach
50(1)
5.2 Comparisons of SSR Methods
51(1)
5.3 K-Stage Sample Size Reestimation Trial
52(1)
5.4 Summary
53(2)
6 Special Two-Stage Group Sequential Trials 55(18)
6.1 Event-Based Design
55(1)
6.2 Equivalence Trial
55(2)
6.3 Adaptive Design with Farrington-Manning Margin
57(1)
6.4 Noninferiority Trial with Paired Binary Data
58(5)
6.4.1 Noninferiority Hypothesis
58(1)
6.4.2 Prostate Cancer Diagnostic Trial
59(4)
6.5 Trial with Incomplete Paired Data
63(4)
6.5.1 Mixture of Paired and Unpaired Data
63(1)
6.5.2 Minimum Variance Estimator
64(1)
6.5.3 Retinal Disease Trial
65(2)
6.6 Trial with Coprimary Endpoints
67(2)
6.6.1 Introduction
67(1)
6.6.2 Group Sequential Trial
68(1)
6.7 Trial with Multiple Endpoints
69(4)
7 Pick-the-Winners Design 73(6)
7.1 Overview of Multiple-Arm Designs
73(1)
7.2 Pick-the-Winner Design
74(2)
7.3 Stopping Boundary and Sample Size
76(1)
7.4 Summary and Discussion
77(2)
8 The Add-Arms Design 79(18)
8.1 Introduction
79(2)
8.2 The Add-Arm Design
81(3)
8.2.1 Design Description
81(2)
8.2.2 Controlling Selection Probability and Type-I Error
83(1)
8.3 Clinical Trial Examples
84(4)
8.3.1 Phase-II Dose-Finding Designs
84(2)
8.3.2 Phase-II-III Seamless Designs
86(1)
8.3.3 Phase-IV Postmarketing Study
86(2)
8.4 Extension of Add-Arms Designs
88(6)
8.4.1 Design Description
88(3)
8.4.2 Stopping Boundary and Selection Probability
91(1)
8.4.3 Comparison of Power
92(2)
8.5 Summary
94(3)
9 Biomarker-Adaptive Design 97(10)
9.1 Taxonomy
97(2)
9.2 Biomarker-Enrichment Design
99(2)
9.3 Biomarker-Informed Adaptive Design
101(5)
9.3.1 Single-Level Model
101(3)
9.3.2 Hierarchical Model
104(2)
9.4 Summary
106(1)
10 Response-Adaptive Randomization 107(6)
10.1 Basic Response-Adaptive Randomization
107(2)
10.2 Generalized Response-Adaptive Randomization
109(2)
10.3 Summary and Discussion
111(2)
11 Adaptive Dose-Escalation Trial 113(10)
11.1 Oncology Dose-Escalation Trial
113(2)
11.1.1 Dose Level Selection
113(1)
11.1.2 Traditional Escalation Rules
114(1)
11.2 Continual Reassessment Method
115(3)
11.2.1 Reassessment of Parameter
115(2)
11.2.2 Assignment of Next Patient
117(1)
11.2.3 Stopping Rule
117(1)
11.3 Alternative Form CRM
118(1)
11.4 Evaluation of Dose-Escalation Design
118(2)
11.5 Summary and Discussion
120(3)
12 Deciding Which Adaptive Design to Use 123(8)
12.1 Determining the Objectives
124(1)
12.2 Determining Design Parameters
124(3)
12.2.1 Model Parameters
124(2)
12.2.2 Determine the Thresholds for α and β
126(1)
12.2.3 Determine the Stopping Boundaries
126(1)
12.3 Evaluation Matrix of Adaptive Design
127(4)
12.3.1 Group Sequential Design
127(1)
12.3.2 Sample-Size Reestimation Design
128(1)
12.3.3 Dose-Finding Trial (Add-Arm, Drop-Arm Design)
128(1)
12.3.4 Response-Adaptive Randomization
128(1)
12.3.5 Dose-Escalation Design
129(1)
12.3.6 Biomarker-Enrichment Design
129(1)
12.3.7 Biomarker-Informed Design
130(1)
13 Monitoring Trials and Making Adaptations 131(4)
13.1 Stopping and Arm-Selection
131(1)
13.2 Conditional Power
132(1)
13.3 Sample-Size Reestimation
132(1)
13.4 New Randomization Scheme
133(2)
14 Data Analyses of Adaptive Trials 135(6)
14.1 Orderings in Sample Space
135(2)
14.2 Adjusted p-Value
137(1)
14.2.1 Definitions of p-Values
137(1)
14.2.2 Simulation Approach
137(1)
14.3 Parameter Estimation
138(1)
14.4 Confidence Interval
139(1)
14.5 Summary
140(1)
15 Planning and Execution 141(12)
15.1 Study Planning
141(2)
15.2 Working with a Regulatory Agency
143(3)
15.3 Trial Execution
146(5)
15.3.1 Dynamic Drug Supply
146(2)
15.3.2 Data Monitor Committee
148(3)
15.4 Summary
151(2)
Appendix A Thirty-Minute Tutorial to R 153(6)
A.1 Introduction
153(1)
A.2 R Language Basics
154(5)
A.2.1 Arithmetics
154(1)
A.2.2 Vector
155(1)
A.2.3 Matrix
155(1)
A.2.4 Functions
155(1)
A.2.5 Conditional Statement and Loop
156(1)
A.2.6 Writing and Invoking R Function
157(1)
A.2.7 Graphics
157(2)
Appendix B R Functions for Adaptive Designs 159(44)
B.1
Chapter 2
159(1)
B.2
Chapter 3
160(7)
B.3
Chapter 4
167(4)
B.4
Chapter 5
171(11)
B.5
Chapter 6
182(6)
B.6
Chapter 7
188(2)
B.7
Chapter 8
190(1)
B.8
Chapter 9
191(2)
B.9
Chapter 10
193(3)
B.10
Chapter 11
196(2)
B.11
Chapter 12
198(1)
B.12
Chapter 13
199(2)
B.13
Chapter 14
201(1)
B.14 Commonly Used Stopping Boundaries
202(1)
Bibliography 203(8)
Index 211
Mark Chang is vice president of biometrics at AMAG Pharmaceuticals and an adjunct professor at Boston University. Dr. Chang is an elected fellow of the American Statistical Association and a co-founder of the International Society for Biopharmaceutical Statistics. He serves on the editorial boards of statistical journals and has published eight books, including Principles of Scientific Methods, Paradoxes in Scientific Inference, Modern Issues and Methods in Biostatistics, Monte Carlo Simulation for the Pharmaceutical Industry, and Adaptive Design Theory and Implementation Using SAS and R, Second Edition.
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