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El. knyga: Phase II Clinical Development of New Drugs

  • Formatas: PDF+DRM
  • Serija: ICSA Book Series in Statistics
  • Išleidimo metai: 08-Apr-2017
  • Leidėjas: Springer Verlag, Singapore
  • Kalba: eng
  • ISBN-13: 9789811041945
  • Formatas: PDF+DRM
  • Serija: ICSA Book Series in Statistics
  • Išleidimo metai: 08-Apr-2017
  • Leidėjas: Springer Verlag, Singapore
  • Kalba: eng
  • ISBN-13: 9789811041945

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This book focuses on how to appropriately plan and develop a Phase II program, and how to design Phase II clinical trials and analyze their data. It provides a comprehensive overview of the entire drug development process and highlights key questions that need to be addressed for the successful execution of Phase II, so as to increase its success in Phase III and for drug approval. Lastly it warns project team members of the common potential pitfalls and offers tips on how to avoid them.

Recenzijos

It provides a hands-on approach to each subject, and the necessary statistical background is presented in the most non-mathematical way possible. Throughout the book, the scope of each subject is well defined, and if the reader has the need for further support, useful references are helpfully recommended. I would strongly recommend this book to anyone working in clinical development or with an interest in this field, regardless of their background. (David Manteigas, ISCB News, iscb.info, Issue 65, June, 2018)

1 Introduction
1(26)
1.1 Background
1(1)
1.2 Non-clinical Development
2(2)
1.2.1 Pharmacology
2(1)
1.2.2 Toxicology/Product Safety
3(1)
1.2.3 Formulation Development
4(1)
1.3 Pre-marketing Clinical Development
4(6)
1.3.1 Phase I Clinical Trials
5(2)
1.3.2 Phase II Clinical Trials
7(1)
1.3.3 Phase IB Clinical Trials
7(1)
1.3.4 Clinical Development for Products Treating Life-Threatening Diseases
8(2)
1.3.5 New Drug Application/Biologies License Application
10(1)
1.4 Clinical Development Plan
10(2)
1.5 Patient-Centered Outcomes
12(3)
1.5.1 Clinical Outcome Assessments
12(1)
1.5.2 Patient-Reported Outcomes
13(2)
1.6 Post-marketing Clinical Development
15(2)
1.7 Product Label
17(2)
1.8 Importance of Phase II Clinical Development
19(3)
1.9 Highlight of Each
Chapter of This Book
22(5)
References
24(3)
2 Concept of Alpha
27(28)
2.1 Lady Tasting Tea
27(3)
2.2 Alpha Type I Error Rate
30(2)
2.3 Intention-to-Treat
32(3)
2.4 Patient Analysis Sets
35(1)
2.5 Multiple Comparisons
36(10)
2.5.1 Multiple Doses
36(4)
2.5.2 Multiple Endpoints
40(4)
2.5.3 Other Types of Multiplicity
44(2)
2.6 P-Value and Statistical Significance
46(3)
2.7 Stages of a Clinical Trial
49(2)
2.8 Subject Selection and Choice of Alpha at Phase II
51(4)
References
53(2)
3 Confirmation and Exploration
55(20)
3.1 Introduction
55(1)
3.2 A Motivational Example
56(1)
3.3 Clinical Development Plan (CDP)
57(2)
3.4 Clinical Study Design and Sample Size Calculations
59(1)
3.5 Statistical Analysis Plan (SAP)
60(1)
3.6 Application Example---Another Three Group Phase III Design
61(1)
3.7 Application Example---Dose Selection
62(2)
3.8 Proof of Concept and Dose Ranging
64(2)
3.9 Treatment-by-Factor Interaction
66(5)
3.10 Evaluation of Product Safety
71(1)
3.11 Every Clinical Trial Can Be Considered as Both Confirmatory and Exploratory
72(1)
3.12 Conclusion
73(2)
References
74(1)
4 Design a Proof of Concept Trial
75(18)
4.1 Introduction
75(2)
4.2 Proof of Concept Trials
77(3)
4.2.1 Impact of PoC Decisions
77(2)
4.2.2 How to Communicate Risks Associated with a PoC Study
79(1)
4.3 The Primary Endpoint in a PoC Design
80(1)
4.4 MTD Could Be Under Estimated or Over Estimated
81(2)
4.5 Monotonicity Assumption
83(4)
4.5.1 Background
83(1)
4.5.2 Strong or Weak Application of the Monotonicity Assumption
84(1)
4.5.3 Why This Assumption Is Still Useful
85(2)
4.6 Agreement on a Delta
87(2)
4.7 Choice of Alpha and Beta
89(1)
4.8 Sample Size Considerations
90(3)
References
91(2)
5 Design of Dose-Ranging Trials
93(24)
5.1 Background
93(2)
5.2 Finding Minimum Effective Dose (MinED)
95(2)
5.3 A Motivating Example
97(1)
5.4 How Wide a Range of Doses to Study?
97(3)
5.4.1 Definition of Dose Range in a Given Study
99(1)
5.4.2 Binary Dose Spacing
100(1)
5.5 Frequency of Dosing
100(2)
5.6 Parallel Controlled Fixed Dose Designs
102(2)
5.7 Number of Doses and Control Groups
104(1)
5.8 MCP-Mod
105(2)
5.9 Sample Size Considerations
107(2)
5.10 Application Example
109(4)
5.11 Discussion
113(4)
References
115(2)
6 Combining Proof of Concept and Dose-Ranging Trials
117(14)
6.1 Background
117(1)
6.2 Considerations in Designing Combined PoC and Dose Ranging Studies
118(1)
6.3 Concerns of Using a Dose-Response Model
119(2)
6.4 Sample Size Allocation
121(5)
6.4.1 Comparison of Power
123(3)
6.5 Estimation of Dose-Response Relationship
126(2)
6.6 Risk of Inconclusiveness
128(3)
References
129(2)
7 Risks of Inconclusiveness
131(14)
7.1 Introduction
131(1)
7.2 Go/NoGo Decision in a Two-Group PoC Study
132(5)
7.2.1 The Decision Process
136(1)
7.2.2 The Concept of Another Delta
136(1)
7.3 Go/NoGo Decision with Multiple Treatment Groups
137(2)
7.4 Dose Titration Studies Cannot Be Used for Dose-Finding
139(1)
7.5 A Practical Design to Help Finding MinED
140(2)
7.6 Discussion
142(3)
References
143(2)
8 Analysis of a Proof of Concept Study
145(10)
8.1 Introduction
145(1)
8.2 When the Primary Endpoint Is a Continuous Variable
146(5)
8.2.1 Data Description and Hypothesis
146(1)
8.2.2 T-Test Approach
147(1)
8.2.3 Analysis of Covariance Approach
148(1)
8.2.4 Mixed-Effect Models to Analyze the Longitudinal Data
149(2)
8.3 When the Primary Endpoint Is a Binary Variable
151(2)
8.3.1 Data Description and Hypothesis
151(1)
8.3.2 Cochran-Mantel-Haenszel Method
151(1)
8.3.3 Logistic Regression
152(1)
8.4 Discussion
153(2)
References
154(1)
9 Data Analysis for Dose-Ranging Trials with Continuous Outcome
155(28)
9.1 Introduction
155(2)
9.2 Data and Preliminary Analysis
157(1)
9.3 Establishing PoC with a Trend Test
158(2)
9.4 Multiple Comparison Procedure (MCP) Approach
160(7)
9.4.1 Fisher's Protected LSD (Fixed Sequence Test)
161(1)
9.4.2 Bonferroni Correction
162(1)
9.4.3 Dunnett's Test
163(1)
9.4.4 Holm's Step-Down Procedure
164(1)
9.4.5 Hochberg Step-Up Procedure
165(1)
9.4.6 Gate-Keeping Procedure
166(1)
9.5 Modeling Approach (Mod)
167(6)
9.5.1 Dose-Response Models
167(2)
9.5.2 R Step-by-Step Implementations
169(4)
9.6 MCP-Mod Approach
173(7)
9.6.1 Introduction
173(2)
9.6.2 Step-by-Step Implementations in R Package "MCPMod"
175(5)
9.7 Discussion
180(3)
References
181(2)
10 Data Analysis of Dose-Ranging Trials for Binary Outcomes
183(22)
10.1 Introduction
183(1)
10.2 Data and Preliminary Analysis
184(3)
10.3 Modeling Approach
187(6)
10.3.1 Pearson's Χ2-Test
187(2)
10.3.2 Cochran-Armitage Test for Trend
189(1)
10.3.3 Logistic Regression with Dose as Continuous Variable
190(2)
10.3.4 Logistic Regression with Dose as Categorical Variable
192(1)
10.4 Multiple Comparisons
193(10)
10.4.1 The Raw p-Values
193(1)
10.4.2 Bonferroni Adjustment
194(1)
10.4.3 Bonferroni-Holm Procedure
195(2)
10.4.4 Hochberg Procedure
197(1)
10.4.5 Gatekeeping Procedure
198(1)
10.4.6 MCP Using p-Values from Cochran-Mantel-Haenszel Test
199(4)
10.5 Discussion
203(2)
References
204(1)
11 Bayesian Approach
205(20)
11.1 Introduction
205(3)
11.1.1 An Example on Bayesian Concept
205(1)
11.1.2 A Brief History
206(1)
11.1.3 B ayes Theorem
206(1)
11.1.4 Bayesian Hypothesis Testing Framework
207(1)
11.2 Bayesian Updating
208(4)
11.2.1 Example Continued for Bayesian Updating
208(4)
11.3 Bayesian Inference
212(2)
11.4 Markov Chain Monte Carlo (MCMC) Method
214(2)
11.5 Bayesian Methods for Phase II Clinical Trials
216(1)
11.6 Example
217(8)
11.6.1 Using Non-informative Priors
218(2)
11.6.2 Using Informative Priors
220(2)
11.6.3 Summary
222(1)
References
223(2)
12 Overview of Phase III Clinical Trials
225
12.1 Introduction
225(1)
12.2 Scope of Phase IH Plans
225(1)
12.3 Drug Label and Target Product Profile
226(1)
12.4 Phase III Non-inferiority Trial Designs
227(1)
12.5 Dose and Regimen Selection, Drug Formulation and Patient Populations
228(3)
12.5.1 Dose and Regimen Selection
228(2)
12.5.2 Drug Formulations
230(1)
12.5.3 Patient Populations
231(1)
12.6 Number of Phase III Trials for a Labeling Claim
231(1)
12.7 Number of Primary Efficacy Endpoints
232(1)
12.8 Missing Data Issues
233(1)
12.9 Phase III Clinical Outcome Assessments
234(3)
12.10 Multi-regional Phase III Clinical Trial Issues
237(1)
12.11 The Trend Towards Personalized or Precision Medicines
238(1)
12.12 Summary
239
References
239
Naitee Ting is a Fellow of ASA. He is currently a Director in the Department of Biostatistics and Data Sciences at Boehringer-Ingelheim Pharmaceuticals Inc. (BI).  He joined BI in September of 2009, and before joining BI, he was at Pfizer Inc. for 22 years (1987-2009).  Naitee received his Ph.D. in 1987 from Colorado State University (major in Statistics).  He has an M.S. degree from Mississippi State University (1979, Statistics) and a B.S. degree from College of Chinese Culture (1976, Forestry) at Taipei, Taiwan.  Naitee published articles in Technometrics, Statistics in Medicine, Drug Information Journal, Journal of Statistical Planning and Inference, Journal of Biopharmaceutical Statistics, Biometrical Journal, Statistics and Probability Letters, and Journal of Statistical Computation and Simulation.  His book "Dose Finding in Drug Development" was published in 2006 by Springer, and is considered as the leading reference in the field ofdose response clinical trials.  The book "Fundamental Concepts for New Clinical Trialists", co-authored with Scott Evans, was published by CRC in 2015.  Naitee is an adjunct professor of Columbia University, University of Connecticut and University of Rhode Island.  Naitee has been an active member of both the American Statistical Association (ASA) and the International Chinese Statistical Association (ICSA). Professor Ding-Geng Chen is a fellow of the American Statistical Association and currently the Wallace Kuralt distinguished professor at the University of North Carolina at Chapel Hill, USA, and an extraordinary professor at University of Pretoria, South Africa. He was a professor at the University of Rochester and the Karl E. Peace endowed eminent scholar chair in biostatistics at Georgia Southern University. He is also a senior consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics and public health statistics. Professor Chen has written more than 150 referred publications and co-authored/co-edited twelve books on clinical trial methodology with R and SAS, meta-analysis using R, advanced statistical causal-inference modeling, Monte-Carlo simulations, advanced public health statistics and statistical models in data science.

Shuyen Ho received his PhD in Statistics from University of Wisconsin Madison, and his Bachelor in Applied Mathematics from Taiwan. Dr. Ho is a Biostatistics Director at PAREXEL International in Durham, North Carolina and has worked in the pharmaceutical industry for over 25 years. Prior to PAREXEL, he was a Clinical Statistics Director at GlaxoSmithKline (GSK) and Group Leader at Merck. He specializes in Phase II & III clinical development and has helped developed widely used respiratory medicines such as Claritin, Advair and Veramyst. 

Joseph C. Cappelleri earned his MS in statistics from the City University of New York (Baruch College), PhD in psychometrics from Cornell University, and MPH in epidemiology from Harvard University. Dr. Cappelleri is a senior director of biostatistics at Pfizer Inc. He has also served on the adjunct faculties at Brown University, Tufts Medical Center, and the University of Connecticut. A Fellow of the American Statistical Association, he has delivered numerous conference presentations and published extensively on clinical and methodological topics, including regression-discontinuity designs, meta-analysis, and health measurement scales. Dr. Cappelleri is the lead author of the book Patient-Reported Outcomes: Measurement, Implementation and Interpretation.