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E-raamat: Fundamental Concepts for New Clinical Trialists

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(George Washington University), (Boehringer-Ingelheim, Ledyard, Connecticut, USA)
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Fundamental Concepts for New Clinical Trialists describes the core scientific concepts of designing, data monitoring, analyzing, and reporting clinical trials as well as the practical aspects of trials not typically discussed in statistical methodology textbooks.

The first section of the book provides background information about clinical trials. It defines and compares clinical trials to other types of research studies and discusses clinical trial phases, registration, the protocol document, ethical issues, product development, and regulatory processes. It also includes a special chapter outlining the valuable attributes that statisticians can develop to maximize their contributions to a clinical trial.

The second section examines scientific issues faced in each progressive step of a clinical trial. It covers issues in trial design, such as randomization, blinding, control-group selection, endpoint selection, superiority versus noninferiority, and parallel group versus crossover designs; data monitoring; analyses of efficacy, safety, and benefit-risk; and the reporting/publication of clinical trial results.

As clinical trials remain the gold standard research studies for evaluating the effects of a medical intervention, newcomers to the field must have a fundamental understanding of the concepts to tackle real-world issues in all stages of trials. Drawing on their experiences in academia and industry, the authors provide a foundation for understanding the fundamental concepts necessary for working in clinical trials.

Arvustused

"The book focuses on important concepts and promotes thinking clinical trials, and it is very readable. This book targets both statisticians and non-statisticians and wishes to facilitate better communication between them. I found that some chapters are especially useful for statisticians involved in clinical trials. . . Dr Evans uses this book as part of his Principles of Clinical Trials course at the Harvard School of Public Health. Overall, it is an exciting book!" ~International Statistical Review

"Statisticians learn the easy part of designing and analyzing clinical trials in class, but we usually learn the hard parts by our post-graduate failures. This book offers a course outline and valuable set of instructions to describe how to avoid many lessons we might otherwise have to learn the hard way." ~The International Biometric Society "The book focuses on important concepts and promotes thinking clinical trials, and it is very readable. This book targets both statisticians and non-statisticians and wishes to facilitate better communication between them. I found that some chapters are especially useful for statisticians involved in clinical trials. . . Dr Evans uses this book as part of his Principles of Clinical Trials course at the Harvard School of Public Health. Overall, it is an exciting book!" ~International Statistical Review

"Statisticians learn the easy part of designing and analyzing clinical trials in class, but we usually learn the hard parts by our post-graduate failures. This book offers a course outline and valuable set of instructions to describe how to avoid many lessons we might otherwise have to learn the hard way." ~The International Biometric Society

Preface xv
Authors xix
Section I Background
1 Clinical Trials
3(22)
1.1 Introduction
3(5)
1.2 Phases
8(3)
1.3 Protocol
11(3)
1.4 Clinical Trial Registration
14(2)
1.5 Ethical Issues
16(6)
1.5.1 Historical Ethical Failures
17(1)
1.5.2 Landmark Documents
17(1)
1.5.3 Institutional Review Boards
18(2)
1.5.4 Informed Consent
20(1)
1.5.5 Modern Cases of Negligence
21(1)
1.5.6 Statistical Ethics
21(1)
References
22(3)
2 Product Development Process
25(14)
2.1 The Drug Label
26(1)
2.2 Nonclinical Development
27(3)
2.2.1 Pharmacology
27(1)
2.2.2 Toxicology/Drug Safety
28(1)
2.2.3 Drug Formulation Development
29(1)
2.3 Clinical Development
30(8)
2.3.1 Phase I Clinical Trials
30(1)
2.3.2 Phase II/III Clinical Trials
31(2)
2.3.3 New Drug Application
33(1)
2.3.4 Accelerated Approval and Unique Clinical Development Methods
34(1)
2.3.5 Clinical Development Plan
35(1)
2.3.6 Postmarketing Development
36(2)
References
38(1)
3 Regulatory Review Organizations
39(18)
3.1 Food and Drug Administration
39(12)
3.1.1 Drugs
41(1)
3.1.2 Biologics
41(2)
3.1.3 Devices
43(2)
3.1.4 FDA—Industry Interactions
45(15)
3.1.4.1 Pre-IND Meeting
46(1)
3.1.4.2 End of Phase II Meeting
46(1)
3.1.4.3 Pre-NDA/BLA Meeting
47(1)
3.1.4.4 Advisory Committee Meetings
48(3)
3.2 European Medicines Agency
51(3)
3.3 Guidances
54(2)
References
56(1)
4 Clinical Trial Statisticians
57(14)
4.1 Roles of the Clinical Trial Statistician
57(3)
4.2 Important Attributes and Suggestions for Development
60(7)
4.2.1 Improve Communication Skills (Writing, Listening, Speaking, and Presenting)
61(1)
4.2.2 Keep Learning (Statistics and Medicine)
62(1)
4.2.3 Know the Medical Literature
62(1)
4.2.4 Think First (before Researching) and Keep Thinking
62(1)
4.2.5 Educate Colleagues regarding Fundamental Statistical Concepts
62(1)
4.2.6 Identify Options and Their Pros and Cons
63(1)
4.2.7 Be Proactive
63(1)
4.2.8 Become Detective Sherlock Holmes
63(1)
4.2.9 Avoid Being Isolated
63(1)
4.2.10 Ask Lots of Questions; Question the Question
63(1)
4.2.11 Voice Scientific Opinions
64(1)
4.2.12 Protect Scientific Integrity
64(1)
4.2.13 Use Your References and Resources
64(1)
4.2.14 Identify Mentors
65(1)
4.2.15 Learn from Your Mistakes
65(1)
4.2.16 Do Not Rush with Answers
65(1)
4.2.17 Be Open-Minded and Compassionate; Practice Humility and Professionalism
65(1)
4.2.18 Finish the Job
66(1)
4.2.19 Participate in Professional Societies, Attend Professional Meetings, and Take Short Courses
66(1)
References
67(4)
Section II Scientific and Practical Issues
5 General Considerations in Clinical Trial Design
71(50)
5.1 General Design Issues in Clinical Trials
71(22)
5.1.1 What Is the Question?
71(1)
5.1.2 Design Efficiency and Robustness
72(1)
5.1.3 Selection of a Population and Entry Criteria
73(2)
5.1.4 Selection of Endpoints
75(7)
5.1.4.1 Desirable Characteristics of Endpoints
75(1)
5.1.4.2 Scales of Measurement
76(1)
5.1.4.3 Objective vs. Subjective Endpoints
77(1)
5.1.4.4 Composite Endpoints
77(2)
5.1.4.5 Multiple Endpoints
79(1)
5.1.4.6 Surrogate Endpoints
80(2)
5.1.5 Controlled vs. Uncontrolled Single-Arm Trials
82(1)
5.1.6 Sample Size
83(5)
5.1.6.1 Hypothesis Testing versus Precision
83(1)
5.1.6.2 Choosing an Acceptable Type I Rate
83(1)
5.1.6.3 Choosing an Acceptable Type II Error Rate
84(1)
5.1.6.4 Choosing the Minimum Clinically Important Difference
84(1)
5.1.6.5 Estimating Variability
85(1)
5.1.6.6 Group Sequential and Adaptive Designs
85(1)
5.1.6.7 Other Issues to Be Considered during Sample Size Calculation
86(2)
5.1.6.8 Simulations
88(1)
5.1.6.9 Sensitivity Analyses
88(1)
5.1.7 Data Management Considerations
88(2)
5.1.7.1 Case Report Form Development
89(1)
5.1.8 The Prevention of Missing Data
90(3)
5.2 Design Issues in Controlled Clinical Trials
93(17)
5.2.1 Randomization
93(5)
5.2.1.1 Stratification
93(1)
5.2.1.2 Block Randomization
93(2)
5.2.1.3 Adaptive Randomization
95(1)
5.2.1.4 Interactive Voice Recognition System
96(1)
5.2.1.5 Cluster Randomization
97(1)
5.2.2 Blinding/Masking
98(7)
5.2.2.1 Selection of a Control Group
100(1)
5.2.2.2 Placebos/Shams
100(3)
5.2.2.3 Active Controls
103(1)
5.2.2.4 Historical Controls
104(1)
5.2.3 Parallel Group vs. Crossover Designs
105(5)
5.2.3.1 Parallel Group Designs
105(1)
5.2.3.2 Crossover Designs
106(4)
5.3 Special Issues
110(8)
5.3.1 Design Issues in Biologics
110(3)
5.3.1.1 Immunogenicity Studies, Field Studies, Lot Consistency Studies
112(1)
5.3.2 Design Issues in Devices
113(1)
5.3.3 Multicenter Trials
114(1)
5.3.3.1 Multiregional Trials
114(1)
5.3.4 Design Issues in Rare Diseases
115(1)
5.3.5 Bayesian Designs
116(2)
References
118(3)
6 Clinical Trial Designs
121(54)
6.1 Phase I
121(5)
6.1.1 PK/PD Designs
122(1)
6.1.2 Bioavailability/Bioequivalence
123(2)
6.1.3 Estimation of MTD
125(1)
6.2 Other Trial Designs Including Phase II and III
126(43)
6.2.1 Proof of Concept Study
126(1)
6.2.2 Dose-Finding Study Designs
127(5)
6.2.2.1 Frequency of Dosing
128(1)
6.2.2.2 Fixed Dose versus Dose Titration Designs
129(1)
6.2.2.3 Range of Doses to Be Studied
130(1)
6.2.2.4 Number of Doses
130(1)
6.2.2.5 Dose Allocation and Dose Spacing
131(1)
6.2.2.6 Adaptive Dose-Finding
132(1)
6.2.3 Noninferiority Trials
132(11)
6.2.3.1 Examples
133(2)
6.2.3.2 Design Issues
135(5)
6.2.3.3 Clarification of the Two Distinct Objectives
140(1)
6.2.3.4 Analyses
141(1)
6.2.3.5 Missing Data
141(1)
6.2.3.6 Switching between NI and Superiority
142(1)
6.2.4 Futility Designs
143(1)
6.2.5 Factorial Designs
144(2)
6.2.5.1 The 2 x 2 Factorial Design
144(1)
6.2.5.2 The No Interaction Assumption
145(1)
6.2.5.3 Sample Size
146(1)
6.2.6 Factorial Designs with More than Two Factors and Assessment of More than One Outcome
146(2)
6.2.6.1 Interim Monitoring
147(1)
6.2.6.2 Recruitment and Adherence
147(1)
6.2.6.3 Analyses and Reporting
147(1)
6.2.7 Biomarker Designs
148(3)
6.2.8 Adaptive Designs
151(10)
6.2.8.1 Two-Stage Designs
153(3)
6.2.8.2 Changing Endpoints
156(4)
6.2.8.3 Sample Size Recalculation
160(1)
6.2.9 Dynamic Treatment Regimes
161(3)
6.2.10 Diagnostic Device Trials
164(11)
6.2.10.1 Example
166(3)
6.3 Phase IV
169(2)
References
171(4)
7 Interim Data Monitoring
175(26)
7.1 Data Monitoring Committees/Data Safety Monitoring Boards
175(14)
7.1.1 Membership
176(1)
7.1.2 When Are DMCs Needed?
177(1)
7.1.3 Roles
177(2)
7.1.4 Organization
179(2)
7.1.5 Charter
181(1)
7.1.6 Data Monitoring Plan
182(1)
7.1.7 Meetings
182(1)
7.1.8 Reports
183(3)
7.1.9 Recommendations
186(1)
7.1.10 DMCs of the Future
187(2)
7.2 Interim Monitoring Methods
189(2)
7.2.1 Evaluating Efficacy
189(1)
7.2.2 Evaluating Futility
190(1)
7.3 Limitations and Extensions
191(7)
7.3.1 Predicted Intervals
192(3)
7.3.1.1 Binary Endpoints
192(1)
7.3.1.2 Continuous Endpoints
193(1)
7.3.1.3 Time-to-Event Endpoints
193(1)
7.3.1.4 Example: NARC 009
194(1)
7.3.2 Predicted Interval Plots
195(9)
7.3.2.1 Example
195(2)
7.3.2.2 The Utility of PIs
197(1)
7.4 A Centralized Risk-Based Approach to Monitoring
198(1)
References
198(3)
8 Analysis Considerations
201(60)
8.1 SAP
201(3)
8.2 Other Preparations for Analyses
204(2)
8.2.1 Data Management Preparations for Analyses
204(1)
8.2.2 Clinical Data Interchange Standards Consortium
204(1)
8.2.3 Statistical Programming
204(2)
8.3 General Issues
206(45)
8.3.1 Describe the Data
206(1)
8.3.2 Analysis Sets (ITT versus Per Protocol [ PP])
206(6)
8.3.2.1 The ITT Principle
207(5)
8.3.2.2 Intent-to-Diagnose
212(1)
8.3.3 Baseline Comparisons and Baseline as a Covariate
212(3)
8.3.4 p-Values versus Confidence Intervals
215(2)
8.3.4.1 Poor p-Value Interpretation
215(1)
8.3.4.2 Need for CIs
216(1)
8.3.5 Time Windows, Visit Windows
217(1)
8.3.6 Multiplicity
218(4)
8.3.7 Confounding Effect Modification
222(1)
8.3.8 Stratification
223(1)
8.3.9 Subgroup Analyses
224(2)
8.3.9.1 Subpopulation Treatment Effect Pattern Plot
226(1)
8.3.10 Multicenter Trials
226(2)
8.3.11 Multinational Trials
228(2)
8.3.12 Missing Data
230(5)
8.3.12.1 Preliminary Analyses for Missing Data
230(1)
8.3.12.2 Definitions
231(1)
8.3.12.3 Analyses Methodologies for Missing Data
232(3)
8.3.13 Competing Risks
235(2)
8.3.14 Censoring in Survival Data
237(2)
8.3.14.1 Design and Monitoring
237(1)
8.3.14.2 Preliminary Investigation
238(1)
8.3.14.3 Sensitivity Analyses
238(1)
8.3.14.4 Extreme Sensitivity Analyses
239(1)
8.3.15 Adherence
239(2)
8.3.16 Rescue Medications
241(2)
8.3.17 Treatment Crossover
243(2)
8.3.18 Association # Causation
245(1)
8.3.19 Causation # Determination
245(1)
8.3.20 Diagnostic Trials
246(5)
8.3.20.1 ITD and the Impact of Interpretable Tests
249(1)
8.3.20.2 Example
250(1)
8.4 Report Writing
251(6)
8.4.1 Balanced Interpretation
254(1)
8.4.2 Drug Trials in a Regulatory Setting
254(3)
References
257(4)
9 Analysis of Safety, Benefit: Risk, and Quality of Life
261(40)
9.1 Safety
261(13)
9.1.1 Adverse Events
263(7)
9.1.1.1 Definitions
263(1)
9.1.1.2 Coding
264(1)
9.1.1.3 Spontaneous versus Active Collection
265(1)
9.1.1.4 Targeted AEs
266(1)
9.1.1.5 Analysis Issues
266(4)
9.1.2 Laboratory and Vital Sign Data
270(2)
9.1.2.1 Analysis Issues
270(2)
9.1.3 Safety Analyses Using Observational Data
272(2)
9.2 Benefit: Risk Evaluation
274(17)
9.2.1 Challenges
274(1)
9.2.2 Measurement
274(2)
9.2.3 Summarization
276(1)
9.2.4 Assessment
277(1)
9.2.5 Combining Separate Marginal Analyses
278(6)
9.2.5.1 One Dimension: Within-Intervention Measures
278(1)
9.2.5.2 One Dimension: Comparative Measures
279(3)
9.2.5.3 Multidimensional Approaches
282(2)
9.2.6 Within-Patient Analyses
284(5)
9.2.6.1 Linear Combinations
285(1)
9.2.6.2 Composite Event-Time Endpoints
286(1)
9.2.6.3 Ordinal Data
286(1)
9.2.6.4 Scatterplot Methods for Continuous Data
287(2)
9.2.6.5 Adjudication Committee (AC) Approach
289(1)
9.2.7 Tailored Medicine
289(2)
9.3 Quality of Life
291(5)
9.3.1 QoL Instruments
292(1)
9.3.2 Issues in Design and Analyses
293(1)
9.3.3 Patient Preferences
294(2)
References
296(5)
10 Publishing Trial Results
301(20)
10.1 Guidelines for Reporting Clinical Trial Results
304(8)
10.1.1 The CONSORT Statement
304(1)
10.1.2 Reporting of Harms Data
304(5)
10.1.3 The TREND Statement
309(1)
10.1.4 The STARD Statement
309(3)
10.2 Reporting the Results of Subgroup Analyses
312(2)
10.3 Reporting Benefits and Risks
314(3)
10.4 Reporting NI Trials
317(1)
10.5 Reporting Adaptive Designs
317(1)
10.6 Reporting Bayesian Designs
317(2)
References
319(2)
Appendix: Excerpts from the Lipitor® Drug Label 321(8)
Index 329
Dr. Scott Evans teaches clinical trials at Harvard University, where he is the director of the Statistical and Data Management Center for the Antibacterial Resistance Leadership Group, an NIH-funded clinical trials network. He serves on a U.S. FDA Advisory Committee and several data monitoring committees for industry and NIH-sponsored clinical trials. He has been a recipient of the Mosteller Statistician of the Year Award and is a fellow of the American Statistical Association. Dr. Evans is a visiting professor at the Department of Medical Statistics at Osaka University and serves as the executive editor for CHANCE and the editor-in-chief of Statistical Communications in Infectious Diseases.

Dr. Naitee Ting has close to 30 years of experience in the pharmaceutical industry and currently works at Boehringer Ingelheim. He has also taught courses on clinical trials in the Department of Statistics at the University of Connecticut, University of Rhode Island, and Department of Biostatistics at Columbia University. He is a fellow of the American Statistical Association.
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