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E-raamat: Group Sequential and Confirmatory Adaptive Designs in Clinical Trials

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Group Sequential and Confirmatory Adaptive Designs in Clinical TrialsSuurem pilt
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This book provides an up-to-date review of the general principles of and techniques for confirmatory adaptive designs. Confirmatory adaptive designs are a generalization of group sequential designs. With these designs, interim analyses are performed in order to stop the trial prematurely under control of the Type I error rate. In adaptive designs, it is also permissible to perform a data-driven change of relevant aspects of the study design at interim stages. This includes, for example, a sample-size reassessment, a treatment-arm selection or a selection of a pre-specified sub-population.Essentially, this adaptive methodology was introduced in the 1990s. Since then, it has become popular and the object of intense discussion and still represents a rapidly growing field of statistical research. This book describes adaptive design methodology at an elementary level, while also considering designing and planning issues as well as methods for analyzing an adaptively planned trial. Th

is includes estimation methods and methods for the determination of an overall p-value. Part I of the book provides the group sequential methods that are necessary for understanding and applying the adaptive design methodology supplied in Parts II and III of the book. The book contains many examples that illustrate use of the methods for practical application.The book is primarily written for applied statisticians from academia and industry who are interested in confirmatory adaptive designs. It is assumed that readers are familiar with the basic principles of descriptive statistics, parameter estimation and statistical testing. This book will also be suitable for an advanced statistical course for applied statisticians or clinicians with a sound statistical background.

Part I Group Sequential Designs.- Repeated Significance Tests: Procedures with Equally Sized Stages.- Procedures with Unequally Sized Stages.- Confidence Intervals, p -Values, and Point Estimation.- Applications.- Part II Adaptive Confirmatory Designs with a Single Hypothesis: Adaptive Group Sequential Tests.- Decision Tools for Adaptive Designs.- Estimation and p -Values for Two-stage Adaptive Designs.- Adaptive Designs with Survival Data.- Part III Adaptive Designs with Multiple Hypotheses: Multiple Testing in Adaptive Designs.- Applications and Case Studies.- Appendix - Software for Adaptive Designs.- Index.

Arvustused

This is an up-to-date review of the general principles of, and techniques for, confirmatory adaptive designs. Chapters are well illustrated with tables and figures for better understanding. This is a valuable resource for clinicians and applied statisticians with a sound statistical background who are interested in confirmatory adaptive designs. (Pooja Sethi, Doody's Book Reviews, October, 2016)

Part I Group Sequential Designs
1 Repeated Significance Tests
3(22)
1.1 Introduction
3(4)
1.2 Basics
7(8)
1.3 Power and Average Sample Size
15(5)
1.4 The Recursive Integration Formula
20(5)
2 Procedures with Equally Sized Stages
25(38)
2.1 Classical Designs
26(17)
2.1.1 Definition
26(4)
2.1.2 Power and Average Sample Size
30(6)
2.1.3 Wang and Tsiatis Power Family
36(6)
2.1.4 Other Designs
42(1)
2.2 Symmetric Designs
43(7)
2.3 One-Sided Designs
50(12)
2.4 A Note on Two-Sided Designs
62(1)
3 Procedures with Unequally Sized Stages
63(20)
3.1 Effect of Using Boundaries for Equally Sized Stages
64(5)
3.2 Sample Sizes Fixed in Advance
69(5)
3.3 The α-Spending Function Approach
74(9)
4 Confidence Intervals, p-Values, and Point Estimation
83(18)
4.1 Confidence Intervals and p-Values
84(11)
4.1.1 Sample Space Orderings
84(7)
4.1.2 Monitoring a Trial
91(4)
4.2 Point Estimation
95(6)
5 Applications
101(32)
5.1 Normal Response
102(8)
5.2 Binary Response
110(12)
5.2.1 Testing a Single Rate
110(6)
5.2.2 Parallel Group Design
116(6)
5.3 Survival Data
122(11)
Part II Confirmatory Adaptive Designs with a Single Hypothesis
6 Adaptive Group Sequential Tests
133(38)
6.1 Basic Principle and Assumptions
134(1)
6.2 Combination Tests
135(16)
6.2.1 General Methodology
135(2)
6.2.2 Fisher's Product Test
137(4)
6.2.3 Weighted Fisher's Product Test
141(2)
6.2.4 Inverse Normal Combination Test
143(3)
6.2.5 Sample Size Adaptations in Group Sequential Designs
146(5)
6.3 Conditional Error Function Approach
151(13)
6.3.1 Proschan and Hunsberger's Method
151(2)
6.3.2 Relationship Between Conditional Error Functions and Combination Tests
153(2)
6.3.3 The CRP Principle
155(4)
6.3.4 Type I Error Maximization Method
159(5)
6.4 Two-Sided Adaptive Tests
164(3)
6.5 The Multi-Stage Case
167(4)
7 Decision Tools for Adaptive Designs
171(18)
7.1 Conditional Power
171(4)
7.1.1 The Conditional Power of Combination Tests
172(1)
7.1.2 Conditional Power with Fisher's Product Test and Inverse Normal Method
173(2)
7.2 Futility Stopping Based on Conditional Power
175(1)
7.3 Sample Size Modification Based on Conditional Power
176(2)
7.4 On the Parameter Value Used in the Conditional Power and Sample Size Calculation
178(9)
7.4.1 Using a Minimal Clinically Relevant Effect Size
178(1)
7.4.2 Using the Interim Estimate
179(1)
7.4.3 Using the Bayesian Posterior Mean
180(1)
7.4.4 Bayesian Predictive Power
181(4)
7.4.5 Discussion
185(2)
7.5 A Case Study
187(2)
8 Estimation and p-Values for Two-Stage Adaptive Designs
189(32)
8.1 Overall p-Values for Adaptive Two-Stage Designs
189(11)
8.1.1 Stage-Wise Ordering and Related p-Values
190(3)
8.1.2 Repeated p-Values for Two-Stage Combination Tests
193(5)
8.1.3 Numerical Examples
198(2)
8.2 Adaptive Confidence Intervals
200(9)
8.2.1 Exact Confidence Bounds for Combination Tests
201(3)
8.2.2 Repeated Confidence Bounds for Combination Tests
204(1)
8.2.3 Two-Sided Confidence Intervals
205(1)
8.2.4 Numerical Examples
206(3)
8.3 Point Estimation in Adaptive Designs
209(9)
8.3.1 Maximum Likelihood Estimate
209(2)
8.3.2 Fixed Weighted ML-Estimate
211(1)
8.3.3 Median Unbiased Point Estimation
212(1)
8.3.4 Adaptively Weighted ML-Estimate
213(1)
8.3.5 Comparison of Point Estimates
214(4)
8.4 Extensions
218(3)
9 Adaptive Designs with Survival Data
221(10)
9.1 Combination of p- Values from Log-Rank Tests
221(3)
9.1.1 Use of Independent Increments
222(1)
9.1.2 Applying Left Truncation at the Second Stage
223(1)
9.2 Restriction of the Information Used for the Adaptations
224(2)
9.3 Sample Size Reassessment Rules
226(1)
9.4 Estimation of the Hazard Ratio
227(4)
Part III Adaptive Designs with Multiple Hypotheses
10 Multiple Testing in Adaptive Designs
231(10)
10.1 Sources of Multiplicity
232(1)
10.2 The Closure Principle
233(2)
10.3 Closed Testing in Adaptive Designs
235(6)
11 Applications and Case Studies
241(36)
11.1 Adaptive Treatment Selection in Multi-Arm Clinical Trials
241(20)
11.1.1 Test Procedure
242(3)
11.1.2 Intersection Tests
245(2)
11.1.3 Overall p-Values and Confidence Intervals
247(2)
11.1.4 Numerical Example
249(3)
11.1.5 Adaptive Dunnett Test
252(6)
11.1.6 Other Endpoints
258(1)
11.1.7 Case Studies
259(2)
11.2 Adaptive Enrichment Designs
261(10)
11.2.1 Test Procedure
262(2)
11.2.2 Intersection Tests
264(2)
11.2.3 Effect Specification
266(1)
11.2.4 Overall p-Values and Confidence Intervals
266(1)
11.2.5 Other Endpoints
267(1)
11.2.6 A Clinical Trial Example
268(3)
11.3 Other Types of Adaptations
271(3)
11.3.1 A Case Study with Adaptive Multiple Endpoint Selection
272(2)
11.4 Regulatory and Logistical Issues
274(3)
Appendix: Software for Adaptive Designs 277(4)
References 281(16)
Index 297
Gernot Wassmer is an Adjunct Professor of Biostatistics at the Institute of Medical Statistics, University of Cologne, Germany. He received his PhD from the University of Munich, Germany in 1993, after which he was a Research Fellow at Munichs Institute of Medical Statistics, at the Institute for Epidemiology, GSF Neuherberg, and at the Institute of Medical Statistics, University of Cologne. His major research interest is in the field of statistical procedures for group sequential and adaptive plans in clinical trials. He has been a member of independent data monitoring committees for international, multi-center trials in various therapeutic fields and also serves as a consultant for the pharmaceutical industry.

 





Werner Brannath is a Professor of Biostatistics at the Faculty of Mathematics and Informatics, University of Bremen, where he is also head of the biometry group at the Competence Center for Clinical Trials. He has extensiveexperience in the planning and analysis of clinical trials and has been a member of several independent data monitoring committees. His main research interests include adaptive and group sequential designs, as well as multiple testing. 
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