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E-raamat: Randomised Response-Adaptive Designs in Clinical Trials

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  • Formaat: 339 pages
  • Ilmumisaeg: 26-Dec-2013
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781584886945
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Randomised Response-Adaptive Designs in Clinical TrialsSuurem pilt
  • Formaat: 339 pages
  • Ilmumisaeg: 26-Dec-2013
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781584886945
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Statisticians Atkinson and Biswas consider methods of randomized allocations of treatments to patients in sequential clinical trials. The ethical urge to give as many patients as possible the most effective treatment, they say, must be offset by statistical considerations of efficient estimation and powerful statistical tests. Among their topics are controversies and progress in adaptive design, randomized balanced sequential treatment allocation, response-adaptive designs for binary responses, optimum biased-coin designs with covariates, and optimum response-adaptive designs with constraints. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

Randomised Response-Adaptive Designs in Clinical Trials presents methods for the randomised allocation of treatments to patients in sequential clinical trials. Emphasizing the practical application of clinical trial designs, the book is designed for medical and applied statisticians, clinicians, and statisticians in training.

After introducing clinical trials in drug development, the authors assess a simple adaptive design for binary responses without covariates. They discuss randomisation and covariate balance in normally distributed responses and cover many important response-adaptive designs for binary responses. The book then develops response-adaptive designs for continuous and longitudinal responses, optimum designs with covariates, and response-adaptive designs with covariates. It also covers response-adaptive designs that are derived by optimising an objective function subject to constraints on the variance of estimated parametric functions. The concluding chapter explores future directions in the development of adaptive designs.

Arvustused

"... an excellent textbook on this important topic. In general, the book is well written, easy to navigate, and definitely consistent with the high quality of other books in Monographs on Statistics and Applied Probability series by CRC Press. ... a well-structured and clearly presented textbook on randomized response-adaptive designs. ... Because the book offers a well-balanced mix of practical applications and theoretical results, a wide range of readers, from graduate students to applied statisticians with solid mathematical background, will find the book useful. Readers may find the authors' emphasis on the use of simulation methods to compare methods particularly useful." -The American Statistician, August 2015 "Atkinson and Biswas address [ the] questions throughout in a thorough and interesting manner via a logical structure that makes navigating the text a simple and intuitive exercise. ... ideal for anyone looking to learn more about the growing field of response adaptive designs. An easy read, it is well written from the off set, logically advancing the mathematical complexity as the chapters proceed, making it useful for those new to the field of clinical trial design and seasoned trial statisticians alike. Through its use of numerous examples, it clearly achieves its stated aim; to elucidate the practical usefulness of this class of designs. Given their increasing popularity, it may well be one book you should consider adding to your collection sooner rather than later." -ISCB News, 59, June 2015 "This book is clearly written and well-structured for a graduate course as well as consulting statisticians. ... this book is particularly useful for the development of orphan drugs." -Biometrics, March 2015 "... the book covers a very broad range of response-adaptive designs and related issues. ... the book is comprehensively written and gives many exemplary applications from clinical practice. At the same time, the book is mathematically sound and also provides the underlying mathematical formulas and derivations. For these reasons, the book offers important content for applied statisticians but also for more theoretically interested mathematicians." -Biometrical Journal, 2014

Preface xv
0 Introduction: Stories and Data
1(14)
0.1 Scope and Limits
1(2)
0.2 Two-Treatment Trials with a Binary Response
3(1)
0.3 Equal Randomisation
4(1)
0.4 Adaptive Allocation
4(1)
0.5 Urn Model
5(1)
0.6 Some Motivating Clinical Trials
5(10)
0.6.1 AZT Data and Story
6(1)
0.6.2 Michigan ECMO Trial
7(1)
0.6.3 Fluoxetine Trial and Data
8(1)
0.6.4 Crystalloid Preload Trial
9(2)
0.6.5 PEMF Trial
11(1)
0.6.6 Boston ECMO Trial
11(1)
0.6.7 Pregabalin Trial
12(1)
0.6.8 Erosive Esophagitis Trial
13(1)
0.6.9 Appendiceal Mass Trial
14(1)
1 Adaptive Design: Controversies and Progress
15(12)
1.1 Why Adaptive?
15(1)
1.2 How Adaptive?
16(5)
1.2.1 Forcing a Prefixed Allocation: FPA Rule
16(3)
1.2.2 Further Considerations: Alternatives to the FPA Rule
19(2)
1.3 Criticism
21(2)
1.4 What Next?
23(4)
2 Randomised Balanced Sequential Treatment Allocation
27(38)
2.1 Introduction
27(1)
2.2 Balance with Two Treatments
28(14)
2.2.1 Balance and Randomisation
28(1)
2.2.2 Four Design Strategies
29(1)
2.2.3 Properties of Designs
30(1)
Selection Bias
31(2)
Balance and Loss
33(2)
2.2.4 Numerical Comparisons of Designs
35(7)
2.3 Designs with Three or More Treatments
42(5)
2.3.1 Four Design Strategies
42(1)
2.3.2 Properties of Designs
43(2)
2.3.3 Numerical Comparisons of Designs
45(2)
2.4 Designs with Covariates
47(6)
2.4.1 Models
47(1)
2.4.2 Four Further Design Strategies
48(2)
2.4.3 Minimisation and Covariate Balance: Numerical
50(3)
2.5 The Distribution of Loss and of Bias
53(5)
2.6 Heteroscedastic Models
58(2)
2.6.1 Variances and Parameter Estimates
58(1)
2.6.2 Designs with Covariates
59(1)
2.7 More about Biased-Coin Designs
60(3)
2.8 Further Reading
63(2)
3 Response-Adaptive Designs for Binary Responses
65(52)
3.1 Introduction
65(1)
3.2 Urn Designs
65(1)
3.3 Play-the-Winner Rule
66(8)
3.3.1 What Is a PW Rule?
66(1)
3.3.2 Statistical Interpretation of the PW Rule
67(1)
3.3.3 Performance of the PW Rule
68(3)
3.3.4 Real Life Applications of the PW Rule
71(1)
3.3.5 Further Points about the PW Rule
72(1)
3.3.6 Should We Play the Winner?
72(1)
3.3.7 Cyclic Play-the-Winner Rule
73(1)
3.4 Randomised Play-the-Winner Rule
74(8)
3.4.1 Background
74(1)
3.4.2 What Is a Randomised Play-the-Winner Rule?
74(1)
3.4.3 Real-Life Applications
75(1)
3.4.4 Statistical Considerations
76(3)
3.4.5 A General Form of RPW Rule
79(2)
3.4.6 Inference Following RPW Allocation
81(1)
3.5 Generalised Polya Urn
82(4)
3.5.1 Design
82(1)
3.5.2 Statistical Analysis of GPU
83(3)
3.5.3 Generalisations
86(1)
3.6 Success-Driven Design (SDD)
86(2)
3.7 Failure-Driven Design (FDD)
88(2)
3.8 Birth and Death Urn (BDU)
90(1)
3.9 Birth and Death Urn with Immigration
90(1)
3.9.1 Why Immigration?
90(1)
3.9.2 Immigration
90(1)
3.10 Drop-the-Loser Rule
91(3)
3.10.1 Death Process
91(1)
3.10.2 Description of the Drop-the-Loser (DL) Rule
91(3)
3.11 Odds Ratio-Based Adaptive Designs
94(10)
3.11.1 Odds Ratio
94(2)
3.11.2 Design
96(1)
3.11.3 Results
96(3)
3.11.4 More than Two Treatments
99(5)
3.12 Delayed Response from the RPW Rule
104(4)
3.12.1 How to Incorporate Delayed Responses
104(1)
3.12.2 Nonrandom Denominator
105(2)
3.12.3 A Note on the Delayed Response Indicator Variable
107(1)
3.13 Prognostic Factors in Urn Designs
108(4)
3.13.1 RPW with Prognostic Factors
108(3)
3.13.2 Drop-the-Loser with Covariates
111(1)
3.14 Targeting an Allocation Proportion
112(3)
3.14.1 Doubly Adaptive Biased-Coin Designs
112(1)
3.14.2 Sequential Estimation-Adjusted Urn Design (SEU)
113(1)
3.14.3 Efficient Randomised Adaptive Design (ERADE)
114(1)
3.15 Adaptive Designs for Categorical Responses
115(1)
3.16 Comparisons and Recommendations
115(2)
4 Response-Adaptive Designs for Continuous Responses
117(24)
4.1 Motivation
117(1)
4.2 Some Trials with Continuous Responses
117(1)
4.3 Doubly Adaptive Biased-Coin Designs (DBCD)
118(1)
4.4 Nonparametric Designs
118(3)
4.4.1 Treatment Effect Mapping
118(1)
4.4.2 Wilcoxon-Mann-Whitney Adaptive Design (WAD)
119(2)
4.5 Adaptive Designs for Survival Data
121(2)
4.6 Link Function-Based Adaptive Design (BB)
123(13)
4.6.1 Design
123(1)
4.6.2 Another Interpretation of the Link Function
124(1)
4.6.3 Prognostic Factors
124(1)
4.6.4 Numerical Illustrations
125(1)
4.6.5 Choice of Design Parameters
126(3)
4.6.6 Unknown σ2
129(1)
4.6.7 Example
130(1)
4.6.8 Using the Prognostic Factors of the Current Patient
131(4)
4.6.9 Cara Design
135(1)
4.7 Multi-Treatment Multivariate Design
136(4)
4.8 DL Rule for Continuous Responses (CDL)
140(1)
5 Response-Adaptive Designs for Longitudinal Responses
141(18)
5.1 Repeated Responses
141(1)
5.2 Binary Longitudinal Responses (SLPW)
142(4)
5.2.1 Design
142(1)
5.2.2 Conditional Probabilities
143(1)
5.2.3 Limiting Allocation Proportion
144(2)
5.2.4 Inference: Further Reading
146(1)
5.3 Design and Analysis for the PEMF Data
146(5)
5.3.1 Situation
146(1)
5.3.2 Design
146(1)
5.3.3 Allocation Probabilities and Proportions
147(3)
5.3.4 PEMF Trial: Results
150(1)
5.4 Longitudinal Categorical Responses
151(1)
5.5 Longitudinal Multivariate Ordinal Responses
152(1)
5.6 Models with Covariates
153(1)
5.6.1 Urn Model
153(1)
5.7 Continuous Longitudinal Responses
153(1)
5.8 Random Number of Responses
154(1)
5.9 Numerical Illustrations
154(5)
5.9.1 Longitudinal Binary Responses
154(1)
5.9.2 Longitudinal Continuous Responses
155(4)
6 Optimum Biased-Coin Designs with Covariates
159(50)
6.1 Modelling and Design
160(4)
6.1.1 A Regression Model
160(1)
6.1.2 D-Optimality
160(2)
6.1.3 Sequential Construction of D-Optimum Designs
162(1)
6.1.4 DA-Optimality
162(1)
6.1.5 Treatment Contrasts and Differences
163(1)
6.1.6 Two Treatments
164(1)
6.2 Biased-Coin DA-Optimum Designs
164(6)
6.2.1 Atkinson's Rule
164(2)
6.2.2 A Bayesian Biased-Coin
166(2)
6.2.3 Nine Allocation Rules
168(2)
6.3 Numerical Comparisons for Two Treatments
170(7)
6.3.1 Five Nuisance Parameters; q = 5
170(5)
6.3.2 Ten Nuisance Parameters; q = 10
175(2)
6.4 Designs for Three Treatments
177(1)
6.5 Distribution of Loss
178(6)
6.5.1 Expected Loss
178(1)
6.5.2 A Chi-Squared Approximation
179(2)
6.5.3 Five Nuisance Parameters
181(1)
6.5.4 Ten Nuisance Parameters
182(1)
6.5.5 Discussion
182(2)
6.6 Skewed Allocations
184(3)
6.6.1 Introduction
184(1)
6.6.2 Efficient Designs
184(1)
6.6.3 Skewed Allocation Rules
185(1)
6.6.4 Skewed Bayesian Biased-Coin Designs
186(1)
6.7 Skewed Allocation -- Numerical
187(5)
6.7.1 Two Treatments
187(3)
6.7.2 Three Treatments
190(2)
6.8 Heteroscedastic Normal Models
192(1)
6.8.1 Models
192(1)
6.8.2 Variances and Efficiencies
192(1)
6.9 Allocation Rules for Heteroscedastic Models
193(5)
6.9.1 Ordering and Skewing
193(2)
6.9.2 Numerical Results
195(3)
6.10 Generalised Linear Models
198(1)
6.11 Binary Data
199(2)
6.12 Allocation Rules for Binomial Models
201(3)
6.12.1 Ordering and Skewing
201(1)
6.12.2 Numerical Results
202(2)
6.13 Gamma Data
204(1)
6.14 Loss, Power, Variability
205(3)
6.15 Further Reading: Skewed Designs
208(1)
7 Optimum Response-Adaptive Designs with Covariates
209(32)
7.1 Introduction
209(1)
7.2 Link Function-Based Adaptive Design
210(18)
7.2.1 Link Function
210(1)
7.2.2 Regularisation
211(1)
7.2.3 Regularisation and Average Properties
212(5)
7.2.4 Individual Trials
217(2)
7.2.5 Bias
219(3)
7.2.6 Two-Treatment Adaptive Designs
222(2)
7.2.7 Adaptive Design for Three Treatments
224(4)
7.3 Adaptive Designs Maximising Utility
228(4)
7.3.1 Designs That Target Allocation Proportions
228(1)
7.3.2 Utility
229(1)
7.3.3 Gain and Allocation Probabilities: Rule G
230(1)
7.3.4 An Operational Rule
231(1)
7.3.5 Asymptotic Properties
231(1)
7.4 Power Comparisons for Four Rules
232(5)
7.5 Redesigning a Trial: Fluoxetine Hydrochloride
237(2)
7.6 Extensions
239(1)
7.7 Further Reading
239(2)
8 Optimal Response-Adaptive Designs with Constraints
241(28)
8.1 Optimal Designs Subject to Constraints
241(1)
8.2 Design of Jennison and Turnbull
242(1)
8.3 RSIHR Design
243(1)
8.4 Maximising Power: Neyman Allocation
244(2)
8.5 Other Designs
246(1)
8.6 BM Design
247(2)
8.7 ZR Design
249(6)
8.7.1 Design
249(1)
8.7.2 Criticism
250(2)
8.7.3 Some Solutions
252(3)
8.8 A General Framework: BBZ Design
255(1)
8.9 Two Normal Populations with Unknown Variances
255(2)
8.10 Two-Sample Nonparametric Design
257(1)
8.11 BM Design for More than Two Treatments
258(4)
8.11.1 Binary Responses
258(2)
8.11.2 Continuous Responses
260(2)
8.12 Optimal Designs with More than One Constraint
262(2)
8.13 Designs for Survival Times
264(1)
8.13.1 BM Design
264(1)
8.13.2 ZR Design
264(1)
8.14 Covariates
265(2)
8.15 Implementation
267(1)
8.16 Adaptive Constraints
267(1)
8.17 Back to
Chapter 7
267(2)
9 Adaptive Design: Further Important Issues
269(22)
9.1 Bayesian Adaptive Designs
269(5)
9.1.1 Rationale
269(1)
9.1.2 Simple Bayesian Adaptive Designs: Illustrations
270(2)
9.1.3 Real-Life Bayesian Adaptive Designs
272(2)
9.1.4 Bayesian Adaptive Design for Continuous Responses
274(1)
9.2 Two-Stage Adaptive Design
274(2)
9.3 Group Sequential Adaptive Design
276(5)
9.3.1 Logistics and Literature
276(1)
9.3.2 A Two-Treatment Design for Continuous Responses
277(1)
9.3.3 A Three-Treatment Design for Binary Responses
278(3)
9.4 Optimal Design for Binary Longitudinal Responses
281(1)
9.5 Inverse Sampling
282(2)
9.6 Robustness in Adaptive Designs
284(1)
9.7 Missing Data in Response-Adaptive Designs
285(3)
9.8 Asymptotic Results for CARA Designs
288(2)
9.9 How to Bridge Theory and Practice
290(1)
A Optimum Design
291(8)
A.1 Optimum Experimental Design
291(4)
A.1.1 Models
291(1)
A.1.2 D-Optimality
291(1)
A.1.3 The Sequential Construction of D-Optimum Designs
292(1)
A.1.4 Treatment Contrasts and Differences
292(2)
A.1.5 Continuous and Exact Designs
294(1)
A.1.6 Literature
295(1)
A.2 A Skewed Bayesian Biased Coin
295(2)
A.3 Asymptotic Properties
297(2)
Bibliography 299(20)
Index 319
Anthony C. Atkinson is an Emeritus Professor of Statistics at the London School of Economics and Political Science.





Atanu Biswas is a professor in the Applied Statistics Unit at the Indian Statistical Institute, Kolkata.
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