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E-raamat: Design and Analysis of Cross-Over Trials

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(Novartis Pharma AG, Basel, Switzerland), (GSK Professor of Biostatistics, London School of Hygiene and Tropical Medicine, UK)
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Design and Analysis of Cross-Over TrialsSuurem pilt
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"Since the publication of the second edition, there has been significant research and development in cross-over trials, which continue to be an important design technique in the pharmaceutical industry. This new edition has been updated with recent references and new material on analysis of small trials, analysis of baseline measurements, bioequivalence, cross-over trials in early phase drug development, and adaptive cross-over trials. The book includes numerous real examples, including some new data sets to illustrate the methods described, with SAS code available for their implementation"--

Jones and Kenward present students, academics, and researchers with the third edition of their text, dedicated to an understanding of a comparative trait known as the cross-over trial, through which patients involved in a study received different sequences of treatments. New for the third edition, the text includes seven new chapters devoted to case studies, coverage of the R package Crossover, updates related to the use of period baselines and the analysis of very small trials, and a variety of other features. Byron Jones is employed by Novartis Pharma AG, Switzerland. Michael G. Kenward is a faculty member of London School of Hygiene and Tropical Medicine, UK. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)

Design and Analysis of Cross-Over Trials is concerned with a specific kind of comparative trial known as the cross-over trial, in which subjects receive different sequences of treatments. Such trials are widely used in clinical and medical research, and in other diverse areas such as veterinary science, psychology, sports science, and agriculture.

The first edition of this book was the first to be wholly devoted to the subject. The second edition was revised to mirror growth and development in areas where the design remained in widespread use and new areas where it had grown in importance. This newThird Edition:

  • Contains seven new chapters written in the form of short case studies that address re-estimating sample size when testing for average bioequivalence, fitting a nonlinear dose response function, estimating a dose to take forward from phase two to phase three, establishing proof of concept, and recalculating the sample size using conditional power
  • Employs the R package Crossover, specially created to accompany the book and provide a graphical user interface for locating designs in a large catalog and for searching for new designs
  • Includes updates regarding the use of period baselines and the analysis of data from very small trials
  • Reflects the availability of new procedures in SAS, particularly proc glimmix
  • Presents the SAS procedure proc mcmc as an alternative to WinBUGS for Bayesian analysis

Complete with real data and downloadable SAS code, Design and Analysis of Cross-Over Trials, Third Editionprovides a practical understanding of the latest methods along with the necessary tools for implementation.

Arvustused

"Jones and Kenward added several valuable case studies to the third edition of their book. The case studies illustrate elegantly the applications of recent innovations in statistical methodologies to cross-over trials. The new edition is an excellent reference for scientists who want to understand cross-over trials or are interested in learning how statistical advancements in the last decade could be used to expand the versatility of cross-over trials." Christy Chuang-Stein, Ph.D., Vice President, Head of Statistical Research and Consulting Center, Pfizer Inc.

"As in the previous two editions, this edition offers a comprehensive coverage on the design and analysis of cross-over trials. With several major noteworthy updates, it will assist statisticians to conveniently tackle practical issues that arise in a cross-over trial The most substantial update is the addition of seven new chapters (Chapters 814) in the form of short case studies. These real-world examples cover a wide range of issues and solutions above and beyond what is commonly encountered in a cross-over trial and significantly broaden the bookthe third edition of Design and Analysis of Cross-Over Trials remains an outstanding reference for statisticians who work on cross-over trials, whether occasionally or frequently." Haiying Chen, Wake Forest School of Medicine, in Journal of the American Statistical Association, Volume 111, 2016

"Jones and Kenward present students, academics, and researchers with the third edition of their text, dedicated to an understanding of a comparative trait known as the cross-over trial, through which patients involved in a study received different sequences of treatments. New for the third edition, the text includes seven new chapters devoted to case studies, coverage of the R package Crossover, updates related to the use of period baselines and the analysis of very small trials, and a variety of other features." Ringgold, Inc. Book News, February 2015

Praise for the Second Edition: "In the second edition, updated from the original published in 1989, the authors have added discussions of new, more comprehensive (downloadable) datasets and some additional topics. ... Substantially updated with more than 130 new references, the book has been thoroughly modernized to reflect new developments in this area. Among the new material added to the book is a chapter on bioequivalence and a discussion of new methods for longitudinal and categorical data. This book continues to be a recommended choice as a valuable reference for clinical statisticians and those who study medical trials where treatments through cross-over design are a feasible approach. For those who already own the first edition, updating to the second will help keep you current on recent developments in this area." Journal of the American Statistics Association

List of Figures xv
List of Tables xvii
Preface to the Third Edition xxv
1 Introduction 1(10)
1.1 What is a cross-over trial?
1(1)
1.2 With which sort of cross-over trial are we concerned?
2(1)
1.3 Why do cross-over trials need special consideration?
3(2)
1.4 A brief history
5(2)
1.5 Notation, models and analysis
7(2)
1.6 Aims of this book
9(1)
1.7 Structure of the book
10(1)
2 The 2 x 2 cross-over trial 11(94)
2.1 Introduction
11(3)
2.2 Plotting the data
14(4)
2.3 Analysis using t-tests
18(9)
2.4 Sample size calculations
27(5)
2.5 Analysis of variance
32(5)
2.6 Aliasing of effects
37(2)
2.7 Consequences of preliminary testing
39(5)
2.8 Analyzing the residuals
44(2)
2.9 A Bayesian analysis of the 2 x 2 trial
46(8)
2.9.1 Bayes using approximations
46(5)
2.9.2 Bayes using Gibbs sampling
51(3)
2.10 Use of baseline measurements
54(8)
2.11 Use of covariates
62(6)
2.12 Nonparametric analysis
68(28)
2.12.1 Testing λ1 = λ2
70(4)
2.12.2 Testing τ1 = τ2, given that λ1 = λ2
74(1)
2.12.3 Testing π1 = π2, given that λ1 = λ2
75(1)
2.12.4 Obtaining the exact version of the Wilcoxon ranksum test using tables
75(1)
2.12.5 Point estimate and confidence interval for δ = τ1 - τ2
76(2)
2.12.6 A more general approach to nonparametric testing
78(5)
2.12.7 Nonparametric analysis of ordinal data
83(2)
2.12.8 Analysis of a multicenter trial
85(4)
2.12.9 Tests based on nonparametric measures of association
89(7)
2.13 Binary data
96(9)
2.13.1 Introduction
96(2)
2.13.2 McNemar's test
98(1)
2.13.3 The Mainland—Gail test
99(1)
2.13.4 Fisher's exact version of the Mainland—Gait test
100(2)
2.13.5 Prescott's test
102(3)
3 Higher-order designs for two treatments 105(30)
3.1 Introduction
105(1)
3.2 "Optimal" designs
106(1)
3.3 Balaam's design for two treatments
107(3)
3.4 Effect of preliminary testing in Balaam's design
110(3)
3.5 Three-period designs with two sequences
113(4)
3.6 Three-period designs with four sequences
117(4)
3.7 A three-period six-sequence design
121(1)
3.8 Which three-period design to use?
122(2)
3.9 Four-period designs with two sequences
124(1)
3.10 Four-period designs with four sequences
125(2)
3.11 Four-period designs with six sequences
127(2)
3.12 Which four-period design to use?
129(1)
3.13 Which two-treatment design to use?
130(5)
4 Designing cross-over trials 135(52)
4.1 Introduction
135(2)
4.2 Variance-balanced designs
137(21)
4.2.1 Designs with p = t
138(9)
4.2.2 Designs with p < t
147(5)
4.2.3 Designs with p > t
152(2)
4.2.4 Designs with many periods
154(33)
4.2.4.1 Quenouille, Berenblut and Patterson designs
154(2)
4.2.4.2 Federer and Atkinson's designs
156(2)
4.3 Optimality results for cross-over designs
158(3)
4.4 Which variance-balanced design to use?
161(2)
4.5 Partially balanced designs
163(6)
4.6 Comparing test treatments to a control
169(1)
4.7 Factorial treatment combinations
170(5)
4.8 Extending the simple model for carry-over effects
175(2)
4.9 Computer search algorithms
177(10)
5 Analysis of continuous data 187(94)
5.1 Introduction
187(1)
5.1.1 Example 5.1: INNOVO trial: dose—response study
187(1)
5.2 Fixed subject effects model
188(5)
5.2.1 Ignoring the baseline measurements
188(4)
5.2.2 Adjusting for carry-over effects
192(1)
5.3 Random subject effects model
193(11)
5.3.1 Random subject effects
193(2)
5.3.2 Recovery of between-subject information
195(4)
5.3.2.1 Example 5.2
196(3)
5.3.3 Small sample inference with random effects
199(3)
5.3.4 Missing values
202(2)
5.4 Use of baseline measurements
204(18)
5.4.1 Introduction and examples
204(3)
5.4.2 Notation and basic results
207(4)
5.4.3 Pre-randomization covariates
211(1)
5.4.4 Period-dependent baseline covariates
212(8)
5.4.4.1 What we mean by a baseline
212(1)
5.4.4.2 Change from baseline
212(4)
5.4.4.3 Baselines as covariates
216(4)
5.4.5 Baselines as response variables
220(1)
5.4.6 Incomplete data
221(1)
5.5 Analyses for higher-order two-treatment designs
222(9)
5.5.1 Analysis for Balaam's design
222(10)
5.5.1.1 Example 5.5: Amantadine in Parkinsonism
222(9)
5.6 General linear mixed model
231(1)
5.7 Analysis of repeated measurements within periods
232(11)
5.7.1 Example 5.7: Insulin mixtures
233(10)
5.7.1.1 Example 5.6 continued
240(3)
5.8 Cross-over data as repeated measurements
243(20)
5.8.1 Allowing more general covariance structures
243(2)
5.8.2 Robust analyses for two-treatment designs
245(8)
5.8.2.1 Single dual pair designs
245(2)
5.8.2.2 Multiple dual pair designs
247(6)
5.8.3 Higher-order designs
253(10)
5.8.3.1 Example 5.8
253(1)
5.8.3.2 Using an unstructured covariance matrix
254(3)
5.8.3.3 Estimating equations and the empirical/sandwich estimate of error
257(3)
5.8.3.4 Box and modified Box procedures
260(3)
5.8.3.5 Permutation test
263(1)
5.9 Case study: an analysis of a trial with many periods
263(18)
5.9.1 Example 5.9: McNulty's experiment
263(2)
5.9.2 McNulty's analysis
265(1)
5.9.3 Fixed effects analysis
266(7)
5.9.4 Random subject effects and covariance structure
273(1)
5.9.5 Modeling the period effects
274(7)
6 Analysis of discrete data 281(38)
6.1 Introduction
281(4)
6.1.1 Modeling dependent categorical data
281(1)
6.1.2 Types of model
282(3)
6.1.2.1 Example 6.1
282(1)
6.1.2.2 Marginal models
283(1)
6.1.2.3 Subject-specific models
284(1)
6.2 Binary data: subject effect models
285(17)
6.2.1 Dealing with the subject effects
285(1)
6.2.2 Conditional likelihood
286(16)
6.2.2.1 Mainland—Gart test
286(1)
6.2.2.2 Mainland—Gart test in a logistic regression framework
287(1)
6.2.2.3 Small sample issues
288(2)
6.2.2.4 Conditional logistic regression
290(3)
6.2.2.5 Random subject effects
293(7)
6.2.2.6 Higher-order designs
300(2)
6.3 Binary data: marginal models
302(5)
6.3.1 Marginal model
302(5)
6.4 Categorical data
307(8)
6.4.1 Example 6.2: Trial on patients with primary dysmenorrhea
307(1)
6.4.2 Types of model for categorical outcomes
307(2)
6.4.3 Subject effects models
309(2)
6.4.3.1 Proportional odds model
309(1)
6.4.3.2 Generalized logit model
310(1)
6.4.4 Marginal models
311(4)
6.4.4.1 Proportional odds model
311(1)
6.4.4.2 Partial proportional odds model
312(3)
6.5 Further topics
315(4)
6.5.1 Count data
315(1)
6.5.2 Time to event data
316(1)
6.5.3 Issues associated with scale
317(2)
7 Bioequivalence trials 319(12)
7.1 What is bioequivalence?
319(2)
7.2 Testing for average bioequivalence
321(10)
8 Case study: Phase I dose—response noninferiority trial 331(12)
8.1 Introduction
331(1)
8.2 Model for dose response
332(4)
8.3 Testing for noninferiority
336(1)
8.4 Choosing doses for the fifth period
336(3)
8.5 Analysis of the design post-interim
339(4)
9 Case study: Choosing a dose—response model 343(8)
9.1 Introduction
343(1)
9.2 Analysis of variance
344(2)
9.3 Dose—response modeling
346(5)
10 Case study: Conditional power 351(6)
10.1 Introduction
351(1)
10.2 Variance spending approach
351(2)
10.3 Interim analysis of sleep trial
353(4)
11 Case study: Proof of concept trial with sample size re-estimation 357(8)
11.1 Introduction
357(1)
11.2 Calculating the sample size
358(1)
11.3 Interim analysis
359(3)
11.4 Data analysis
362(3)
12 Case study: Blinded sample size re-estimation in a bioequivalence study 365(6)
12.1 Introduction
365(1)
12.2 Blinded sample size re-estimation (BSSR)
365(3)
12.3 Example
368(3)
13 Case study: Unblinded sample size re-estimation in a bioequivalence study that has a group sequential design 371(6)
13.1 Introduction
371(1)
13.2 Sample size re-estimation in a group sequential design
372(3)
13.3 Modification of sample size re-estimation in a group sequential design
375(2)
14 Case study: Various methods for an unblinded sample size re-estimation in a bioequivalence study 377(4)
14.1 Introduction
377(2)
14.1.1 Methods
377(2)
14.2 Example
379(2)
Appendix A Least squares estimation 381(4)
A.0.1 Case 1
381(2)
A.0.2 Case 2
383(1)
A.0.3 Case 3
383(2)
Bibliography 385(20)
Index 405
Byron Jones is a senior biometrical fellow and executive director in the Statistical Methodology Group at Novartis Pharmaceuticals. Previously he was a senior statistical consultant/senior director at Pfizer and a senior director and UK head of the Research Statistics Unit at GlaxoSmithKline. In addition to 14 years of experience in the pharmaceutical industry, he has 25 years of experience in academia, ultimately holding the position of professor of medical statistics at De Montfort University. Currently he is an honorary professor at the London School of Hygiene and Tropical Medicine, visiting professor at University College London and at the University of Leicester, and a visiting professorial fellow at Queen Mary, University of London.

Michael G. Kenward is GlaxoSmithKline professor of biostatistics at the London School of Hygiene and Tropical Medicine. Previously he held positions at the Universities of Kent and Reading in the UK, and at research institutes in the UK, Iceland, and Finland. He has acted as a pharmaceutical industry consultant in biostatistics for more than 25 years. His research interests include the analysis of longitudinal data and cross-over trials, and modeling in biostatistics, with a particular interest in the problem of missing data. He has co-authored three textbooks and is well known for his 1994 Royal Statistical Society read paper on missing data.
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