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E-raamat: Design and Analysis of Non-Inferiority Trials

(Merck & Company, North Wales, Pennsylvania, USA), , (Silver Springs, Maryland, USA)
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Design and Analysis of Non-Inferiority TrialsSuurem pilt
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The increased use of non-inferiority analysis has been accompanied by a proliferation of research on the design and analysis of non-inferiority studies. Using examples from real clinical trials, Design and Analysis of Non-Inferiority Trials brings together this body of research and confronts the issues involved in the design of a non-inferiority trial. Each chapter begins with a non-technical introduction, making the text easily understood by those without prior knowledge of this type of trial.

Topics covered include:











A variety of issues of non-inferiority trials, including multiple comparisons, missing data, analysis population, the use of safety margins, the internal consistency of non-inferiority inference, the use of surrogate endpoints, trial monitoring, and equivalence trials Specific issues and analysis methods when the data are binary, continuous, and time-to-event The history of non-inferiority trials and the design and conduct considerations for a non-inferiority trial The strength of evidence of an efficacy finding and how to evaluate the effect size of an active control therapy

A comprehensive discussion on the purpose and issues involved with non-inferiority trials, Design and Analysis of Non-inferiority Trials will assist current and future scientists and statisticians on the optimal design of non-inferiority trials and in assessing the quality of non-inferiority comparisons done in practice.

Arvustused

"This important book is relevant because this type of trial is preferred when an approved treatment is available and a conventional placebo-controlled trial would be difficult to justify. These trials are complex and this book goes a long way toward helping the investigator with the issues that arise. This book is comprehensive and includes extensive coverage of every topic. It includes both frequentist and Bayesian approaches as appropriate. Throughout every chapter, topics are fully explained and examples are completely worked out, including statistical formulas and results. a modern and thorough compendium for anyone involved in the design and analysis of noninferiority trials." Mark Bailey, Technometrics, November 2014

" it offers unique perspectives and insights from three renowned experts in clinical trials who work at the U.S. Food and Drug Administration (FDA) and in the pharmaceutical industry. It is refreshing to see FDA and industry perspectives blended thoughtfully, as in this book. Written clearly and concisely, the book is a pleasure to read. Although there are some technical discussions intended for statisticians, most of the book is readily accessible to medical researchers with little statistical training. We recommend the book to anyone interested in NI trialsstatisticians and nonstatisticians alike." Zhiwei Zhang and Lei Nie, Journal of the American Statistical Association, March 2014

"It is a pleasure to see a book completely devoted to the challenging arena of non-inferiority trials. I am very impressed with its depth and breadth, and believe that it will be an important resource for anyone involved in designing non-inferiority trials. The authors weave in many examples, primarily in oncology, as well as a large set of references from the now substantial statistical literature on non-inferiority designs. This book is a must-have resource for those involved in non-inferiority trials for the

Preface xv
1 What Is a Non-Inferiority Trial? 1(14)
1.1 Definition of Non-Inferiority
1(2)
1.2 Reasons for Non-Inferiority Trials
3(4)
1.3 Different Types of Comparisons
7(3)
1.4 A History of Non-Inferiority Trials
10(3)
References
13(2)
2 Non-Inferiority Trial Considerations 15(28)
2.1 Introduction
15(1)
2.2 External Validity and Assay Sensitivity
16(1)
2.3 Critical Steps and Issues
17(13)
2.3.1 Historical Evidence of Sensitivity to Drug Effects
18(1)
2.3.2 Designing a Trial
19(1)
2.3.3 Selecting the Margin
20(7)
2.3.4 Study Conduct and Analysis Populations
27(3)
2.4 Sizing a Study
30(6)
2.5 Example of Anti-Infectives
36(4)
References
40(3)
3 Strength of Evidence and Reproducibility 43(14)
3.1 Introduction
43(1)
3.2 Strength of Evidence
44(6)
3.2.1 Overall Type I Error
44(1)
3.2.2 Bayesian False-Positive Rate
44(3)
3.2.3 Relative Evidence between the Null and Alternative Hypotheses
47(2)
3.2.4 Additional Considerations
49(1)
3.3 Reproducibility
50(6)
3.3.1 Correlation across Identical Non-Inferiority Trials
52(1)
3.3.2 Predicting Future and Hypothetical Outcomes
52(4)
References
56(1)
4 Evaluating the Active Control Effect 57(34)
4.1 Introduction
57(1)
4.2 Active Control Effect
58(16)
4.2.1 Defining the Active Control Effect
58(1)
4.2.2 Modeling the Active Control Effect
58(1)
4.2.3 Extrapolating to the Non-Inferiority Trial
59(3)
4.2.4 Potential Biases and Random Highs
62(12)
4.3 Meta-Analysis Methods
74(13)
4.3.1 Fixed Effects Meta-Analysis
75(1)
4.3.2 Peto's Method
76(1)
4.3.3 Random-Effects Meta-Analysis
77(1)
4.3.4 Sampling Distributions
78(3)
4.3.5 Concerns of Random-Effects Meta-Analyses
81(4)
4.3.6 Adjusting over Effect Modifiers
85(2)
4.4 Bayesian Meta-Analyses
87(2)
References
89(2)
5 Across-Trials Analysis Methods 91(58)
5.1 Introduction
91(1)
5.2 Two Confidence Interval Approaches
92(6)
5.2.1 Hypotheses and Tests
92(6)
5.3 Synthesis Methods
98(22)
5.3.1 Introduction
98(1)
5.3.2 Retention Fraction and Hypotheses
99(3)
5.3.3 Synthesis Frequentist Procedures
102(6)
5.3.3.1 Relative Metrics
102(1)
5.3.3.2 Absolute Metrics
102(6)
5.3.4 Synthesis Methods as Prediction Interval Methods
108(2)
5.3.5 Addressing the Potential for Biocreep
110(1)
5.3.6 Bayesian Synthesis Methods
110(5)
5.3.7 Application
115(2)
5.3.8 Sample Size Determination
117(3)
5.4 Comparing Analysis Methods and Type I Error Rates
120(21)
5.4.1 Introduction
120(1)
5.4.2 Comparison of Methods
121(3)
5.4.3 Asymptotic Results
124(4)
5.4.4 More on Type I Error Rates
128(13)
5.4.4.1 Non-Inferiority Trial Size Depends on Estimation of Active Control Effect
131(1)
5.4.4.2 Incorporating Regression to Mean Bias
132(9)
5.5 A Case in Oncology
141(5)
5.5.1 Applying the Arithmetic Definition of Retention Fraction
142(1)
5.5.2 Applying the Geometric Definition of Retention Fraction
143(1)
5.5.3 Power of Such Procedures
144(2)
References
146(3)
6 Three-Arm Non-Inferiority Trials 149(18)
6.1 Introduction
149(3)
6.2 Comparisons to Concurrent Controls
152(8)
6.2.1 Superiority over Placebo
152(2)
6.2.2 Non-Inferior to Active Control
154(6)
6.3 Bayesian Analyses
160(5)
References
165(2)
7 Multiple Comparisons 167(14)
7.1 Introduction
167(1)
7.2 Comparing Multiple Groups to an Active Control
168(3)
7.2.1 Unordered Treatments: Subset Selection
169(1)
7.2.2 Ordered Treatments: Subset Selection
170(1)
7.2.3 All-or-Nothing Testing
171(1)
7.3 Non-Inferiority on Multiple Endpoints
171(4)
7.3.1 Multiple Endpoints in a Single Family
172(2)
7.3.2 Multiple Endpoints in Multiple Families
174(1)
7.3.3 Further Considerations
175(1)
7.4 Testing for Both Superiority and Non-Inferiority
175(4)
7.4.1 Testing Superiority after Achieving Non-Inferiority
176(2)
7.4.2 Testing Non-Inferiority after Failing Superiority
178(1)
References
179(2)
8 Missing Data and Analysis Sets 181(26)
8.1 Introduction
181(1)
8.2 Missing Data
182(14)
8.2.1 Potential Impact of Missing Data
182(2)
8.2.2 Preventing Missing Data
184(2)
8.2.3 Missing Data Mechanisms
186(1)
8.2.4 Assessing Missing Data Mechanisms
187(2)
8.2.5 Analysis of Data when Some Data Are Missing
189(7)
8.2.5.1 Handling Ignorable Missing Data in Non-Inferiority Analyses
189(4)
8.2.5.2 Handling Non-Ignorable Missing Data
193(3)
8.3 Analysis Sets
196(8)
8.3.1 Different Analysis Populations
197(1)
8.3.2 Influence of Analysis Population on Conclusions
198(5)
8.3.3 Further Considerations
203(1)
References
204(3)
9 Safety Studies 207(12)
9.1 Introduction
207(2)
9.2 Considerations for Safety Study
209(7)
9.2.1 Safety Endpoint Considerations
210(1)
9.2.2 Design Considerations
211(1)
9.2.3 Possible Comparisons
212(8)
9.2.3.1 Ruling Out a Meaningful Risk Increase Compared to Placebo
212(1)
9.2.3.2 Ruling Out a Meaningful Risk Increase Compared to an Active Control
213(2)
9.2.3.3 Indirect Comparison to Placebo
215(1)
9.3 Cardiovascular Risk in Antidiabetic Therapy
216(2)
References
218(1)
10 Additional Topics 219(32)
10.1 Introduction
219(1)
10.2 Interaction Tests
220(6)
10.2.1 Test Procedures
222(2)
10.2.2 Internal Consistency
224(1)
10.2.3 Conclusions and Recommendations
225(1)
10.3 Surrogate Endpoints
226(4)
10.4 Adaptive Designs
230(7)
10.4.1 Group Sequential Designs
231(4)
10.4.2 Changing the Sample Size or the Primary Objective
235(2)
10.5 Equivalence Comparisons
237(10)
10.5.1 Data Scales
238(1)
10.5.2 Two One-Sided Tests Approach
239(1)
10.5.3 Distribution-Based Approaches
240(2)
10.5.4 Lot Consistency
242(5)
References
247(4)
11 Inference on Proportions 251(68)
11.1 Introduction
251(2)
11.2 Fixed Thresholds on Differences
253(20)
11.2.1 Hypotheses and Issues
253(1)
11.2.2 Exact Methods
254(5)
11.2.2.1 Exact Confidence Intervals
257(2)
11.2.3 Asymptotic Methods
259(3)
11.2.4 Comparisons of Confidence Interval Methods
262(6)
11.2.4.1 Inferences on a Single Proportion
263(1)
11.2.4.2 Inferences for a Difference in Proportions
264(4)
11.2.5 Sample Size Determination
268(5)
11.2.5.1 Optimal Randomization Ratio
271(2)
11.3 Fixed Thresholds on Ratios
273(16)
11.3.1 Hypotheses and Issues
273(1)
11.3.2 Exact Methods
273(5)
11.3.2.1 Exact Conditional Non-Inferiority Test
274(4)
11.3.3 Asymptotic Methods
278(1)
11.3.4 Comparisons of Methods
279(4)
11.3.5 Sample-Size Determination
283(6)
11.3.5.1 Optimal Randomization Ratio
286(3)
11.4 Fixed Thresholds on Odds Ratios
289(4)
11.4.1 Hypotheses
289(1)
11.4.2 Exact Methods
290(1)
11.4.3 Asymptotic Methods
291(1)
11.4.4 Sample Size Determination
292(1)
11.5 Bayesian Methods
293(4)
11.6 Stratified and Adjusted Analyses
297(7)
11.6.1 Adjusted Rates
298(2)
11.6.2 Adjusted Estimators
300(4)
11.7 Variable Margins
304(4)
11.8 Matched-Pair Designs
308(6)
11.8.1 Difference in Two Correlated Proportions
309(2)
11.8.2 Ratio of Two Correlated Proportions
311(3)
References
314(5)
12 Inferences on Means and Medians 319(38)
12.1 Introduction
319(1)
12.2 Fixed Thresholds on Differences of Means
320(14)
12.2.1 Hypotheses and Issues
320(1)
12.2.2 Exact and Distribution-Free Methods
321(1)
12.2.3 Normalized Methods
322(4)
12.2.3.1 Test Statistics
323(3)
12.2.4 Bayesian Methods
326(5)
12.2.5 Sample Size Determination
331(3)
12.3 Fixed Thresholds on Ratios of Means
334(8)
12.3.1 Hypotheses and Issues
334(1)
12.3.2 Exact and Distribution-Free Methods
334(1)
12.3.3 Normalized and Asymptotic Methods
335(3)
12.3.3.1 Test Statistics
335(3)
12.3.4 Bayesian Methods
338(2)
12.3.5 Sample Size Determination
340(2)
12.4 Analyses Involving Medians
342(11)
12.4.1 Hypotheses and Issues
343(1)
12.4.2 Nonparametric Methods
344(7)
12.4.3 Asymptotic Methods
351(2)
12.5 Ordinal Data
353(2)
References
355(2)
13 Inference on Time-to-Event Endpoints 357(38)
13.1 Introduction
357(4)
13.2 Censoring
361(2)
13.3 Exponential Distributions
363(5)
13.3.1 Confidence Intervals for a Difference in Means
364(2)
13.3.2 Confidence Intervals for the Hazard Ratio (Ratio of Means)
366(2)
13.4 Nonparametric Inference Based on a Hazard Ratio
368(12)
13.4.1 Event and Sample Size Determination
375(2)
13.4.2 Proportional Hazards Assessment Procedures
377(3)
13.5 Analyses Based on Landmarks and Medians
380(9)
13.5.1 Landmark Analyses
380(4)
13.5.2 Analyses on Medians
384(5)
13.6 Comparisons over Preset Intervals
389(3)
References
392(3)
Appendix: Statistical Concepts 395(38)
A.1 Frequentist Methods
395(15)
A.1.1 p-Values
395(6)
A.1.2 Confidence Intervals
401(3)
A.1.3 Comparing and Contrasting Confidence Intervals and p-Values
404(1)
A.1.4 Analysis Methods
405(5)
A.1.4.1 Exact and Permutation Methods
405(3)
A.1.4.2 Asymptotic Methods
408(2)
A.2 Bayesian Methods
410(9)
A.2.1 Posterior Probabilities and Credible Intervals
410(2)
A.2.2 Prior and Posterior Distributions
412(3)
A.2.3 Statistical Inference
415(4)
A.3 Comparison of Methods
419(8)
A.3.1 Relationship between Frequentist and Bayesian Approaches
419(6)
A.3.1.1 Exact Confidence Intervals and Credible Intervals Using a Jeffreys Prior
419(2)
A.3.1.2 Comparison Involving a Retention Fraction
421(2)
A.3.1.3 Likelihood Function for a Non-Inferiority Trial
423(2)
A.3.2 Dealing with More than One Comparison
425(2)
A.4 Stratified and Adjusted Analyses
427(4)
A.4.1 Stratification
427(2)
A.4.2 Analyses
429(2)
References
431(2)
Index 433
Dr. Mark Rothmann earned his Ph. D. in Statistics at the University of Iowa. He taught several years as a professor and has worked at the U. S. Food and Drug Administration. He has done research in many areas involving the design and analysis of clinical trials.

Dr. Brian L. Wiens, received his MS in statistics from Colorado State University and his Ph.D. in statistics from Temple University. He has worked at several pharmaceutical, biotechnology and medical device companies since 1991. He has published research in several areas of design and analysis of clinical trials. Dr. Wiens is a Fellow of the American Statistical Association.

Dr. Ivan S.F. Chan received his M.S. in Statistics from The Chinese University of Hong Kong and his Ph.D. in Biostatistics from University of Minnesota. He has worked at Merck Research Laboratories since 1995 and is currently Senior Director and Franchise Head for vaccines, leading the statistical support for all vaccine clinical research programs at Merck. Dr. Chan has published research in many areas of statistics including exact inference, analysis of non-inferiority trials, and methodologies for clinical trials.
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