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E-raamat: Analysis of Clinical Trials Using SAS: A Practical Guide, Second Edition

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  • Formaat: 410 pages
  • Ilmumisaeg: 17-Jul-2017
  • Kirjastus: SAS Institute
  • Keel: eng
  • ISBN-13: 9781635261462
  • Formaat - PDF+DRM
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  • Formaat: 410 pages
  • Ilmumisaeg: 17-Jul-2017
  • Kirjastus: SAS Institute
  • Keel: eng
  • ISBN-13: 9781635261462

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Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications. Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines. This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates: SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST) SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE) macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials) Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.
Preface v
About This Book xi
About These Authors xii
1 Model-based and Randomization-based Methods By Alex Dmitrienko and Gary G. Koch
1(66)
1.1 Introduction
1(3)
1.2 Analysis of continuous endpoints
4(16)
1.3 Analysis of categorical endpoints
20(21)
1.4 Analysis of time-to-event endpoints
41(15)
1.5 Qualitative interaction tests
56(11)
References
61(6)
2 Advanced Randomization-based Methods
67(34)
Richard C. Zink
Gary G. Koch
Yunro Chung
Laura Elizabeth Wiener
2.1 Introduction
67(3)
2.2 Case studies
70(3)
2.3 %NParCov4 macro
73(1)
2.4 Analysis of ordinal endpoints using a linear model
74(4)
2.5 Analysis of binary endpoints
78(1)
2.6 Analysis of ordinal endpoints using a proportional odds model
79(1)
2.7 Analysis of continuous endpoints using the log-ratio of two means
80(1)
2.8 Analysis of count endpoints using log-incidence density ratios
81(1)
2.9 Analysis of time-to-event endpoints
82(4)
2.10 Summary
86(15)
3 Dose-Escalation Methods
Guochen Song, Zoe Zhang, Nolan Wages, Anastasia Ivanova, Olga Marchenko and Alex Dmitrienko
101(1)
3.1 Introduction
101(2)
3.2 Rule-based methods
103(4)
3.3 Continual reassessment method
107(9)
3.4 Partial order continual reassessment method
116(7)
3.5 Summary
123(4)
References
123(4)
4 Dose-finding Methods
127(52)
Srinand Nandakumar
Alex Dmitrienko
Ilya Lipkovich
4.1 Introduction
127(1)
4.2 Case studies
128(4)
4.3 Dose-response assessment and dose-finding methods
132(13)
4.4 Dose finding in Case study 1
145(15)
4.5 Dose finding in Case study 2
160(19)
References
176(3)
5 Multiplicity Adjustment Methods
179(72)
Thomas Brechenmacher
Alex Dmitrienko
5.1 Introduction
179(5)
5.2 Single-step procedures
184(5)
5.3 Procedures with a data-driven hypothesis ordering
189(13)
5.4 Procedures with a prespecified hypothesis ordering
202(10)
5.5 Parametric procedures
212(9)
5.6 Gatekeeping procedures
221(30)
References
241(3)
Appendix
244(7)
6 Interim Data Monitoring
251(68)
Alex Dmitrienko
Yang Yuan
6.1 Introduction
251(2)
6.2 Repeated significance tests
253(39)
6.3 Stochastic curtailment tests
292(27)
References
315(4)
7 Analysis of Incomplete Data
319(59)
Geert Molenberghs
Michael G. Kenward
7.1 Introduction
319(3)
7.2 Case Study
322(2)
7.3 Data Setting and Methodology
324(10)
7.4 Simple Methods and MCAR
334(4)
7.5 Ignorable Likelihood (Direct Likelihood)
338(3)
7.6 Direct Bayesian Analysis (Ignorable Bayesian Analysis)
341(3)
7.7 Weighted Generalized Estimating Equations
344(5)
7.8 Multiple Imputation
349(13)
7.9 An Overview of Sensitivity Analysis
362(1)
7.10 Sensitivity Analysis Using Local Influence
363(8)
7.11 Sensitivity Analysis Based on Multiple Imputation and Pattern-Mixture Models
371(7)
7.12 Concluding Remarks
378(1)
References 378(7)
Index 385