Analysis of Clinical Trials Using SAS

Description

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.

Sujets

• Health care
• Pharmaceutical drug
• Statistics
• Biostatistics

Informations

• Publié par: SAS Institute
• Date de parution: 17 juillet 2017
• Nombre de lectures: 0
• EAN13: 9781635261448
• Langue: English
• Poids de l'ouvrage: 19 Mo

Extrait

Analysis of Clinical Trials Using SAS
A Practical Guide
Second Edition
Edited by Alex Dmitrienko Gary G. Koch
The correct bibliographic citation for this manual is as follows: Dmitrienko, Alex, and Gary G. Koch. 2017. Analysis of Clinical Trials Using SAS : A Practical Guide, Second Edition . Cary, NC: SAS Institute Inc.
Analysis of Clinical Trials Using SAS : A Practical Guide, Second Edition
Copyright 2017, SAS Institute Inc., Cary, NC, USA ISBN 978-1-62959-847-5 (Hard copy) ISBN 978-1-63526-144-8 (EPUB) ISBN 978-1-63526-145-5 (MOBI) ISBN 978-1-63526-146-2 (PDF)
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Contents
Preface
About This Book
About These Authors
1 Model-based and Randomization-based Methods By Alex Dmitrienko and Gary G. Koch
1.1 Introduction
1.2 Analysis of continuous endpoints
1.3 Analysis of categorical endpoints
1.4 Analysis of time-to-event endpoints
1.5 Qualitative interaction tests
References
2 Advanced Randomization-based Methods By Richard C. Zink, Gary G. Koch, Yunro Chung and Laura Elizabeth Wiener
2.1 Introduction
2.2 Case studies
2.3 %NParCov4 macro
2.4 Analysis of ordinal endpoints using a linear model
2.5 Analysis of binary endpoints
2.6 Analysis of ordinal endpoints using a proportional odds model
2.7 Analysis of continuous endpoints using the log-ratio of two means
2.8 Analysis of count endpoints using log-incidence density ratios
2.9 Analysis of time-to-event endpoints
2.10 Summary
3 Dose-Escalation Methods By Guochen Song, Zoe Zhang, Nolan Wages, Anastasia Ivanova, Olga Marchenko and Alex Dmitrienko
3.1 Introduction
3.2 Rule-based methods
3.3 Continual reassessment method
3.4 Partial order continual reassessment method
3.5 Summary
References
4 Dose-finding Methods By Srinand Nandakumar, Alex Dmitrienko and Ilya Lipkovich
4.1 Introduction
4.2 Case studies
4.3 Dose-response assessment and dose-finding methods
4.4 Dose finding in Case study 1
4.5 Dose finding in Case study 2
References
5 Multiplicity Adjustment Methods By Thomas Brechenmacher and Alex Dmitrienko
5.1 Introduction
5.2 Single-step procedures
5.3 Procedures with a data-driven hypothesis ordering
5.4 Procedures with a prespecified hypothesis ordering
5.5 Parametric procedures
5.6 Gatekeeping procedures
References
Appendix
6 Interim Data Monitoring By Alex Dmitrienko and Yang Yuan
6.1 Introduction
6.2 Repeated significance tests
6.3 Stochastic curtailment tests
References
7 Analysis of Incomplete Data By Geert Molenberghs and Michael G. Kenward
7.1 Introduction
7.2 Case Study
7.3 Data Setting and Methodology
7.4 Simple Methods and MCAR
7.5 Ignorable Likelihood (Direct Likelihood)
7.6 Direct Bayesian Analysis (Ignorable Bayesian Analysis)
7.7 Weighted Generalized Estimating Equations
7.8 Multiple Imputation
7.9 An Overview of Sensitivity Analysis
7.10 Sensitivity Analysis Using Local Influence
7.11 Sensitivity Analysis Based on Multiple Imputation and Pattern-Mixture Models
7.12 Concluding Remarks
References
Index
Preface
Introduction
Clinical trials have long been one of the most important tools in the arsenal of clinicians and scientists who help develop pharmaceuticals, biologics, and medical devices. It is reported that close to 10,000 clinical studies are conducted every year around the world. We can find many excellent books that address fundamental statistical and general scientific principles underlying the design and analysis of clinical trials. [for example, Pocock (1983); Fleiss (1986); Meinert (1986); Friedman, Furberg, and DeMets (1996); Piantadosi (1997); and Senn (1997)]. Numerous references can be found in these fine books. It is also important to mention recently published SAS Press books that discuss topics related to clinical trial statistics as well as other relevant topics, e.g., Dmitrienko, Chuang-Stein, and D Agostino (2007); Westfall, Tobias, and Wolfinger (2011); Stokes, Davis, and Koch (2012); and Menon and Zink (2016).
The aim of this book is unique in that it focuses in great detail on a set of selected and practical problems facing statisticians and biomedical scientists conducting clinical research. We discuss solutions to these problems based on modern statistical methods, and we review computer-intensive techniques that help clinical researchers efficiently and rapidly implement these methods in the powerful SAS environment.
It is a challenge to select a few topics that are most important and relevant to the design and analysis of clinical trials. Our choice of topics for this book was guided by the International Conference on Harmonization (ICH) guideline for the pharmaceutical industry entitled Structure and Content of Clinical Study Reports, which is commonly referred to as ICH E3. The documents states the following:
Important features of the analysis, including the particular methods used, adjustments made for demographic or baseline measurements or concomitant therapy, handling of dropouts and missing data, adjustments for multiple comparisons, special analyses of multicenter studies, and adjustments for interim analyses, should be discussed [in the study report].
Following the ICH recommendations, we decided to focus in this book on the analysis of stratified data, incomplete data, multiple inferences, and issues arising in safety and efficacy monitoring. We also address other statistical problems that are very important in a clinical trial setting. The latter includes reference intervals for safety and diagnostic measurements.
One special feature of the book is the inclusion of numerous SAS macros to help readers implement the new methodology in the SAS environment. The availability of the programs and the detailed discussion of the output from the macros help make the applications of new procedures a reality.
The book is aimed at clinical statisticians and other scientists who are involved in the design and analysis of clinical trials conducted by the pharmaceutical industry and academic institutions or governmental institutions, such as NIH. Graduate students specializing in biostatistics will also find the material in this book useful because of the applied nature of this book.
Since the book is written for practitioners, it concentrates primarily on solutions rather than the underlying theory. Although most of the chapters include some tutorial material, this book is not intended to provide a comprehensive coverage of the selected topics. Nevertheless, each chapter gives a high-level description of the methodological aspects of the statistical problem at hand and includes references to publications that contain more advanced material. In addition, each chapter gives a detailed overview of the key statistical principles. References to relevant regulatory guidance documents, including recently released guidelines on adaptive designs and multiplicity issues in clinical trials, are provided. Examples from multiple clinical trials at different stages of drug development are used throughout the book to motivate and illustrate the statistical methods presented in the book.
Outline of the book
The book has been reorganiz