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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 nearly 10,000
clinical studies are conducted every year around the world. One can find many excellent books that
address fundamental statistical and general scientific principles underlying the design and analysis of
clinical trials, such as those by 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.
The aim of this book is unique in that we focus 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 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 the 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” (this document is commonly referred to as ICH E3). The document states that
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, such as 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 application of new procedures a reality. The
authors are planning to make the SAS macros compatible with new SAS products such as SAS
Enterprise Guide. Enterprise Guide tasks that implement the statistical methods discussed in the book
will be published on the SAS Enterprise Guide Users Group Web site at http://www.segus.org.
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, academic institutions, or
governmental institutions such as the National Institutes of Health (NIH). Graduate students specializing
in biostatistics will also find the material in this book useful because of its applied nature.
Because the book is written for practitioners, it concentrates primarily on solutions rather than
theory. Although most of the chapters include some tutorial material, this book was not intended to
vi Analysis of Clinical Trials Using SAS: A Practical Guide
provide a comprehensive coverage of the selected topics. Nevertheless, each chapter gives a high-level
description of the theoretical 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 underlying statistical principles.
There are some exceptions to the presentation of minimum theory in the book. For example, Chapter
5 discusses the analysis of incomplete data and covers comparatively complex statistical concepts such
as multiple imputation. Although the theoretical part is written at a higher statistical level, examples and
applications are prepared in such a way that they can be easily understood.
Examples from real trials are used throughout the book to illustrate the concepts being discussed and
to help the reader understand their relevance in a clinical trial setting. Most of the data come from real
clinical trials. In several cases, because of confidentiality concerns, we relied on simulated data that are
representative of real clinical trial data. Although simulated data might lack authenticity, using them
does afford us the opportunity to see how close to the truth we can get using the proposed methodology.
In this regard, we echo Hastie and Tibshirani’s (1990, page 239) statement that “an advantage of using
simulated examples is that you know the truth.”
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