Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods

635 Pages·2015·9.92 MB·English

by  Sandeep Menon

The correct bibliographic citation for this manual is as follows: SAS
Institute Inc. 2015. Modern Approaches to Clinical Trials Using
SAS®: Classical, Adaptive, and Bayesian Methods Cary, NC: SAS
Institute Inc.
Modern Approaches to Clinical Trials Using SAS®: Classical, Adaptive, and Bayesian
Methods
Copyright © 2015, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-62959-385-2 (Hardcopy)
ISBN 978-1-62960-082-6 (Epub)
ISBN 978-1-62960-083-3 (Mobi)
ISBN 978-1-62960-084-0 (PDF)
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Foreword
Recent years, and perhaps particularly the past decade, have seen a
rapid evolution in the statistical methodology available to be used in
clinical trials, from both technical and implementation standpoints.
Certain practices as they might have been performed not too far into the
past might in fact now seem somewhat primitive or naïve. Much, but
certainly by no means all, of the recent development is related to recent
interest in adaptive trial designs. The term itself is quite broad, and
encompasses a wide variety of techniques and applications. Many trial
aspects are potential candidates for adaptation, including but not limited
to: sample size or information requirements, dose or treatment regimen
selection, targeted patient population selection, the randomization
allocation scheme; and within each of these categories there may be
multiple and fundamentally different technical and strategic approaches
that are now available for practitioners to consider.
Classical procedures as well have undergone advancements in the
statistical details of their implementation, and their usage in analysis and
interpretation of trial results. Enhancements in classical approaches, and
the progress made or envisioned in utilization of novel adaptive and
Bayesian designs and methodologies, are reflective of the current
interest in the transition to personalized medicine approaches, by which
optimal therapies corresponding to particular patient characteristics are
sought. A categorization of designs and methods into classical, adaptive,
and Bayesian methods is by no means mutually exclusive, as a number
of methodologies have aspects of more than one of these classes. Just
to cite one example, group sequential designs are a familiar feature in
current clinical trial practice that fall under both the classical and
adaptive headings; this is also certainly an area that has seen an
evolution in recent years. Aspects of clinical trial or program design such
as dose finding or population enrichment may contain aspects that are
adaptive, or Bayesian, or both, as is communicated well in this volume.
The interest in novel adaptive and Bayesian approaches certainly does
not preclude the possibility that classical approaches will be preferred in
many situations; they maintain the attributes which led to their
widespread adoption in the first place. As has been pointed out by many
authors, the best use of these novel approaches will be realized by a full
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understanding of their behavior and an objective evaluation of their
advantages and relevant tradeoffs in particular situations. This point is
clearly and objectively conveyed throughout this volume, as approaches
of varied types are presented not to promote or endorse their casual
routine use, but rather are described with sufficient explanations to help
practitioners make the best choices for their situations, and of course to
have the computational tools to implement them.
It seems inevitable that the availability to users of software and
computational capabilities is inextricably linked with increased
consideration of and interest in alternative design and analysis
strategies, and ultimately their implementation. Certainly, if a novel
methodology is seen as adding value in such an important arena as
clinical trials, it will spur development of the computational tools
necessary to implement it. However, in a cycle, the increased availability
to practitioners leads to increased consideration and implementation,
which spurs further interest, enables learnings from experience, perhaps
motivates further research, and ultimately leads to further
methodological and in-practice improvements and evolution.
Just as a simple illustration of this phenomenon: questions regarding
how clinical sites should best be accounted for in main statistical
analysis models had undergone some debate in past decades, with
occasional flurries of literature activity, but evolution in conventional
practices was limited. The introduction of SAS’ in the early
proc mixed
1990s provided a platform for more widespread consideration and
usage of some approaches that were less commonly utilized at that
time, which incorporated clinical site as a random effect in analysis
models in various manners. There were implications for important
related issues, such as sample size determination and targeted center-
size distributions, and for certain practices that were in use at the time
such as small center pooling algorithms. Given the presence of the new
computational tool available to users in the form of the SAS procedure,
it may not be a coincidence that by the latter part of that decade there
was vigorous dialogue taking place in the literature on matters involving
how best to design multicenter studies and accommodate center in
analysis models, and within a relatively short period of time there were
notable changes in conventional practices.
Given the extent of recent methodological advances, and the wide
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knowledge of and usage of SAS throughout the clinical trials community,
a focused volume such as this one is particularly timely in this regard. It
integrates a broad yet coherent summary of current approaches for
clinical trial design and analysis, with particular emphasis on important
recently developed ones, along with specific illustrations of how they can
be implemented and performed in SAS. In some cases this involves
relatively straightforward calls to SAS procedures; in many others,
sophisticated SAS macros developed by the authors are presented.
Motivating examples are described, and SAS outputs corresponding to
those examples are explained to help guide readers through the most
accurate understandings and interpretations. This text might well
function effectively as a technical resource on state-of-the-art clinical
trials methodology even if it did not contain the SAS illustrations and
explanations; and it could also fit within a useful niche if it focused solely
on the SAS illustrations without the methodological and practical
explanations. The fact that it contains both aspects, well integrated in
chapters prepared by experienced subject matter experts, makes it a
particularly valuable resource. The ability that the material contained
here offers to practitioners to test and compare different design and
analysis options to choose the one that seems best for a given situation
can help drive the most impactful usage of these new technologies; and,
along the lines of the methodology-computational tools cycle described
earlier, this perhaps may assist in leading to further experience-driven
methodological or implementation advancements.
Paul Gallo
Novartis
October 2015
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About This Book
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Purpose
Modern Approaches to Clinical Trials Using SAS®: Classical, Adaptive,
and Bayesian Methods is unique and multifaceted, covering several
domains of modern clinical trial design, including classical, group
sequential, adaptive, and Bayesian methods that are applicable to and
widely used in various phases of pharmaceutical development. Topics
covered include, but are not limited to, dose-response and dose-
escalation designs; sequential methods to stop trials early for
overwhelming efficacy, safety, or futility; Bayesian designs that
incorporate historical data; adaptive sample size re-estimation; adaptive
randomization to allocate subjects to more effective treatments; and
population enrichment designs. Methods are illustrated using clinical
trials from diverse therapeutic areas, including dermatology,
endocrinology, infectious disease, neurology, oncology, and
rheumatology. Individual chapters are authored by renowned
contributors, experts, and key opinion leaders from the
pharmaceutical/medical device industry or academia.
Numerous real-world examples and sample SAS code enable users to
readily apply novel clinical trial design and analysis methodologies in
practice.
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Is This Book for You?
This book is intended for biostatisticians, pharmacometricians, clinical
developers, and statistical programmers involved in the design, analysis,
and interpretation of clinical trials. Further, students in graduate and
post-graduate programs in statistics or biostatistics will benefit from the
many practical illustrations of statistical concepts.
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Prerequisites
Based on the above audience, users will benefit most from this book
with some graduate training in statistics or biostatistics, and some
experience or exposure to clinical trials. Some experience with
simulation may be useful, though this is not required to use this book.
Some experience with SAS/STAT procedures, SAS/IML, and the SAS
macro language is expected.
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