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1949stone

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[资源] A practical guide to clinical data management

A practical guide to clinical data management



When new drugs or devices are tested in humans, the data generated by,
and related to, these trials are known as clinical data. These data represent
a huge investment by the biopharmaceutical or device company and are its
greatest assets. It is these data that will eventually make a new product
useful, and marketable, in disease therapy. The management of clinical data,
from its collection to its extraction for analysis, has become a critical element
in the steps to prepare a regulatory submission and to obtain approval to
market a treatment. As its importance has grown, clinical data management
(CDM) has changed from an essentially clerical task in the late 1970s and
early 1980s to the highly computerized specialty it is today.
I wrote the first edition of this book at a time when the role of clinical
data managers had developed into a specialty and career. Professional
organizations, conferences, and seminars were available but there were few
training courses and little information available that dealt with the wide
scope of, and variability in, typical data management tasks. The original
book was written mainly to provide basic information for new data managers,
but it included some more advanced topics for experienced data managers taking on new tasks.
The first edition, written starting in early 1998 and published in 1999,
reflected the state of clinical data management and the industry at that time.
A new Food and Drug Administration (FDA) regulation, 21 CFR 11,
“Electronic Records; Electronic Signatures,” had just come into effect at the time
of writing and we knew it would have an impact on how clinical data
management was carried out, but we did not know the full impact at that
point. While data management groups still struggle today in complying with
21 CFR 11, the basic requirements and expectations are now fairly clear. This
new edition incorporates the changes that data management groups have
made under 21 CFR 11 and also the other changes that I have seen in data
management industry practices. The FDA, industry auditors, and directors
of data management groups all have higher expectations now for how data
management tasks are carried out. This book is meant to help data managers
understand those expectations.
Another big change that has taken place since the first edition has been
the move to much greater use of electronic data capture (EDC) in place of
standard paper case report forms. At the time of this writing, most companies
are at a minimum experimenting with EDC and some will perform only
EDC trials. In revising each chapter, I looked for opportunities to point out
differences (and similarities) in data management between EDC-based studies
and paper-based studies. At this time, all data managers have to understand
processing for both types of studies.
To address the new expectations and reflect the kind of work that data
managers typically see today, the book has been reorganized. Part I,
“Elements of the Process,” covers the basic data management tasks that all data
managers must understand and all data management groups must deal with.
Part II, “Necessary Infrastructure,” is new. It addresses the expectations of
the FDA and auditors for how data management groups carry out their work
in compliance with regulations. Part III, “CDM Systems,” focuses on the
computer systems, including EDC, that all data management groups use and
the special procedures that must be in place to support those systems.
Even though industry and FDA expectations for quality in data
management are higher, that still does not mean that there is only one way
to do things. Often, there are several perfectly acceptable ways to perform
a task, any of which would ensure the integrity of the data and the ability
to analyze it. To acknowledge this diversity, every chapter presents a range
of successful and, above all, practical options for each element of the process
or task. This by no means implies that the approaches presented here are
the only possible ones! One thing I have learned is that there are always
new ways to tackle a task, and one has to understand the complete environment
(human and technical) to define an approach that will work best for a
given situation. The key to finding a successful, practical approach to data
management tasks in any environment is to be aware of the range of
possibilities and the implications of each. That is the aim of this book: to provide
data managers with enough background information and a number of
options for a task so they can find or develop an approach that gets the work
done with efficiency and quality.
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