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Institutional Subscription. Free Shipping Free global shipping No minimum order. Describes the entire clinical trial management process from start to finish in a step-by-step guide Provides best practice elements, including case studies, practical examples, activities, and checklists.
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Medical Data Management
Amazon Sell Back. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number Hardcover International Standard Book Number Hardcover This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use.
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Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Includes bibliographical references and index. ISBN Hardcover : alk. Drugs--Testing--Data processing. Clinical trials--Data processing.
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Database management. Clinical Trials. Data Collection--methods. I4 Pp ] RM 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 s and early s 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 and published in , reflected the state of clinical data management and the industry at that time.
Epub Practical Guide To Clinical Data Management
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. 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. It addresses the expectations of the FDA and auditors for how data management groups carry out their work in compliance with regulations. 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. She earned a degree in physics from the Massachusetts Institute of Technology in and went on to do graduate work at Brandeis University.
On leaving graduate school, she worked as a staff physicist for a medical device manufacturer and became interested in clinical data and clinical trials. She has held a variety of senior consulting positions at software vendors and service providers both in the United States and in Europe since the mids. She has been an independent consultant since January She is often called upon to give seminars and training sessions for data management professionals through the Drug Information Association, Society for Clinical Data Managers, and other organizations.
She is also an instructor for the extension programs of the University of California Berkeley and the University of California Santa Cruz. Any data management group, large or small, long established, or just emerging, will have to perform these tasks or be closely involved with them. Even companies that do not perform data management in house, but rather send all their data management work to contract research organizations, will need to beware of these elements and oversee them as they are carried out.
For the most part, they all did a good job of this and produced datasets with accurate data that reflected values provided by the investigator sites. However, even with good study files, some data management groups found they could not always quickly find an answer when an auditor asked a specific question about the conduct of a past study. So, several years ago, some companies began to address this problem by creating a document whose purpose was to record all the most important information on how data management was carried out for a study.
They quickly found that creating this kind of document at the start of a study provides added value — beyond its function as a reference — by forcing study planning. The documents are also more accurate when written at the start of a study rather than as a summary or report at the end of the study. A DMP that is written at the beginning of a study provides a focus for identifying the work to be performed, who will perform that work, and what is to be produced as documentation of the work. Plans can vary in length depending on the size of the data management group and how standard study activities are.
In this chapter, we will discuss what goes into these plans and how to use them efficiently. What goes into a plan? A DMP should touch on all the elements of the data management process for the study in question. The elements are those that form the structure of Part I of this book and are summarized in Figure 1. Who is responsible for the work? Which SOPs or guidelines will apply?