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|a Zink, Richard C.
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|a Risk-based monitoring and fraud detection in clinical trials using JMP and SAS /
|c Richard C. Zink.
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|a Cary, N.C. :
|b SAS Institute,
|c 2014.
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|a 1 online resource (1 volume) :
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|a Online resource; title from title page (Safari, viewed August 6, 2014).
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|a Includes bibliographical references and index.
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|a This book presents a practical implementation of methodologies within JMP Clinical for the centralized monitoring of clinical trials. Focused on intermediate users, this book describes analyses for RBM that incorporate and extend the recommendations of TransCelerate Biopharm Inc., methods to detect potential patient-or investigator misconduct, snapshot comparisons to more easily identify new or modified data, and other novel visual and analytical techniques to enhance safety and quality reviews. It highlights recent regulatory guidance documents on risk-based approaches, addresses the requirements for CDISC data, and describes methods to supplement analyses with data captured external to the study database. Given the interactive, dynamic, and graphical nature of JMP Clinical, any individual from the clinical trial team--including clinicians, statisticians, data managers, programmers, regulatory associates, and monitors--can make use of this book and the numerous examples contained within to streamline, accelerate, and enrich their reviews of clinical trial data. The analytical methods described in this book enable the clinical trial team to take a proactive approach to data quality and safety to streamline clinical development activities and address shortcomings while the study is ongoing. --
|c Edited summary from book.
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0 |
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|a Introduction -- Risk-based monitoring: basic concepts -- Risk-based monitoring: customizing the review experience -- Detecting fraud at the clinical site -- Detectign patient fraud -- Snapshot comparisons -- Final thoughts.
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|i Print version:
|a Zink, Richard C.
|t Risk-based monitoring and fraud detection in clinical trials using JMP and SAS.
|d Cary, North Carolina : SAS Institute, 2014
|z 9781612909912
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