Table Of ContentThe correct bibliographic citation for this manual is as follows: Zink, Richard C. 2014. Risk-Based Monitoring and
Fraud Detection in Clinical Trials Using JMP® and SAS®. Cary, North Carolina: SAS Institute Inc.
Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP® and SAS®
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Contents
About This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
About the Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Chapter 1 • Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Topics Addressed in This Book . . . . . . . . . . . . . . . . . . . . . 3
1.3 The Importance of Data Standards . . . . . . . . . . . . . . . . . . 8
1.4 JMP Clinical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5 Clinical Trial Example: Nicardipine . . . . . . . . . . . . . . . . . . 13
1.6 Organization of This Book . . . . . . . . . . . . . . . . . . . . . . . . 15
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Chapter 2 • Risk-Based Monitoring: Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Risk Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3 Geocoding Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.4 Reviewing Risk Indicators . . . . . . . . . . . . . . . . . . . . . . . . 53
2.5 Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Chapter 3 • Risk-Based Monitoring: Customizing the Review Experience . . . . . . . . . . . . . 79
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.2 Defining Alternate Risk Thresholds and Actions . . . . . . . 80
3.3 Performing Additional Statistical and
Graphical Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3.4 Creating JMP Scripts and Add-Ins . . . . . . . . . . . . . . . . . 108
3.5 Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
Chapter 4 • Detecting Fraud at the Clinical Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
vi Contents
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.2 Study Visits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
4.3 Measurements Collected at the Clinical Site . . . . . . . . . 144
4.4 Multivariate Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
4.5 Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
Chapter 5 • Detecting Patient Fraud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
5.2 Initials and Birthdate Matching . . . . . . . . . . . . . . . . . . . . 180
5.3 Hierarchical Clustering of Pre-Dosing
Covariates Across Clinical Sites . . . . . . . . . . . . . . . . . . . 185
5.4 Review Builder: Quality and Fraud . . . . . . . . . . . . . . . . . 192
5.5 Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
Chapter 6 • Snapshot Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
6.2 Domain Keys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
6.3 Review Flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
6.4 Adding and Viewing Notes . . . . . . . . . . . . . . . . . . . . . . . 217
6.5 Using Review Flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
6.6 Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
Chapter 7 • Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
A Work in Progress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
Stay in Touch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
Recommended Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
vii
About This Book
Purpose
There is increasing literature and discussion on the need of the pharmaceutical industry to
improve efficiency and reduce costs through the use of centralized monitoring techniques. So-
called risk-based monitoring (RBM) makes use of central computerized review of clinical trial data
and site metrics to determine if and when clinical sites should receive more extensive quality
review through on-site monitoring visits. There are many benefits to RBM beyond cost: Statistical
and graphical checks can determine the presence of outliers or unusual patterns in the data,
comparisons can be made between sites to assess performance and identify potentially
fraudulent data or miscalibrated or faulty equipment, and issues can be identified and resolved
while the trial is ongoing. As stated in the recent FDA guidance on the topic, RBM forces the
sponsor to take a more proactive approach to quality through a well-defined protocol and
sufficient training and communication, and by highlighting those data most important to patient
safety and the integrity of the final study results. There is further emphasis on flexibility and
adaptability: responding to issues as they arise to prevent further problems, with the willingness
to put additional safeguards in place or refine procedures to address shortcomings as the trial is
ongoing.
The primary goal of this book is to describe a practical implementation of JMP and SAS for the
centralized monitoring of clinical trials. This includes RBM analyses by defining risk indicators and
their corresponding thresholds, methods to identify patient- and investigator-perpetrated fraud,
and other visual and analytical techniques to enhance safety and quality reviews. A second but
equally important goal is to promote these methodologies and establish a dialogue with
practitioners for continual improvement of analytical techniques and software. Controlling clinical
trial costs through centralized monitoring is important, as these expenses are ultimately passed
on to the consumer; reining in costs is one way to keep drugs affordable for the people who need
them. Further, we’ll see repeatedly throughout the book that centralized monitoring meets the
needs for Good Clinical Practice—protecting the well-being of trial participants, and maintaining a
high level of data quality to ensure the validity and integrity of the final analysis results. While this
book is written from the point of view of drug development, the methods described are equally
applicable to clinical trials of biologics (i.e., vaccines) or medical devices.
viii About This Book
Is This Book for You?
Any clinical trial is the result of the efforts of a diverse team. This team includes clinicians,
statisticians, data managers, programmers, regulatory associates, and monitors, to name a few.
Every team member has a role to play in the review of trial data, and each individual brings a
unique skill set important for understanding patient safety, protocol adherence, or data
insufficiencies that can affect the final analysis, clinical study report, and subsequent regulatory
review and approval. Given the interactive, dynamic, and graphical nature of JMP Clinical, it is my
sincere hope that any individual from the clinical trial team can make use of this book and the
examples contained within to streamline, accelerate, and enrich their reviews of clinical trial data.
Though JMP Clinical makes use of SAS, it is neither expected nor required that the user have
any SAS programming experience.
Prerequisites
Users familiar with JMP can expect a similar experience with JMP Clinical; analyses and reports
are available from a menu-driven, point-and-click interface. Basic knowledge of JMP is useful
(though not required) and can be obtained through Tutorials in the Help menu, the PDF books
Discovering JMP and Using JMP under Help > Books or by referring to JMP Essentials,
Second Edition by Curt Hinrichs and Chuck Boiler. This book focuses heavily on the operational
and data aspects of clinical trials. Familiarity in these areas will make for a more pleasant reading
experience. Data standards from the Clinical Data Interchange Standards Consortium (CDISC)
are a requirement to use JMP Clinical and are discussed throughout this book. Some familiarity
with these standards is useful, though they are addressed at a basic level.
About the Examples
Software Used to Develop the Book's Content
JMP Clinical 5.0, the software version that is the focus of this book, is a combination product that
includes JMP 11.1 and SAS 9.4 M1 for Base SAS, SAS/GRAPH, and SAS/ACCESS to PC files,
and SAS analytics releases 12.3 for SAS/IML, SAS/STAT, and SAS/Genetics. Also included are
About This Book ix
macros written in SAS and the JMP Scripting Language (JSL) that generate the various analyses
and reports that are specific to JMP Clinical.
The most straightforward answer, however, is simply JMP Clinical 5.0.
Example Code and Data
Data for the clinical trial of Nicardipine is included and shipped with JMP Clinical. Users should
have immediate access to this data.
You can access additional supporting files for this book by linking to its author page at http://
support.sas.com/publishing/authors. Select the name of the author. Then, look for the cover
thumbnail of this book, and select Example Code and Data to display the SAS programs that are
included in this book.
For an alphabetical listing of all books for which example code and data is available, see http://
support.sas.com/bookcode. Select a title to display the book’s example code.
If you are unable to access the code through the Web site, send an e-mail to [email protected].
Output and Graphics Used in This Book
All output was generated using JMP Clinical 5.0 and captured using FullShot 9.5 Professional.
Additional Resources
SAS offers you a rich variety of resources to help build your SAS skills and explore and apply the
full power of SAS software. Whether you are in a professional or academic setting, we have
learning products that can help you maximize your investment in SAS.
Keep in Touch
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to contact us about a specific book, please include the book title in your correspondence.
To Contact the Author through SAS Press
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x About This Book
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xi
About the Author
Richard C. Zink is a Principal Research Statistician Developer in the JMP
Life Sciences division at SAS Institute. He is currently a developer for JMP
Clinical, an innovative software package designed to streamline the review of
clinical trial data. He joined SAS in 2011 after eight years in the
pharmaceutical industry, where he designed and analyzed clinical trials for
patients diagnosed with chronic hepatitis B infection, chronic myeloid
leukemia, glaucoma, dry eye disease, blepharitis, or cystic fibrosis; he also
participated in U.S. and European drug submissions and in two FDA advisory
committee hearings. When not actively engaged in clinical development
responsibilities, he supported non-clinical development, pharmaceutical
sciences, and sales and marketing activities.
Richard is an active member of the Biopharmaceutical Section of the
American Statistical Association, the Drug Information Association, and
Statisticians in the Pharmaceutical Industry. He is currently the Statistics
Section Editor for Therapeutic Innovation & Regulatory Science (formerly
Drug Information Journal). He is a frequent speaker at workshops and
scientific meetings and has lectured for courses in statistics and clinical trials.
His research interests include the analysis of pre- and post-market adverse
events, subgroup identification for patients with enhanced treatment
response, and risk-based monitoring and fraud detection in clinical trials.
Richard holds a Ph.D. in Biostatistics from the University of North Carolina at
Chapel Hill and has more than 20 years of SAS programming experience.
This is his first book.
xii About the Author
