PAREXEL's Patrick Nadolny on the benefits of data management expertise, tech, and process to efficient Risk Based Study Execution (RBSE).

1. © 2017 PAREXEL INTERNATIONAL CORP.
DATA MANAGEMENT
ROLES, PROCESS &
TECHNOLOGIES
IN RISK BASED STUDY
EXECUTION
SCDM, Orlando, Florida, September 2017
Patrick Nadolny, VP, Product Management, PAREXEL

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OBJECTIVE
In this presentation, we will
explore the benefits of Data
Management expertise, process
and technology to support
efficient and quality driven
approaches to Risk Based Study
Execution (RBSE).

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AGENDA
• Industry trends affecting Data Management
• Data Management: Study Start-Up enablers
• Data Management: Key considerations for Risk Based Study
Execution
• Data Management Role in Risk Based Study Execution
• Technology Requirements for Data Management
• Conclusion
• Q & A

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TOKYO, JAPAN
INDUSTRY TRENDS
AFFECTING
DATA MANAGEMENT

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INDUSTRY TRENDS
COMPLEX
57%* of all clinical studies
have at least one substantial
global protocol amendment
Probability of Success from
Phase I to Approval in 9.6%**
COSTLY
The average cost to develop
and gain marketing approval
for a new prescription medicine,
a process often lasting
longer than a decade, is now
$2.56 billion dollars*
HOW TO MITIGATE THESE
CHALLENGES WITH BETTER
DATA - DRIVEN DECISIONS
LENGTHY
In 2016 Study Start-Up took
 8 weeks longer than in 2003
Protocol Approval to First Subject
Randomized:
2014-2016 – 20.7 Weeks*
2003-2005 – 12.4 Weeks*
* KMR, 2017, Average across all Study Phases * Tufts January 2014, Impact Report Summary * Tuft CSDD Outlook 2015
* * Biotechnology Innovation Organization (BIO) ,
Clinical Development Success Rates 2006-2015

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INDUSTRY TRENDS – ENROLLMENT
QUIZ!
First Subject Randomized to Last Subject Randomized* ?
• Response A – 27 Weeks to 38 Weeks  11 Weeks
• Response B – 22 Weeks to 42 Weeks  20 Weeks
• Response C – 25 Weeks to 41 Weeks  15 Weeks
Changes between 2003 and 2016 ?
* KMR, 2017, Average across all Study Phases

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INDUSTRY TRENDS – DATABASE LOCK
QUIZ !
Last Subject Complete to Database Lock* ?
• Response A – 10 Weeks to 8 Weeks  2 Weeks
• Response B – 11 Weeks to 7 Weeks  4 Weeks
• Response C – 10 Weeks to 5 Weeks  5 Weeks
* KMR, 2017, Average across all Study Phases
Changes between 2003 and 2016 ?

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41%
30%
INDUSTRY TRENDS
Increasing adoption of Risk Based Monitoring and Adaptive approaches
Adaptive Design
ISR (2015)
Increased to 30% (versus 22% in 2014)
22% reported in
2014
Risk Based Monitoring
ISR (Nov 2015)
Average increase YoY is 9%
RISING COMPLEXITY

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INDUSTRY TRENDS
RISING
COMPLEXITY
ePRO
Imaging
Sensors &
Wearables
Devices
(e.g. ECG)
EHR/EMR
EDC centricity is
declining fast !

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INDUSTRY TRENDS
IMPACT OF TRADITIONAL DATA MANAGEMENT *
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
N = 1,168 Studies
EDC Data with no correction post first entry (96.3%)
EDC Data Corrected by any process (3.7%)
* TransCelerate Sep 2014: Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials
96.3 %
0.0%
1.0%
2.0%
3.0%
4.0%
1.2%
1.1%
1.4%
SDV
Auto-Queries
Other: DM, Stat, Coding, Med
Monitoring, remote Reviews, etc.
3.7%
Only 3.7% of the data entered in the
e-CRF is ever changed after initial entry.
With only 1.2% of other data changes
not attributed to SDV nor Auto-Queries

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EVOLUTION OF DATA MANAGEMENT LANDSCAPE
ICH E6 REVISION 2
Need to implement an end to end Risk Management Strategy
• Full Risk Management Life Cycle: Identification, Evaluation, Control (QTL), Communication, Review
and Reporting of Risk and Issues.
• Focus on activities essential to ensuring human subject protection and the reliability of trial results.
• Includes the design of efficient clinical trial protocols, tools and procedures for data collection and
processing, as well as the collection of information that is essential to decision making.
• Identify Critical Processes & Data
• Centralized monitoring processes provide additional monitoring capabilities that can complement and
reduce the extent and/or frequency of on-site monitoring and help distinguish between reliable data
and potentially unreliable data.
• Centralized monitoring is a remote evaluation of accumulating data, performed in a timely manner,
supported by appropriately qualified and trained persons (e.g., data managers, biostatisticians).

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Centralized monitoring Review
.. may include statistical analyses, of accumulating data from centralized monitoring can be used to:
(a) identify missing data, inconsistent data, data outliers, unexpected lack of variability and protocol
deviations.
(b) examine data trends such as the range, consistency, and variability of data within and across sites.
(c) evaluate for systematic or significant errors in data collection and reporting at a site or across sites;
or potential data manipulation or data integrity problems.
(d) analyze site characteristics and performance metrics.
(e) select sites and/or processes for targeted on-site monitoring.
Data Surveillance / Statistical Monitoring
Risk Based Monitoring
Data Cleaning (EDC, eCOA, Imaging, etc)
Clinical Data Repository- Central Issue & Risk Management
EVOLUTION OF DATA MANAGEMENT LANDSCAPE
ICH E6 REVISION 2

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Industry Focus
• Cost & Time Reduction
• Gain operational efficiencies
• Increase predictability of Outcome
• Manage complexity
• Keep-up with regulations (e.g. ICH E6 Revision 2)
Also ..
• Invest in emerging countries while continuing growth in
“wealthy” established one (w/ aging patient population)
• Early consideration for reimbursement and payer needs

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15. © 2017 PAREXEL INTERNATIONAL CORP. /
Enable faster start-up
The obvious .. Quality By Design!
• Leverage Standards for Data Collection [CDASH]
• Define Standard agreements with pre-defined deliverables from
preferred partners (i.e. prevent scope creep and one-off designs):
e-COA, IVRS, Imaging, Wearables, CROs, etc.
Also...
• Conduct a data risk assessment to feed the global study RACT.
Risk is Study, Country & Site Level (DM Plan appendix?)
• Focus Edit Checks on Critical Data Elements to ensure Patient
Safety & reliability of trial results ( Embrace RBSE)
• Define Data Management Quality Tolerance Limits (QTLs)
• Synergize strategy with site and country selection
• Synergize strategy with on-site and offsite Monitoring
DATA
MANAGEMENT

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Sımplify to enable RBSE support
The obvious!
• Leverage Standards for Integration [ODM] and reporting [SDTM]
• Automate tasks to ease workflow and limit risk of manual errors
(e.g. direct eSAE Capture in EDC to eliminate SAE Reconciliation)
Also...
• Implement a targeted data cleaning strategy supporting RBSE
(Line Listing Review is inefficient and does not help finding the
needle in the hay stack !)
• Automate detection of missing data patterns, outliers, trends, lack
of variability, etc. Simple algorithms can go a long way !
Look at Scientific and Operational Data!!
• Consider Proactive Data Mastering to reduce and potentially
eliminate data reconciliation (One Record of truth with automated
propagation (e.g. IMPACT® CTMS to DataLabs® EDC)
DATA
MANAGEMENT

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Need to change !
Larger and more complex role…
• Be a change agent
• Be holistic (Process and Data),
• Handle complexity (More data sources, Adaptive design, etc.),
• Be even more analytical (e.g. Science driven, trend investigation),
• Be more collaborative (e.g. Feasibility),
• Prevent instead of correct,
• Apply DM Skills beyond EDC via MDR and CDR based tools
(Include operational data such as CTMS, Audit Trials, etc.)
• Manage risks: Not checking every box and being risk adverse
• Learn from RBM (e.g. fastidious SDV has limited value and does
not secure quality)
MOVE THE NEEDLE !
DATA
MANAGERS
ROLE

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Need the right tools (We are in 2017)
Big and complex data to manage!
• Refuse to collect data twice because systems cannot share them
(e.g. EDC and IVRS – Stratification). Also, unify and share sites
and patients identifiers across systems.
• Implement metadata driven transformation (e.g. Raw to SDTM)
• Use a flexible data integration platform (CDR – Not EDC.) Need to
handle Imaging, wearable and sensor data.
• Leverage direct data capture when possible: no SDV, no queries
(eCOA, e-Source, etc.)
• Use objective data (Imaging, Sensor, Wearable, Diagnostic
Equipment, etc.) for better reliability and chance of study success
• Need Robust Predictive Analytics and visualizations for RBSE
• Centralize, share and report study issues (e.g. IMPACT® CTMS)
• Flexible tool to support Adaptive Design
DATA
MANAGEMENT
TOOLS

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HOW DOES PERCEPTIVE MYTRIALS® ANALYTICS HELP?
Platform that leverages the power of data to drive informed and proactive decisions
Clinphone®
RTSM
DataLabs®
EDC
Project
Manage-
ment
Rave
InForm
IxRS
IMPACT®
CTMS
D
a
t
a
L
a
y
e
r
Central
Cross-study
Insights
(CDR)

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CENTRAL CROSS-STUDY INSIGHTS
Powered by Perceptive MyTrials® Analytics
Central
Cross-study
Insights
(CDR)
Sensor
Analytics
Site and
Country
Selection
Patient Data
(Safety) Review
Data Driven
Monitoring
Study / CRO
Oversight

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CENTRAL ISSUE MANAGEMENT
Powered by Perceptive MyTrials® Analytics
Central
Issue
Management
(CTMS)
Sensor
Analytics
Site and
Country
Selection
Patient Data
(Safety) Review
Data Driven
Monitoring
Study / CRO
Oversight

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Conclusion
• Data Management Role – Holistic – More complex – Prevent
instead of correct – Able to manage risks
More rewarding and visible role
• Process – risk based – Focused on what matters
Focused on subject protection and the reliability of
study results
• Technology – Not EDC Centric (e.g., CTMS, sensor, etc.)
• Technology must assist finding the needle in the hay stack
Need to ease and speed-up process while reducing
cost and improving quality

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QUESTIONS?
Patrick Nadolny, VP, Product Management
PAREXEL International
Patrick.Nadolny@PAREXEL.COM