Watch the webinar here: http://www.screencast.com/t/0lATKYlJ8 Dr. Chris Boone, then-VP in Avalere’s Evidence Translation and Implementation Practice, discussed clinical trial data transparency and considerations for governance and open data sharing. Clinical trials are extremely valuable as the primary data source for seeking regulatory approval of products. Historically, regulatory agencie have been the sole recipients of clinical trial data, butthere has been a recent push from various stakeholder groups to open access to clinical trial data to non-regulatory researchers as an act of ethical responsibility to patients, a contribution to public health, and a demonstrated commitment to advancing the science. Some of the barriers include developing a sound approach for de-identifying patient data, adopting universal clinical trial data format, and managing the proactive and non-selective access and security of clinical data once collected. Dr. Boone discusses rationales and benefits/risks of clinical trial transparency, responsible use of publicly sharing this data, barriers and legal implications, and reasonable data sharing models. Discover more health data resources on our website at http://www.healthdataconsortium.org/

1. HDC Webinar Series
Introduced by Dwayne Spradlin, CEO, Health Data Consortium
Clinical Trial Data Transparency:
Explaining Governance for Public Data Sharing
Chris Boone, Avalere Health LLC

2. Clinical Trial Data Transparency
Explaining Governance Considerations for Public
Data Sharing
Chris Boone

3. Objectives and Outputs for Today’s Webinar
3
PROJECT OVERVIEW
Avalere recognizes the current landscape and the drive of data transparency.
This session provides an overview of the clinical trial data transparency
discussion in the EU and discusses some of the key barriers to effectively
governing the process for collecting, storing, securing, authorizing access, and
auditing the data from a regulatory perspective.
MEETING OBJECTIVES
● Review the rationales for and benefits/risks of clinical trial transparency
● Discuss responsible use of public data sharing
● Analyze key barriers to overcome with data collection and standardization
● Describe legal and policy implications allowing public access to data
● Identify and describe reasonable data sharing models

4. Overall Approach To The Project
4
AVALERE FOLLOWED A FOUR-STEP PROCESS TO COMPLETE THE RESEARCH
Develop
Research
Strategy
Summarize
and Report
Key Findings
Consult
Internal
Avalere
Experts
Conduct a
Literature
and Policy
Review
Define:
 Criteria for
structured
search of
white and
grey
literature
 Develop
outline
 Consult resident
Avalere experts
 Use expert
insights to guide
targeted
literature review
 Identify
credible
sources that
discuss the
EU regulatory
framework
and its history
 Address data
infrastructure
needs
 Summarize key
findings across
each domain in
final report
 “White paper”
provides
highlights in
consolidated
format

5. Key Definitions for Today’s Discussion
5
● IPD: Individual participant data
● CSR: clinical study reports
● Data sharing: “the responsible entity making the data available via open or restricted
access, or exchanged among parties”
● Data generator: “may include industry sponsors, data repositories, and researchers
conducting clinical trials”
● Data holder: “to mean the entity or entities that have access to data, including
regulatory agencies, journals to which manuscripts are submitted, and other
repositories such as ClinicalTrials.gov in the US”
● Intervention: “a process or action that is the focus of a clinical trial. This can include
giving participants drugs, medical devices, procedures, vaccines, and other products
that are either investigational or already available.”

6. Background

7. Context of the Study
7
● Clinical trials are essential to determining the safety of medical interventions and
their ability to achieve particular health outcomes
● Clinical trials are required by regulatory authorities around the world before a new
medical product can be brought to market, or before a new indication, formulation,
or target population can be approved for an intervention already on the market
● Vast amounts of data are generated over the course of a clinical trial – held by
sponsors
● Data from clinical trials, including participant-level data, are being shared by
sponsors and investigators more widely than ever before
● Some sponsors have voluntarily offered data to researchers, some journals now
require authors to agree to share the data underlying the studies they publish
● For more than a decade, policymakers have sought to expand public access to
information about planned, ongoing, and completed clinical trials
● The EMA has proposed the expansion of access to data submitted in
regulatory applications

8. 8
“Science is built on data: its collection,
analysis, publication, reanalysis,
critique, and reuse. However, the
current system of scientific publishing
works against maximum dissemination
of the scientific data underlying
publications. Barriers include inability to
access data, restrictions on usage
applied by publishers or data providers,
and publication that is difficult to
reuse…”
“Science is built on data: its collection,
analysis, publication, reanalysis,
critique, and reuse. However, the
current system of scientific publishing
works against maximum dissemination
of the scientific data underlying
publications. Barriers include inability to
access data, restrictions on usage
applied by publishers or data providers,
and publication that is difficult to
reuse…”

9. Clinical Trial Data
Transparency
Less Flexible More Flexible
Clinical Trial Data
Transparency
Less Flexible More Flexible
There is an Ongoing Push for Greater Transparency of Clinical Trial
Data in Europe and the United States
9
Increasing
Sharing
Requirement
s
Increasing
Sharing
Requirement
s
Limited
Sharing
Initiatives
and Offers
Limited
Sharing
Initiatives
and Offers
REGULATORS MUST WEIGH THE RISKS AND BENEFITS OF SHARING CLINICAL TRIAL DATA
EMAEMA FDAFDA
UK House
of
Commons
UK House
of
Commons
PhRM
A
PhRM
A
EFPIAEFPIA
European
Alliance for
Personalize
Medicine
European
Alliance for
Personalize
Medicine
IOMIOM
Individual
Companies/
Institutions
Individual
Companies/
Institutions
Regional National
EFPIA: European Federation of Pharmaceutical Industries and Associations
EMA: European Medicines Agency
IOM: Institute of Medicine
PhRMA: Pharmaceutical Research and Manufacturers of America

10. Growing pressure from academic groups and patient-consumer
advocates to release clinical trial data led to EMA decision
10
EMA TAKES THE FOLLOWING VIEWS AND POSITIONS:
● Enabling public scrutiny and secondary
analysis of CTs
● Protection of personal data (PPD)
● Respect for the boundaries of patients’
informed consent
● Protection of commercially confidential
information (CCI)
● Ensuring future investment in bio-
pharmaceutical research and
development (R&D)
● Addressing the consequences of
inappropriate secondary data analysis
● Protecting the Agency’s and the European
Commission’s deliberations and decision-
making process
● Ensuring that transparency is a two-way
street

11. Timeline* of Developments Related to Clinical Data Transparency
in Europe
● Prior to 2010, EMA released clinical trial reports upon request. It is now working to finalize its policy on
publishing clinical trial data proactively once the decision-making process on an application for a EU-wide
market authorization is complete
● As a separate but concurrent initiative, EMA is working to make summaries of the results of clinical trials
publicly available through the EU Clinical Trials Register
● In December 2013, Council of the European Union released the revised Proposal for Regulation on Clinical
Trials (aimed at repealing Directive 2001/20/EC). If approved by the European Parliament in 2014, each EU
member state will automatically be subject to the regulation and new requirements for public transparency of
clinical trials’ results
● There have also been national debates about data transparency:
● For example - U.K.: House of Commons ordered a report Access to clinical trial information and the
stockpiling of Tamiflu (Dec 18 203)
● Europe is more pro-active than the U.S., and works within a different legal construct, but biopharma data is
often based on global development activities
Mar 19-20 2014
Discussion about
EMA Policy at
EMA Management
Board Meeting
Dec 12 2013Jun 24 2013
11
*Timeline not to scale.
Jul 17 2012
European
Commission
adopts proposal to
replace Clinical
Trials Directive of
2001
EMA releases
draft Policy 70 for
a 3-month public
consultation
EMA releases key
principles
Council of EU
releases proposal
on sharing of
clinical trial data
Dec 17 2013 May 22 2014
EU Elections

12. There are some Potential Benefits of Sharing Clinical Trial Data…
12
“CLINICAL TRIAL DATA IS NOT COMMERCIAL CONFIDENTIAL INFORMATION” – EMA’S
POSITION
Mello et al. 2013. “Preparing for responsible sharing of clinical trial data.” N Engl J Med 369(17):1652.

13. … but there are some Risks or Unintended Consequences as well.
13
o Patient Privacy: It is difficult to effectively de-identify certain data –
especially data from trials concerning rare diseases or other small trials
o Poorly Conducted Analyses: Could also lead to unskilled analysts,
market competitors, or others with strong private agendas to publicize
poorly conducted analyses
o Dis-incentivizes future research: Could affect incentives to invest in
research to develop new medical products.
o Operational Costs: Could potentially administratively burdensome to
operate since regulatory agencies are already overwhelmed
1
2
3
4

14. Clinical Data Management

15. What is Clinical Data Management (CDM)
15
DEFINED AS “THE DEVELOPMENT, EXECUTION AND SUPERVISION OF PLANS,
POLICIES, PROGRAMS AND PRACTICES THAT CONTROL, PROTECT, DELIVER AND
ENHANCE THE VALUE OF DATA AND INFORMATION ASSETS” IN THE CLINICAL
TRIAL ARENA
CDM Domains
Data
Governance
Data
Architecture
Database
Management
Data Security
Management
Data Quality
Management
Reference and
Master Data
Management
Data
Warehousing
and Business
Intelligence
Metadata
Management
Lu et al. 2010. “Clinical data management: Current status, challenges, and future directions from industry perspectives.” Open
Access Journal of Clinical Trials 2:93-105.

16. Evolution of Clinical Data Management
16

17. What Data is Shared?
17
● Depending on the regulatory jurisdiction, data might or might not be shared or made available
to the public for secondary uses
● Shared data might include both summary data and individual patient data
● In the US, if a sponsor is seeking regulatory approval, data are shared in confidence with
regulators
● In the EU, the EMA has undertaken regulatory action to share anonymized clinical trial data
with external requestors.
● Select study data might also be made available to individual researchers on a case by case
basis upon request, or could be made publicly available, usually at the summary level, for
example, through publication in a peer-reviewed journal or through publicly accessible clinical
trial registration sites (e.g. ClinicalTrials.gov)
● Data not shared includes:
o Analyzable data sets
o Clinical study reports (CSRs)
o Individual participant data (IPD)

18. Current Practices in Data Disclosure
18
● Publication in peer-reviewed scientific journal is currently the primary
method
o Scientific journal articles generally contain a brief summary of the trial
background, research question(s), methodology, results, figures and
tables, and discussion
● Clinical trial sponsors seeking regulatory approval from authorities such as
the FDA and the EMA must submit detailed CSRs and IPD as required,
which forms the basis of the marketing application for a product
● Case-by-case basis upon request
● Discussions have not been specific regarding which datasets might be
shared

19. Providers and Recipients of Shared Data
19
PROVIDERS
● Individual participants in a clinical trial
● Clinical trial funders (e.g. govnerment,
industry, foundations, or advocacy
organizations)
● Contract research organizations
● Principal investigators or their institutions
● Site principal investigators of a multisite
trial
● Data coordinating center
● Regulatory agencies
● Systematic reviewers and guideline
developers
RECIPIENTS
● Individuals participating in the trial
● Secondary researchers
● Institutions supporting the researchers
● Funding agencies
● IRBs or scientific peer review committees
● The DSMB or DMC for another clinical
trial
● Educators
● A disease advocacy group seeking to
advance research
● Prospective plaintiffs or attorneys
● Competitors of the industry sponsor
● Members of the media
● Interested members of the public

20. Current Model for Clinical Trials Results Dissemination
20
Van Valkenhoef et al. “Deficiencies in the transfer and availibility of clinical trials evidence: a review of existing systems and
standards.” BMC Medical Informatics and Decision Making 2012, 12:95.

21. Four Possible Models for Expanded Access
21
Mello et al. 2013. “Preparing for responsible sharing of clinical trial data.” N Engl J Med 369(17):1656.

22. Alternative Model for Clinical Trials Results Dissemination
22
Van Valkenhoef et al. “Deficiencies in the transfer and availibility of clinical trials evidence: a review of existing systems and
standards.” BMC Medical Informatics and Decision Making 2012, 12:95.

23. Guidance Considerations for the Future of
Data Sharing

24. To minimize potential harms and maximize the public health with
increased sharing of clinical trial data, one must do the following:
24
● Respecting all promises made by trial investigators to study participants,
including individuals’ informed consent, as well as contracts and
agreements with institutions and research facilities;
● Safeguarding study participant privacy such that individual patients cannot
be identified through subsequent studies; and
● Ensuring the quality of data, including the appropriateness of the
availability, use and reliability of the data such that conclusions from
subsequent research are valid, and will not undermine current and future
therapies and their appropriate use in the interests of public health.
1
2
3

25. What is Data Quality?
25
“Data quality” has been broadly defined as “data strong enough to support
conclusions and interpretations equivalent to those derived from error-free
data

26. Primary Dimensions of Data Quality for Data Sharing
26
1. Data
Standardizatio
n and
Accessibility
2. Data Reliability
3. Data Use
• The processes through which the data is collected and
reported, including how the data should be collected, entered,
extracted, and transferred to avoid errors and ensure maximum
usability, and what data elements should be available to
researchers.
• Trustworthiness or whether the way in which data is made
available and subsequently used can be trusted and relied on
for future clinical research and healthcare decision making.
Ensuring reliability typically involves the validation, aggregation,
normalization, and/or auditing of the data sets, and assuring
that even good data has not be corrupted
• Denotes the applied methodologies to analyze the data, and
purposes that the data can be used for once it is made
available
WHEN ASSESSING DATA QUALITY, THREE THINGS ARE ROUTINELY CONSIDERED:

27. Data Standardization and Accessibility
27
ISSUES WITH DATA STANDARDIZATION AND ACCESSIBLITY MAY LIMIT THE ABILITY OF
SUBSEQUENT RESEARCH TO DRAW CONCLUSIONS
MAJOR FINDINGS
1.1: One component of achieving subsequently integrate-able data is defining what
constitutes proper collection and reporting of data in the first place. Common terms and
standards for data entry, collection, and transfer can enable standardized reporting.
1.2: It is important to note which data elements are available from a single study and
are the same data elements available from other studies (i.e., what is the level of
consistency of the data available across multiple studies).
1.3: Appropriate cross-study analyses require consistent data element accessibility
across initial data sets.
1.4: Another consideration is the current heterogeneity of clinical data management
(CDM) systems, software systems that assist in the process of collecting and managing
trial data, which can compromise the quality of data by hindering its exchange. Some
expect this lack of interoperability between the various systems to hinder the exchange
of trial data in Europe

28. Data Reliability
28
POOR DATA RELIABILITY HINDERS MEANINGFUL USE OF ALL DATA
MAJOR FINDINGS
1.1: For the processes of validation, normalization, and auditing to yield high quality
data that can be used in an interoperable manner among different European member
states, or between other stakeholders, standardized information technology solutions
need to be in place to facilitate them.
1.2: In response to this challenge, the European Clinical Research Infrastructure
Network (ECRIN) Working group on Data Centers recently developed standard
requirements through expert consensus. These standards specify the criteria “for high
quality [Good Clinical Practice] GCP-compliant data management in multinational
clinical trials.”
1.3: A good start to ensuring data integrity and the potential for appropriate public
health conclusions to be reached in secondary studies may be requiring that recipients
of data to be shared under the EMA’s publication and access to clinical trial data draft
policy certify that they will abide by these standards.

29. Data Use
29
LACK OF CLARITY IN RESEARCH PROTOCOLS MAY LIMIT EXPECTED GAINS FROM
INCREASED CLINICAL TRIAL DATA SHARING
MAJOR FINDINGS
1.1: Clear standards for reporting on methodology - for both those submitting clinical
trial data as well as those accessing it subsequently - help researchers reach
appropriate conclusions.
1.2: Protocols for secondary studies and analyses will also need to be made public and
be subjected to the same standards of methodological rigor as the original research, or
indeed, any other clinical research.
1.3: The worst case scenario is a flawed study impacting regulatory approvals – either
by allowing a truly ineffective or unsafe drug on the market or preventing a truly
effective or safe drug access to the market. For products already approved, flawed
studies can cause extreme confusion or unnecessary public anxiety, and even result in
drugs being wrongfully suspended or withdrawn from the market.

30. Noteworthy Strategies and Tools to Address Challenges
30
● Accountability for the release of the data
● Educational campaigns to support consumers and the general public
● Training for researchers
● Tools to facilitate informed consent
● Organize research/consensus meetings around clinical data standards
● Mechanisms to validate secondary research
● Outcomes assessments

31. Special Thanks to Project Team
PhRMA Team
• Jonathan Kimball, MA (Deputy Vice President, International Affairs)
Avalere Team
• Gillian Woollett, MA, DPhil (Senior Vice President, FDA Practice)
• Chris Boone, PhD, MSHA (Vice President, Evidence Translation & Implementation
Practice )
• Brenda Huneycutt, PhD, JD, MPH (Senior Manager, FDA Practice)
• Judit Illes, BCL/LLB, MS (Senior Associate, Evidence Translation & Implementation
Practice)
• Kathryn Jackson, PhD (FDA Fellow)
• Debbie Garner, MBA (Europe, Middle East and Africa Regional Director)
31