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HDC Webinar Series
Introduced by Dwayne Spradlin, CEO, Health Data Consortium
Clinical Trial Data Transparency:
Explaining...
Clinical Trial Data Transparency
Explaining Governance Considerations for Public
Data Sharing
Chris Boone
Objectives and Outputs for Today’s Webinar
3
PROJECT OVERVIEW
Avalere recognizes the current landscape and the drive of da...
Overall Approach To The Project
4
AVALERE FOLLOWED A FOUR-STEP PROCESS TO COMPLETE THE RESEARCH
Develop
Research
Strategy
...
Key Definitions for Today’s Discussion
5
● IPD: Individual participant data
● CSR: clinical study reports
● Data sharing: ...
Background
Context of the Study
7
● Clinical trials are essential to determining the safety of medical interventions and
their abilit...
8
“Science is built on data: its collection,
analysis, publication, reanalysis,
critique, and reuse. However, the
current ...
Clinical Trial Data
Transparency
Less Flexible More Flexible
Clinical Trial Data
Transparency
Less Flexible More Flexible
...
Growing pressure from academic groups and patient-consumer
advocates to release clinical trial data led to EMA decision
10...
Timeline* of Developments Related to Clinical Data Transparency
in Europe
● Prior to 2010, EMA released clinical trial rep...
There are some Potential Benefits of Sharing Clinical Trial Data…
12
“CLINICAL TRIAL DATA IS NOT COMMERCIAL CONFIDENTIAL I...
… but there are some Risks or Unintended Consequences as well.
13
o Patient Privacy: It is difficult to effectively de-ide...
Clinical Data Management
What is Clinical Data Management (CDM)
15
DEFINED AS “THE DEVELOPMENT, EXECUTION AND SUPERVISION OF PLANS,
POLICIES, PROGR...
Evolution of Clinical Data Management
16
What Data is Shared?
17
● Depending on the regulatory jurisdiction, data might or might not be shared or made available
to...
Current Practices in Data Disclosure
18
● Publication in peer-reviewed scientific journal is currently the primary
method
...
Providers and Recipients of Shared Data
19
PROVIDERS
● Individual participants in a clinical trial
● Clinical trial funder...
Current Model for Clinical Trials Results Dissemination
20
Van Valkenhoef et al. “Deficiencies in the transfer and availib...
Four Possible Models for Expanded Access
21
Mello et al. 2013. “Preparing for responsible sharing of clinical trial data.”...
Alternative Model for Clinical Trials Results Dissemination
22
Van Valkenhoef et al. “Deficiencies in the transfer and ava...
Guidance Considerations for the Future of
Data Sharing
To minimize potential harms and maximize the public health with
increased sharing of clinical trial data, one must do the ...
What is Data Quality?
25
“Data quality” has been broadly defined as “data strong enough to support
conclusions and interpr...
Primary Dimensions of Data Quality for Data Sharing
26
1. Data
Standardizatio
n and
Accessibility
2. Data Reliability
3. D...
Data Standardization and Accessibility
27
ISSUES WITH DATA STANDARDIZATION AND ACCESSIBLITY MAY LIMIT THE ABILITY OF
SUBSE...
Data Reliability
28
POOR DATA RELIABILITY HINDERS MEANINGFUL USE OF ALL DATA
MAJOR FINDINGS
1.1: For the processes of vali...
Data Use
29
LACK OF CLARITY IN RESEARCH PROTOCOLS MAY LIMIT EXPECTED GAINS FROM
INCREASED CLINICAL TRIAL DATA SHARING
MAJO...
Noteworthy Strategies and Tools to Address Challenges
30
● Accountability for the release of the data
● Educational campai...
Special Thanks to Project Team
PhRMA Team
• Jonathan Kimball, MA (Deputy Vice President, International Affairs)
Avalere Te...
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Clinical Trial Data Transparency: Explaining Governance for Public Data Sharing

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/

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Clinical Trial Data Transparency: Explaining Governance for Public Data Sharing

  1. 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. 2. Clinical Trial Data Transparency Explaining Governance Considerations for Public Data Sharing Chris Boone
  3. 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. 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. 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. 6. Background
  7. 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. 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. 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. 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. 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. 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. 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. 14. Clinical Data Management
  15. 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. 16. Evolution of Clinical Data Management 16
  17. 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. 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. 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. 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. 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. 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. 23. Guidance Considerations for the Future of Data Sharing
  24. 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. 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. 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. 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. 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. 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. 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. 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

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