Conclusions Paper

Insights from the Clinical Trial Data Transparency Forum, April 2015

From Intention to ActionLessons learned from the early stages of sharing patient-level clinical trial data

ContentsIntroduction ........................................................................1

What About the Costs and Risks of Data Sharing? ....1

From Endorsement to Reality – the Way Forward ......2

How Big Pharma Does Data Sharing ............................2

Data sharing policy at Sanofi ...............................................2

Data sharing policy at Takeda .............................................2

Answering the Questions, Sharing What Works ........3

On what elements of process, policy and practice do we need consensus? .......................................................3

Who will review and approve/deny data requests?.......3

Who will fulfill the approved data requests? ...................4

How will you protect patient privacy? ...............................4

Establishing consistency across companies ....................5

Getting granular about the risks to patient privacy ........5

What are the special challenges of data sharing for small to midsize pharma? ..............................................6

Data Sharing Is Off and Running ...................................7

Then and Now – Kinks in the Process............................7

Lessons Learned................................................................8

Embrace transparency, even if you don’t want to. ..........8

Collaborate. .............................................................................8

Be prepared for the work of assessments and agreements. ....................................................................8

Educate, educate, educate. .................................................9

Do something, even if it’s imperfect for now. ..................9

Don’t equate access with value. .........................................9

For More Information .......................................................9

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IntroductionStakeholders across the life sciences industry have come to agreement that more open access to patient-level data is a good thing – good for science, good for business and good for humanity.

• Data sharing can lead to discovery of new trends and associations that generate new insights or hypotheses for further research.

• Data sharing enables objective, third-party review and validation of study results, thereby building public trust.

• Data sharing honors the valuable information provided by patients and researchers in previous clinical trials and extends the future value of their efforts.

Data sharing also enables broader research on a condition rather than on the efficacy of a single product. Take epilepsy, for example. For about 70 percent of patients with epilepsy, seizures can be controlled with drugs, but which one should be prescribed? There are more than 25 applicable compounds marketed under more than 30 brands.

Some were developed decades ago and are still effective, while newer ones offer more options to closely match the therapy to the patient. The right choice depends on the type of seizures, the patient’s age and other health conditions, and other factors specific to each patient, such as side effects, withdrawal time and delivery method. The ability to do comparative analysis across those 30 branded therapies – triangulating data from multiple studies and multiple organizations – can yield new knowledge that vastly improves quality of life for these people.

What About the Costs and Risks of Data Sharing?Until just a few years ago, discussions about sharing clinical trial data – particularly patient-level data with enough richness for secondary analysis – would have been framed in uncertainty and resistance. How much work is this going to be? What if we get flooded with data requests, or we can’t get our hands on the data? How can we balance the public good with the imperative for privacy? What happens if researchers reach different conclu-sions from our own – or reach false conclusions through bad science or malicious intent? Will we be scooped or scandalized by our own data?

These concerns, while real, can be mitigated – and meanwhile, government and industry forces have propelled transparency and data sharing to the forefront. The greatest push has been from the European Medicines Agency (EMA), which in 2012 committed to complete transparency regarding patient-level clinical data and study results. In 2014 the EMA announced that it would publish the clinical study reports (CSRs) contained in all applications for marketing authorizations submitted after Jan. 1, 2015. That’s now.

The pharmaceutical industry as a whole had already demon-strated support for sharing clinical trial data in July 2013 when the members of Pharmaceutical Research and Manufacturers of America (PhRMA) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) developed and endorsed Principles for Responsible Clinical Trial Data Sharing. These principles include commitments to:

• Enhance data sharing with researchers.

• Enhance public access to clinical study information.

• Share results with patients who participate in clinical trials.

• Certify procedures for sharing clinical trial information.

• Reaffirm the commitments to publish clinical trial results.

“EFPIA and its members are wholly supportive of sharing clinical trial information,” said Andy Powrie-Smith, EFPIA’s Communications Director. “The public expects it of us. The power of data is phenomenal, and we’re in an environment where expectations around data transparency are enormous. We will create incredible amounts of data that we have to harness for the benefit of patients, to develop new therapies that save people’s lives.

“At the same time, there’s a lot of work to do getting internal policies and procedures in place to align with new regulations. There has been a perception that transparency is an on/off button. Press the button and you’re transparent. It clearly isn’t like that. It’s a process, a journey where there’s a lot of learning still to do. At times the relationships have been tense or bumpy, but there is actually a good sense about the importance of clinical trial data sharing, and it has been massively moved up the agenda in recent years.”

Powrie-Smith was a keynote speaker at the fifth SAS-hosted Clinical Trial Data Transparency Forum, held in Heidelberg, Germany, in April 2015. The forum brought together 142 delegates from 63 organizations in 13 countries to advance that agenda in a truly open and collaborative way. Discussions

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focused on where the industry is now, how to address patient privacy issues, how small to midsize pharmaceutical companies can participate, and the experiences of researchers who are already using multisponsor data sharing environments.

From Endorsement to Reality – the Way ForwardA Deloitte survey conducted for the Harvard University Multi-Regional Clinical Trials (MRCT) Center a few months before the forum confirmed industry endorsement for the idea of data sharing. But there are a lot of questions to answer to get from concept to fruition – questions about process, policy and prac-tices – said Dr. Joanne Waldstreicher, Chief Medical Officer at Johnson & Johnson. For example:

• What studies and data should be shared, and what excluded?

• What’s the best way to combine data sets across multiple sponsors?

• What requirements should data requesters be expected to meet?

• Should there be an independent, central or federated body to manage the request process?

• How will patient privacy and confidentiality be protected, and who will do it?

• What will the request review process look like, and who makes the final decision?

• What terms and conditions should be included in data use agreements?

• What are the requirements for publication and data sharing of secondary analysis?

• How will stakeholders assess compliance/adherence to standards?

• How can we make participation feasible and attractive to data owners?

A year ago, these were questions open for wide debate. Now several industry bodies and large pharmaceutical companies have established policies, are moving forward, and are sharing their experiences so others can benefit from their groundwork.

How Big Pharma Does Data SharingData sharing policy at Sanofi“Sanofi has a commitment to follow the guidelines EFPIA and PhRMA have established,” said Dr. Pierre-Yves Lastic, Associate Vice President and Chief Privacy Officer at the world’s fifth-largest pharmaceutical company by prescription sales. “We are committed to publish the results, whether negative or not.”

The company’s policy states that Sanofi makes clinical trial data and full CSRs available from studies sponsored by the Sanofi group of companies – and from vaccine studies spon-sored by Sanofi Pasteur – that have been submitted to US and European Union regulatory agencies for products approved since 2014, where the requested trials have also been accepted for publication.

Requests for Sanofi data are handled by an independent review panel of publicly named scientists and health care professionals who are not employees of the company. The panel’s decisions are based on scientific merit. Does the proposed research have the potential to advance medical knowledge and contribute to the advancement of public health? Is it feasible, and is the research team suitably qualified? Since January 2014, approved requesters have then accessed the data through the multi-sponsor ClinicalStudyDataRequest.com website.

Sanofi will not share data with a third party where the trial participant did not give informed consent, where the proposed research is outside the bounds of consent, or where a participant could possibly be identified, such as with certain rare diseases.

Data sharing policy at TakedaTakeda is the leading pharmaceutical company in Japan and 16th-largest in the world, with a market presence in more than 70 countries worldwide. Integrity and transparency have been core values at Takeda throughout its 230-year history, so it was a natural progression to apply these core values to clinical trial patient-level data sharing, said Karen Devcich, Vice President of Global Medical Writing and Clinical Trial Data Transparency. “Data sharing is supported at the highest level in our organiza-tion. Our goal was to establish data sharing policies and be part of industry-leading solutions that would demonstrate Takeda’s clinical transparency leadership.”

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Takeda shares data from Takeda-sponsored Phase 1-4 inter-ventional trials that support commercially available new medi-cines or vaccine products for a given indication/formulation that have received one of the following marketing approvals since 2005:

• US and Europe approvals.

• Japan approval (when US or European regulatory submission is not planned).

• US or Europe approval (when submissions in both regions are not planned, such as due to license agreements).

The Takeda policy was carefully chosen so that for a given commercially available Takeda product approved since 2005, all interventional studies for that indication, regardless of phase, will be made available for patient-level data sharing. This enables researchers to have a more complete picture of the data for an approved medicine than would otherwise be possible.

With regard to which region’s approvals will trigger sharing, Devcich said that “as a global company rooted in Japan, it is important to Takeda to allow similar levels of access to data for our Japanese products to support our Japanese patient and researcher communities.”

Takeda chose to be part of the ClinicalStudyDataRequest.com multisponsor solution that includes a truly independent review panel managed by the Wellcome Trust. “It was also important to Takeda that this platform included the enhanced security and data sharing experience of the SAS® environment,” Devcich said.

At the same time as the data sharing initiative, Takeda also launched a new company clinical trial register that includes information from more than 1,000 clinical trials. Studies listed on TakedaClinicalTrials.com are linked directly to studies listed on ClinicalStudyDataRequest.com to greatly facilitate researcher access to Takeda patient-level data.

Answering the Questions, Sharing What WorksOn what elements of process, policy and practice do we need consensus?A few weeks before the SAS-hosted forum, delegates from industry, academia, government, nonprofits and patient groups met for a workshop hosted by the Harvard MRCT center, the Laura and John Arnold Foundation and the Wellcome Trust.

Participants discussed long-term vision for sharing patient-level data and identified six core features or principles necessary to make it work:

1. Organizational structure – A centralized, international, not-for-profit organization responsible for a coordinated data sharing initiative.

2. Centralized and single portal – A central user interface with a robust search engine, including information on trials around the world.

3. Governance – A not-for-profit, central, multi-stakeholder body with authority and accountability to enable the long-term vision and oversee the data sharing enterprise from end to end.

4. Data requirements – Sufficient data ontogeny, data definition and metadata to support integration of disparate data sets for analysis.

5. Shared or common services – Efficient shared or common services across data creators and sponsors (policy setting, data de-identification and criteria for independent review panel decisions).

6. Flexibility – A data platform that accommodates different expectations and research needs, including the ability to download data if available and the ability to host data for those who do not wish to do so themselves.

Who will review and approve/deny data requests?Various approaches to the review process exist, said Karla Childers, Director of Strategic Projects at Johnson & Johnson. In some cases, individual companies respond to requests and make their own decisions about sharing. Others appoint an independent, third-party review panel. Others follow a hybrid model, where requests go first to a review panel of the original study sponsor, and only denied requests are escalated to an external panel.

“Can we envision a system that uses a completely independent, third-party custodial [entity] managing the process across a range of sponsors and stakeholders?” asks Childers.

That does seem to be the direction, and there’s a lot of merit to it, said Jennifer O’Callaghan, Clinical Data Sharing Manager at the Wellcome Trust. It’s the approach recommended in the Trust-commissioned Technopolis Group report. An indepen-dent review panel provides impartial scientific and ethical review of access requests to protect patient confidentiality,

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ensure appropriate secondary uses of the data, review scien-tific and analytical robustness of research proposals, assess the competence of the data requester and maintain public trust.

Since January 2014, the Janssen pharmaceutical division of Johnson & Johnson has been using the Yale Open Data Access (YODA) Project as the independent review panel for all requests for access to data. The YODA Project reviews and makes deci-sions on all requests and research proposals received for access to Janssen data. Policies and procedures for this process are posted on its portal at yoda.yale.edu.

Researchers browse the YODA Project Portal to see what trials are available and then submit research proposals directly to the YODA Project portal. “Yale contacts and coordinates communications with requesters,” said Jeff Gardner, Head of Process and Technology for Data Transparency at Janssen. “The YODA Project independently communicates with researchers, approves or declines research proposals, and manages researcher access to data on the SAS clinical trial data sharing platform.

“Our primary responsibility within the YODA request process is to make the data available and load it to the SAS platform. By our interpretation, the researchers really want to work indepen-dently with the data. We generally don’t have direct contact with the researcher or even know the researcher’s identity until it is publicly posted on the YODA site.”

Who will fulfill the approved data requests?For many, this is a big question, because it’s not a trivial effort. After the data set and documents have been located, it takes days to remove personal information, anonymize the data as appropriate for the release, check the proposal and load the data into the access system – applying quality control processes along the way. The process could be twice as long for a large or historical study with complex requirements and multiple editions of the data, or if the researcher has lots of questions about the data.

Should these tasks be performed by clinical research teams, a dedicated data sharing team or an outsourced provider? It depends, said Gardner. How many requests are coming in, and how many studies are included in each request? What internal resources are available, and what is their day-to-day workload? Are the requests distributed across multiple therapeutic areas or clustered around just a few?

There are pros and cons to tasking clinical trials teams to do this. On the plus side, clinical teams are the experts in the data and documentation for their trials. They can respond to small and infrequent requests, and they would be productive on other work when no data requests are coming in. However, there’s a learning curve to bring them up to speed on privacy issues, Gardner noted. Furthermore, data requests could also cause wide variation in workload from month to month and interfere with critical submission deadlines.

A dedicated internal or external team assigned to fulfilling data requests can bring data privacy expertise and the capacity to handle large, complex and frequent requests. When no requests are coming in, this team could be proactively preparing data for studies that have not yet been requested.

Janssen started with the first approach and is evolving to the second, said Gardner. “Before the launch, we had assumed two trial requests per month, with an average of one to three trials per request. We expected this level of effort could be supported by our existing clinical trial teams. In actuality, in the first six months, we received 13 research proposals that required de-identification/redaction of 42 clinical trials – up to 18 studies for one request.

“So we decided to look at a more dedicated model, and we’re moving in that direction. We partnered with a vendor that has been doing an excellent job for us. For example, they turned around a request in about five weeks that would have taken us about five months with internal resources. As you get momentum and start seeing more and more requests coming in, you can start thinking about a dedicated/vendor model.”

How will you protect patient privacy?This is not a one-size-fits-all proposition, said Lastic of Sanofi. “As you increase the sharing circle size, you lose control. Even within the company, not all of these people should have access to this clinical trial data.”

Lastic described a stair-stepped approach to managing the data, with added protections at each step as the audience gets broader or is external to the company. For example, clinical trial and clinical development teams can get pseudonymized data (which still has a patient identifier to link back to the patient if necessary) and role-based data access on a need-to-know basis. Appropriate use is governed by employment contracts and nondisclosure agreements. Compliance is assured through procedures, training and audit.

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Privacy protections get more stringent as the circle expands. Collaborative project partners and external contract researchers get anonymized data and are bound by server security practices, contractual clauses and audits. For broader external audiences, the data is protected by the strongest anonymization process, coupled with restrictions on which studies can be released.

Sanofi will release raw study data sets and analysis-ready data sets after passing through a de-identification process that removes the key identifiers defined by HIPAA privacy rules. For example, date of birth may be changed to age or age category, original dates changed to a time range, and freeform text scoured to remove terms that could possibly identify the patient. Other personally identifying information, such as inves-tigator and geographic location, is obscured, as well as genetic data rich enough to single out a person. Particular care is given to studies related to rare diseases, because of the higher risk of a privacy breach.

This de-identification process then receives final review and quality control checks, as well as testing through periodic re-identification attempts.

Establishing consistency across companiesFor secondary research using data from multiple sponsors, there’s the added issue of consistency across companies. If different companies use their own de-identification processes and standards, how does this affect research that combines their data? “We know how difficult it is to get [consistency] even within one organization,” said Jean-Marc Ferran, consultant and owner of Qualiance and Director of Special Projects at the independent Pharmaceutical Users Software Exchange (PhUSE). “Each company seems to be defining its own high-level guide-lines for data de-identification.”

A PhUSE working group of 25 representatives from phar-maceutical companies, contract research organizations (CROs), government and academia is working to develop peer-reviewed data de-identification standards for CDISC data models. The mission is to develop rules and rationale for protecting patient privacy, while preserving the utility of the data for analysis as much as possible.

De-identification standards for CDISC SDTM 3.2 were still being finalized at the time of the forum and are likely to evolve, said Ferran, who emphasized that the draft standards represent the recommendations of committee members and not their organi-zations. One thing the working group learned for certain during this process – it is difficult to consider all business cases, and consensus is not always simple to reach.

Getting granular about the risks to patient privacyOf course direct identifiers such as subject ID, Social Security number and address must be removed, masked or anony-mized. Ditto for quasi-identifiers – background information that could be combined to identify an individual. But to what degree do you then compromise the analytic utility of the data? If the intent is to mitigate the risk of identifying patients while preserving the value of the data for analysis, we need to get more granular about how each variable is handled.

“Contemporary standards and guidelines recommend a risk-based approach to de-identification,” said Khaled El Emam, Chief Executive Officer of Privacy Analytics, which produces data anonymization solutions. “Because we can precisely quantify the risks of re-identification associated with data disclo-sure, we can minimize the amount of distortion to data. When you don’t do too much – you don’t perturb the data more than is necessary –you can produce high-quality data even for public data releases, and in some cases even for rare diseases.”

El Emam showed a birth registry example, a chart that quanti-fied the probability of re-identification based on different combinations of variables, such as mother’s date of birth, baby’s date of birth, postal code, etc. The more variables, the higher the risk of a privacy breach, and certain variables have more influence on risk. The key point is that the risk of re-identifica-tion is complex and multilayered, a function of multiple factors. “You can manipulate the risk by changing or generalizing dates, locations and ages,” said El Emam. “As you apply these modifi-cations, you can change these risks.”

Mask and anonymize what is necessary to satisfy the risk threshold, just to the extent necessary based on the release context. “Is the data sensitive? Is there express consent? What is the potential harm of disclosure? We can measure the risks precisely and manipulate the risk through a well-defined process,” said El Emam.

He added that a risk-based approach is necessary for a global research ecosystem. “Clinical trial participants come from all over the world, and therefore whatever approaches are used have to meet the standards and expectations of privacy regula-tors in all the jurisdictions from which you’re collecting data. As a practical matter, a risk-based approach will meet that expecta-tion because it’s based on a best-practices approach to disclo-sure control.”

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What are the special challenges of data sharing for small to midsize pharma?“Smaller companies are not opposed to clinical trial data sharing – there is an openness – but there might be a little more understandable reluctance than with big pharma,” said Alexander Natz, Secretary General of the European Confederation of Pharmaceutical Entrepreneurs (EUCOPE). Smaller companies are caught in the middle, subject to the same regulatory forces and public perceptions as their giant counterparts, but with far fewer resources to get in the game and make it sustainable.

If you have only 20 or 30 staff members, where are the resources to staff a scientific review board, to curate the requested data, or to manage the contracts? These companies are often focused on simply getting into new markets; data sharing for the greater good is secondary. And a company that has only a few compounds in the market could naturally wonder, “What if external researchers use our data to apply for market approval in some other country?”

“We have to be pragmatic,” said Leslie Galloway, Chair of the Ethical Medicines Industry Group (EMIG), the UK trade associa-tion that represents the interests of more than 200 small-to-medium sized pharmaceutical, biotech and medical technology companies. “The companies EMIG represents may not have the in-house expertise. Most use CROs, and turnover of staff in CROs is quite high, so study-specific expertise may have been lost. Data sharing also represents significant cost and resource

Seven Steps to a Risk-Based De-Identification Process

1. Determine if anonymization is necessary.

2. Determine the desired state of the data.

3. Classify variables based on the risk of re-identification.

4. Evaluate context and assess risk.

5. Mask direct identifiers.

6. De-identify indirect identifiers.

7. Report and certify.

DataVerify Identify

of Data Recipient

Masking (of Direct

Identifiers)De-identification

Contractual Controls

Security & Privacy Controls

Not-PHI

Public Release of Anonymized Data No Yes High No None

Quasi-Public Release of Anonymized Data Yes Yes Medium-High Yes None

Non-Public Release of Anonymized Data Yes Yes Low-Medium Yes Low-High

PHI

“Protected” Pseudonymized Data Yes Yes None Yes High

“Vanilla” Pseudonymized Data Yes Yes None No None

Personal Data Yes No None None None

© 2015 Privacy Analytics, Inc.Figure 1. Risk-based data anonymization is tailored to variable type and release context.

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Then and Now – Kinks in the ProcessRemember the question of choosing the right epilepsy drug? Some of the more than 30 available medications will have adverse effects or not work for a given patient. Others will significantly improve quality of life. What factors determine which drugs will be best suited for which patient? Could a combination of direct and indirect evidence – triangulated across studies in comparative analysis – be used to better inform treatment choice?

That’s what Sarah Nolan wanted to know. The University of Liverpool researcher started with access to patient-level data from nearly 6,000 patients representing 29 existing studies – academic, pharmaceutical and government studies. The research team had requested and received data from another 18 studies (20 percent of that wave of requests could not be fulfilled because data from some older studies was not available in digital form).

There were still rich resources left to tap. Nolan identified 41 new studies representing more than 10,000 patients. She requested them all – 24 academic studies, 16 pharmaceutical studies and one government study. After more than two years of data requesting, only five requests were successful. Nolan got only 8 percent of the total data requested, representing only 660 patients.

The other requests stalled for years. Nolan would sometimes get an initially positive response, but no data ever arrived. Other times, she got no response to her emails, and didn’t even know if her requests had reached the right person.

“The consequence of this is that the project was completely delayed, with only six months left in the research funds,” Nolan said. “The problem was a lack of transparency in the data requesting process. There wasn’t a clear point to go to actually request data.”

That was then. Nolan shared more recent experience using the ClinicalStudyDataRequest.com site. “In June 2013, a data inquiry was submitted for data, and a year later, June 2014, the data was provided to us in the SAS data access system for analysis. We also made three other inquiries that were unsuc-cessful in 2013 and 2014. It’s not ideal; we would have preferred to have that data, but at least we got a response and a reason.”

implications. Small companies could fairly ask why they should foot the bill for providing data to researchers. They own the product and the data, but they don’t own the registry in which the data was developed, so that is an issue in access to the data.”

Data Sharing Is Off and RunningData sharing has definitely moved from concept to the real world. As of May 2015, ClinicalStudyDataRequest.com had received 133 research proposals as well as 204 inquires about studies not yet listed on the site – and provided data to more than 450 users to support 54 active research projects.

In the first six months, Janssen has received 13 research proposals approved by YODA, which required de-identification/redaction of 42 clinical trials. No proposals for Janssen have been declined to date.

Takeda has rolled out data sharing in waves – global products first, then single-region products, then alliance products. Within six months, Takeda had listed 343 studies as available for sharing. Data was requested for four studies, and all four were approved.

Roche has received 12 research proposals representing 55 studies. “Two of those inquiries were actually requests for docu-ments only,” said Kelly Mewes, Data Sharing Specialist. “We did not proceed with one request because the requirement to share tumor images was out of scope as per our data sharing policy. Another request is in feasibility assessment. Data has been shared with three requesters so far; those researchers are live on the SAS portal doing their research. All of our requests have come from academics from universities looking at new, novel research from our data. Five were multisponsor requests.”

}}“The benefit of doing an analysis like this is that you can form triangles, use the two edges, and bolster connections by using indirect evidence – triangulate in comparative analysis for compounds in a therapeutic area, in this case epilepsy.”– Sarah Nolan, researcher, University of Liverpool

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“One of the lessons we’ve learned is that compromise is key, given the huge spectrum of attitudes, from the conservative to the very liberal,” said O’Callaghan.

}}“There will always be cynics who will be reluctant to share anything, because it’s safer not to, but a cultural change is underway, and it’s the people with the experience who can share and help change that culture.”– Leslie Galloway, Chair, Ethical Medicines Industry Group (EMIG)

Collaborate.“It’s very important that we work together and avoid a fragmented system,” said O’Callaghan. “We need to have a more joined approach to clinical trial data sharing with academia, industry, the Institute of Medicine, EMA, ClinicalStudyDataRequest.com and YODA, so we’re all singing from the same hymn sheet.

“This collaboration needs to be globally representative, including developing nations. Clear written agreement is key between all the voices in the room, many of whom have very different understandings and interpretations of what we’re setting out to do.”

“We did a lot of external benchmarking, looked at what others were doing,” said Devcich of Takeda. “Then policy review; we reviewed lots of options against the benchmarking, and we gathered a lot of stakeholder internal feedback. We have come a long way from trying to set our policy to going forward with all our solutions and committees.”

Be prepared for the work of assessments and agreements.“We need to go compound by compound looking at in-scope criteria,” said Mewes from Roche. “That’s resource-intensive, so we’ve had to expand systems that weren’t designed for this. If you’ve got an electronic submission and you have everything in one package, that’s great. But if you don’t, it can be spread across 10 different systems, and that can be challenging. We thought the bottleneck would be the anonymization, but in fact the data sharing agreements have been the rate-limiting step so far.”

Multiple logins to get into the analysis environment are a little cumbersome, Nolan said, and she would really like to be able to download the data, not just analyze it in the cloud and download her derived data. Uploads have been speedy, but downloads have been variable. Data sharing agreements with some data providers outside the CSDR environment have prevented uploading all available data to the SAS environment. So it’s not a panacea, but it’s a lot better than it was.

“A multisponsor environment is a brilliant time saver,” said Nolan. “You don’t have to submit the same documents again and again to different companies. The steps on the website are very clear, and the process allows a good level of communica-tion between the data provider and the researcher. If all the companies have the same data structures and consistency in legal documents, that will save a lot of time.

“Having independent review panels is fair; they would see the science of the project above commercial self-interest. The process is detailed and encourages good science in the detail of your request. Legal documents have mutual benefit to the company and the researcher [because they specify rights to publish]. If I can’t publish this at the end with my name on it, I won’t get any more research money.”

Lessons LearnedEmbrace transparency, even if you don’t want to.“There will always be a difference between those who advocate for change and those who are responsible for delivering that change,” said Powrie-Smith. “In every sphere of life you have your agitators – you need those people to get the debate going – and you’ve got the people who are responsible for getting the change going.” Even an industrywide cultural shift won’t take root unless it is made meaningful at the individual level.

Changing the culture is especially challenging when the agitators and advocates aren’t at the top. “The position of our management group initially was, ‘We are here to develop new compounds; we are not here to develop transparency,’” said Lars-Peder Haahr from Lundbeck, a 100-year-old pharmaceu-tical company in Denmark. “But we had to do it, and we had to do it from scratch – covering the EFPIA part and the EMA part in the same policy that was announced in December 2014. We chose a single-sponsor solution, and that is a reflection of Lundbeck being a pretty conservative company.”

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}}“A fundamental priority at Johnson & Johnson is that there is value in it, that together we will advance science, the foundation of medical care, and together, we will make a difference in the world.”– Karla Childers, Director of Strategic Projects at Johnson & Johnson

For More InformationView on-demand recordings of all the presentations from the fifth SAS-hosted Clinical Trial Data Transparency Forum at sas.com/images/email/temp/corporate/45330.html.

Comments in this article represent a compendium of general discussion at the forum and not the opinion of any particular organization.

Educate, educate, educate.“Get your data sharing coordinator/team together and train together, learn together, so everybody can become the experts [taking the message companywide],” said Devcich. “We are on road shows all the time, educating people on the transparency policy. We’ll meet people who say they didn’t even know we have a clinical trial data sharing policy, even though they had been previously trained on it. So we’ve really increased our communication.”

Do something, even if it’s imperfect for now.“We’re better to have lessons learned from making mistakes and get on with it,” said O’Callaghan. “It’s okay to have an imperfect working model versus a perfect virtual model.”

“We need to show leadership – getting out there and putting systems in place, sharing data and seeing whether or not the sky falls in or it works okay,” said Powrie-Smith. Just get moving.

Don’t equate access with value.“Just because it’s online doesn’t mean it’s helpful,” said Childers. We shouldn’t measure the success of data sharing by how many proposals were received or approved, or how many terabytes of data shared.

“There should be a value in terms of advancing science and public medicine,” Childers emphasized. “We need to think creatively to ask the tougher questions. How are people using the systems? What has been the impact of sharing data? How can we tell it’s working? Have treatment guidelines changed? Have we affected public health? What measures should we be capturing now to be able to answer those questions? As leaders in R&D, we need to be thinking about how we’re going to answer these questions.

“If we have improved public health, we’ve seen the value – if we can do this in a way that preserves innovation, protects patient privacy and protects confidential information. We have to move forward. There is no reverse on this.”

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