Discussion Framework for Clinical Trial Data Sharing: Guiding Principles, Elements, and Activities

Colleagues,

This is a very welcome initiative with both potentially beneficial gains and risk concerns!

If well regulated and co-funded by sponsors, this initiative can facilitate greatly the different Networks of Excellence, such as those funded by EDCTP in Africa, to support efficient data management, multi-level analysis and adequately powered evidence for multi-centre clinical trials.

It also facilitates expedited secondary data analysis of multiple databases across trial sites, countries, continents to generate adequately powered evidence for action and knowledge management.

There will be increased potential for capacity-building in secondary data analysis skills and utilization of trial data, especially critical in resource-poor settings.

There are caveats related to: intensive funds and resources involved; donor-indifference to funding such initiatives, inadequate regulatory framework against misuse, undue data mining with high risk of irrelevant/plausible 'significant' p-values and confidence intervals (spurious!) and the annoying non acknowledgement of the contributors of the raw data, especially in resource-poor settings etc.

How shall we guarantee and successfully minimized cyber-security risks involved? What will be the arrangements and potential sanctions/penalties for such abuse?

It is very important that IOM contributes to firm recommendations on the robust guidelines for data sharing; resilient regulatory framework; safe-guards/checks against potential breach of participants' confidentiality, intellectual property rights, cyber-security concerns, plagiarism, spurious/misleading analyses from large meta-analysis.

IOM should also contribute to higher level advocacy for co-funding these initiatives by interested sponsors. Profit making agencies must be charged to access and use raw data from the different sources.

I will share the current IOM draft report within our research/innovation networks and encourage their participation and contributions.

All best wishes!

Sharing data is very important now a days to promote clinial trial in low setting resources but other wise quality of data and stanarized data is important. So we have to put in our mind several issues for this training of scientist in low resources countries in data managements,standardized procedure for data setting , and try to compermise the research ethical issue and clarify research agreement and material transfer agreement incourge the funding of data mangement projects or data sharing and biobanking this it can solve the problem of payment and incentives

As someone who has spent a lot of hours producing secondary datasets of low-resource setting trial data for others' projects I would not underestimate the amount of time that this takes, creating meta-data for different specialised analyses that tend to get requested from large trial datasets, responding to queries etc - and then dealing with the governance around making sure reports/manuscripts get sent in with appropriate deadlines for approval, get forwarded to appropriate trial committees/funders etc, comments get sent back, responded to.

These are all administrative tasks, but without them it is difficult to see how the original researchers can be appropriate data custodians on behalf of original trial participants.

The tension of course is that mostly the original researchers would prefer to be doing these secondary analyses themselves, rather than preparing data for others to do this.

What isn't well thought through IMO with regard to non-commercial trials is the fact that most researchers working on trials are on fixed term contracts, with as little resource as possible to keep trial costs down because finances are always a stretch. When these people (Trial Manager, Statistician) leave at the end of the trial, is the next trial supposed to pick up the cost of supporting the previous trial? It's naive to think that there are not ongoing costs associated with data sharing (as above), but I don't see funders committing to long-term support to this. They seem to think that one can just post a simple dataset and that will be enough - in reality, the most interesting questions always arise from the much more complicated secondary data collected within a trial.

While data sharing is beneficial to the research community at large there is need for the researcher who collected the data to get direct benefit for reuse of this data,especially when new knowledge that would have impact on health care is generated.This would serve as an incentive for sharing of data.

The report touches on data ownership but there is no clear definition of who owns the data and this has a huge impact on effectiveness of guidelines and policies for data sharing.Going by the premise that the data is owned by the study participant, it is critical to ensure that the rights of the participants are protected. Consent should be explicitly sought from participants for sharing of their data while for retrospective data a framework to ensure ethical sharing of data and information dissemination back to the participants should be created.

All, I agree with the comments of Jim Todd that highlights the risk of low-income setting trials becoming a raw resource with value addition from further analysis taking place in more developed settings. As with many commodities from low-income settings such resources are typically exported for processing elsewhere.
In addition there is one broad risk related to benefit sharing or fairness that has not been mentioned as far as I can see. This is related to the fact that we tend to ignore that we operate within a relatively bounded space when it comes to resources for research - although the total available has improved it is not infinite. Inevitably therefore we have to consider the opportunity costs of 'new cost centres'.
So allocating resources to support data sharing and secondary analysis risks reducing funds available for primary research. This may not be so bad if the secondary research provides answers that are better or quicker than further primary research, perhaps especially for areas like safety of new products. However, there remains an imbalance in what research funds are spent on. I am not sure the numbers have been recalculated but I suspect the 10/90 gap remains; where 90% of the world's health problems receive 10% of the research funding. Funding for data sharing and shared analysis is likely to focus on those areas with all the research - in other words it too will focus on the needs of the 10% and potentially maintain or worsen the inequity in research funding allocation globally.
Linked to the ideas above I think most people appreciate that there is a danger from increased costs of additional regulation in that they make the primary research more expensive and may result in fewer trials being done especially in places that already have limited funding.
I would prefer to see a parallel focus on how to make conducting trials (in the broadest sense not just individual RCT) easier and cheaper and ones that focus on pragmatic designs in really representative settings. As someone who has worked on trying to translate research evidence into policy there is a considerable barrier to uptake of findings because of difficulties generalising findings to real life situations. Data sharing and re-analysis provide a very partial solution to this problem if the trials do not contain appropriate primary data.

I enjoyed reading the report. However, I think it's worth remembering that there are already excellent data sharing initiatives amongst those working in less well resourced settings outside the US and EU. Those of us running research in these settings endeavour to run these studies well and often have to employ different approaches because we work in low-resource settings and with vulnerable populations. We are already quite good at sharing because rare and neglected diseases necessitates collaboration. To share effectively, however, we need to get better at standardising the data collected or it is not much good for pooled analysis. Furthermore, any proposed solution needs to support research and not create expensive and burdensome barriers. I think most people would support the concept of data sharing, but it has to be made as easy for an independent academic investigator as it is for a pharma company.

Also the comment that 'most clinical trials are global in nature' may be true of trials originating in the US or EU, but is much less true of trials in resource-poor settings. A director of a large research organisation in Asia I can assure the committee that there is plenty of high quality, important work initiated and led within these regions. The IOM's report does not appear to acknowledge this fact; it seems to have been written very much from the perspective of US / EU sponsors.

Research is about digging out data and compile it into information so that others could learn from such findings.

Without sharing data, then it defeats the primary objective of
research whether academic or applied it is either to build to the body of knowledge or share new ideas and solve certain challenging issues.

Research is for public good and it should be shared, but research
costs money as such researchers should be rewarded accordingly so that they could put more effort in doing their work.

As much as there are challenges with data collection in low resource areas, the fact that low resource areas suffer impoverishness, communication barriers, illiteracy, poverty and the like, while designs inculcate all this into the project in My opinion I suggest that standalone easy to use computer gadgets as little as mobile fones, be used to collect this data onsite. This systems will require the user (locals trained on it) to follow step by step guide to collect this data.

We should incorporate the technology of GPS system in data collection, robots should be inculcated in design implementation for data collection in both in low and high resources areas. This will go a long way to reduce discrepancies as regards data collection in the areas.

E.g you take a picture, GPS TAGS THE LOCATION OF THE PICTURE AS IT IS POSTED ONLINE.

Theres the challenge of little or no supervision from agencies thus breaching the integrity of the data collected.

Lastly the issue of security of data collected both in the cloud and on ground. To What extent is there access globally. Hacking into cloud for the what reasons as is seen with NASA, Google, wiki leaks and the l

Experiences in sharing clinical datas between different research groups and different countries may have an unsuccessful end due to the lack of standardization of data recording, clinical perception, and data processing.
To avoid this, the prospective studies with standardization of the collection, interpretation, and analysis process are indicated.