scholarly journals Clinical trial data transparency and GDPR compliance: Implications for data sharing and open innovation

2020 ◽  
Author(s):  
Timo Minssen ◽  
Neethu Rajam ◽  
Marcel Bogers
Keyword(s):  
Clinical Trials ◽  
Data Sharing ◽  
Open Innovation ◽  
Clinical Trial Data ◽  
Public Access ◽  
General Data Protection Regulation ◽  
Policy Goals ◽  
General Data ◽  
Data Transparency ◽  
Privacy Issues

Abstract Recent European Union (EU) initiatives and legislation have considerably increased public access to clinical trials data (CTD). These developments are generally much welcomed for the enhancement of science, trust, and open innovation. However, they also raise many questions and concerns, not least at the interface between CTD transparency and other areas of evolving EU law on the protection of trade secrets, IPRs, and privacy. This article focuses on privacy issues and on the interrelation between developments in transparency and the EU’s new General Data Protection Regulation 2016/679 (GDPR). More specifically, this article examines: (1) the origins and rationales of EU transparency regulations, including the incidents and concerns that have shaped them; (2) the features and implications of the GDPR which are relevant in the context of clinical trials; and (3) the risk for tensions between the GDPR and the policy goals of CTD transparency, as well as implications for data sharing and open innovation. Ultimately, we elaborate on factors that should be carefully considered and addressed to reap the full benefits of CTD transparency.

Data sharing, international property practices and the GDPR: communicating with your consumers

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lucy Cradduck ◽  
Scarlett Stevens ◽  
Matthew Cowan
Keyword(s):  
New Zealand ◽  
Relevant Information ◽  
Public Access ◽  
European Economic Area ◽  
The European Union ◽  
Content Type ◽  
General Data Protection Regulation ◽  
The Public ◽  
Business Partners ◽  
General Data

PurposeThe purpose of this paper is to examine the requirements of the General Data Protection Regulation (“GDPR”) in order to: identify its requirements for the Australian and New Zealand based members of multi-national property firms (“MNPF”); and understand how those firms are currently engaging with customers regarding the obligations the GDPR imposes.Design/methodology/approachThe research was undertaken by means of doctrinal legal research that engaged with statutory law, related policy documents, accessible private firm documents and website materials, and academic and other related writings. The authors considered these in the context of the GDPR's requirements, and how relevant obligations were communicated to the public on the MNPF Australian and New Zealand members' websites.FindingsThe research confirms the available literature's observations of the GDPR's broad reach and the firms to which it applies. The difficulties experienced in locating relevant information highlights the need for a change to firm processes to ensure that any communication obligations are met. The cases engaged with also serve to highlight the need to ensure that the actual practice is consistent with required GDPR processes.Research limitations/implicationsThe research faced three limitations. First: there was a limited number of relevant Australian and New Zealand based property related firms available to consider: not all property related firms were members of a MNPF or had business partners or customers/clients in the European Union or European Economic Area. Second: one of the relevant firms had already identified it was withdrawing from the Australian market. Third: there was a lack of public access to all materials as, while privacy policies as required by domestic laws were readily accessible, access was not readily available to GDPR related or required information or documents.Originality/valueThe research adds to the academic literature in this emerging area of international legal obligation.

scholarly journals Clinical Trial Data Sharing for COVID-19–Related Research

10.2196/26718 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e26718
Author(s):  
Louis Dron ◽  
Alison Dillman ◽  
Michael J Zoratti ◽  
Jonas Haggstrom ◽  
Edward J Mills ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Data Sharing ◽  
Statistical Power ◽  
Meta Analysis ◽  
False Negative ◽  
Clinical Trial Data ◽  
Control Group ◽  
Scientific Cooperation ◽  
Substantial Impact

This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

scholarly journals Clinical Trial Data Sharing for COVID-19–Related Research (Preprint)

2020 ◽  
Author(s):  
Louis Dron ◽  
Alison Dillman ◽  
Michael J Zoratti ◽  
Jonas Haggstrom ◽  
Edward J Mills ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Data Sharing ◽  
Statistical Power ◽  
Meta Analysis ◽  
False Negative ◽  
Clinical Trial Data ◽  
Control Group ◽  
Scientific Cooperation ◽  
Substantial Impact

UNSTRUCTURED This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

scholarly journals Biobanking between the EU and Third Countries — Can Data Sharing Be Facilitated via Soft Regulatory Tools?

2018 ◽  
Vol 25 (5) ◽  
pp. 517-536 ◽  
Author(s):  
Santa Slokenberga
Keyword(s):  
Data Sharing ◽  
Data Protection ◽  
Clinical Care ◽  
Research Capacity ◽  
Genomic Research ◽  
General Data Protection Regulation ◽  
Biobank Research ◽  
General Data ◽  
Legal Frameworks ◽  
The Eu

AbstractIn biobanking, collaboration and data sharing contribute to building genomic research capacity, and have the potential to further scientific advances that ultimately can result in advances in clinical care. However, in the absence of common applicable legal frameworks that enable collaboration, capacity building is hindered. With the applicability of the General Data Protection Regulation, the obstacles to data sharing which involve export of data from European Union Member States to third countries are expected to grow, rendering the collaboration between the EU and third countries even more challenging. This article examines how, if at all, data sharing in biobank research between the EU and third countries could be facilitated via the use of soft regulatory tools. It argues that although the existing soft tools might not in itself be suitable for meeting all the GDPR requirements, they could be the basis on which to raise the area-specific data protection bar globally.

scholarly journals Statement of Data Sharing Goals for Nonprofit Funders of Clinical Trials (Preprint)

2020 ◽  
Author(s):  
Timothy Coetzee ◽  
Mad Price Ball ◽  
Marc Boutin ◽  
Abby Bronson ◽  
David T. Dexter ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Sharing ◽  
Clinical Trial Data ◽  
Original Research ◽  
Data Sets ◽  
Research Participants ◽  
Starting Point ◽  
Disease Advocacy ◽  
New Research

UNSTRUCTURED Sharing clinical trial data can provide value to research participants and communities by accelerating the development of new knowledge and therapies as investigators merge data sets to conduct new analyses, reproduce published findings to raise standards for original research, and learn from the work of others to generate new research questions. As a voice for the perspective of participants in clinical trials, nonprofit funders – including disease advocacy and patient-focused organizations – play a pivotal role in the promotion and implementation of data sharing policies. Funders are uniquely positioned to promote and support a culture of data sharing by serving as trusted liaisons between potential research participants and investigators who wish to access these participant networks for clinical trial recruitment. In short, nonprofit funders can drive policies and influence research culture. The purpose of this statement is to detail a set of aspirational goals and forward-thinking, collaborative solutions to data sharing for nonprofit funders to fold into existing funding policies. The goals in this statement convey the complexity of the opportunities and challenges facing nonprofit funders and the appropriate prioritization of data sharing within their organizations and may serve as a starting point for a data sharing “toolkit” for nonprofit funders of clinical trials, to provide the clarity of mission and mechanisms to enforce the data sharing practices their communities already expect are happening.

scholarly journals The “A” of FAIR – As Open as Possible, as Closed as Necessary

2020 ◽  
Vol 2 (1-2) ◽  
pp. 47-55 ◽  
Author(s):  
Annalisa Landi ◽  
Mark Thompson ◽  
Viviana Giannuzzi ◽  
Fedele Bonifazi ◽  
Ignasi Labastida ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Protection ◽  
Personal Data ◽  
Health Data ◽  
Privacy Rights ◽  
Regulatory Requirements ◽  
Data Set ◽  
General Data Protection Regulation ◽  
Digital Era ◽  
General Data

In order to provide responsible access to health data by reconciling benefits of data sharing with privacy rights and ethical and regulatory requirements, Findable, Accessible, Interoperable and Reusable (FAIR) metadata should be developed. According to the H2020 Program Guidelines on FAIR Data, data should be “as open as possible and as closed as necessary”, “open” in order to foster the reusability and to accelerate research, but at the same time they should be “closed” to safeguard the privacy of the subjects. Additional provisions on the protection of natural persons with regard to the processing of personal data have been endorsed by the European General Data Protection Regulation (GDPR), Reg (EU) 2016/679, that came into force in May 2018. This work aims to solve accessibility problems related to the protection of personal data in the digital era and to achieve a responsible access to and responsible use of health data. We strongly suggest associating each data set with FAIR metadata describing both the type of data collected and the accessibility conditions by considering data protection obligations and ethical and regulatory requirements. Finally, an existing FAIR infrastructure component has been used as an example to explain how FAIR metadata could facilitate data sharing while ensuring protection of individuals.

scholarly journals Digital Enforceable Contracts (DEC): Making Smart Contracts Smarter

2020 ◽  
Author(s):  
Lu-Chi Liu ◽  
Giovanni Sileno ◽  
Tom Van Engers
Keyword(s):  
Data Sharing ◽  
Smart Contracts ◽  
Legal Requirements ◽  
General Data Protection Regulation ◽  
Architectural Analysis ◽  
Blockchain Technology ◽  
Ex Post ◽  
General Data ◽  
Enforcement Mechanisms

The combination of smart contracts with blockchain technology enables the authentication of the contract and limits the risks of non-compliance. In principle, smart contracts can be processed more efficiently compared to traditional paper-based contracts. However, current smart contracts have very limited capabilities with respect to normative representations, making them too distant from actual contracts. In order to reduce this gap, the paper presents an architectural analysis to see the role of computational artifacts in terms of various ex-ante and ex-post enforcement mechanisms. The proposed framework is assessed using scenarios concerning data-sharing operations bound by legal requirements from the General Data Protection Regulation (GDPR) and data-sharing agreements.

scholarly journals Sharing and reuse of individual participant data from clinical trials: principles and recommendations

BMJ Open ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. e018647 ◽  
Author(s):  
Christian Ohmann ◽  
Rita Banzi ◽  
Steve Canham ◽  
Serena Battaglia ◽  
Mihaela Matei ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Sharing ◽  
Task Force ◽  
Clinical Trial Data ◽  
Nominal Group ◽  
Individual Participant Data ◽  
Horizon 2020 ◽  
Wide Range ◽  
Individual Participant ◽  
Trial Participants

ObjectivesWe examined major issues associated with sharing of individual clinical trial data and developed a consensus document on providing access to individual participant data from clinical trials, using a broad interdisciplinary approach.Design and methodsThis was a consensus-building process among the members of a multistakeholder task force, involving a wide range of experts (researchers, patient representatives, methodologists, information technology experts, and representatives from funders, infrastructures and standards development organisations). An independent facilitator supported the process using the nominal group technique. The consensus was reached in a series of three workshops held over 1 year, supported by exchange of documents and teleconferences within focused subgroups when needed. This work was set within the Horizon 2020-funded project CORBEL (Coordinated Research Infrastructures Building Enduring Life-science Services) and coordinated by the European Clinical Research Infrastructure Network. Thus, the focus was on non-commercial trials and the perspective mainly European.OutcomeWe developed principles and practical recommendations on how to share data from clinical trials.ResultsThe task force reached consensus on 10 principles and 50 recommendations, representing the fundamental requirements of any framework used for the sharing of clinical trials data. The document covers the following main areas: making data sharing a reality (eg, cultural change, academic incentives, funding), consent for data sharing, protection of trial participants (eg, de-identification), data standards, rights, types and management of access (eg, data request and access models), data management and repositories, discoverability, and metadata.ConclusionsThe adoption of the recommendations in this document would help to promote and support data sharing and reuse among researchers, adequately inform trial participants and protect their rights, and provide effective and efficient systems for preparing, storing and accessing data. The recommendations now need to be implemented and tested in practice. Further work needs to be done to integrate these proposals with those from other geographical areas and other academic domains.

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