Transparency and objectivity in governance of clinical trials data sharing: Current practices and approaches

Shabani, Mahsa; Obasa, Mojisola

doi:10.1177/1740774519865517

Cited by 13 publications

(15 citation statements)

References 15 publications

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“…In 2015, following the publication of the IOM consensus study, Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk, Tudur Smith et al [20] published a set of good practice principles for data sharing, which emphasized controlled and secure access, participant consent and confidentiality, and a multistakeholder approach for supporting the required resources [1]. Additional publications since this time have included the development of data sharing principles for specific diseases such as the Alzheimer disease, critiques of data access review policies, and proposed strategies for implementing protected cloud-based methods of clinical trial data sharing [21][22][23]. In a more comprehensive critique on data sharing and the reuse of individual participant-level data from clinical trials, Ohmann et al [24] published a number of principles and recommendations that resulted from a multistakeholder consensus process.…”

Section: The Current State Of Clinical Trial Data Sharingmentioning

confidence: 99%

Data Sharing Goals for Nonprofit Funders of Clinical Trials

et al. 2021

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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. 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 participants’ networks for clinical trial recruitment. In short, nonprofit funders can drive policies and influence research culture. The purpose of this paper is to detail a set of aspirational goals and forward thinking, collaborative data sharing solutions for nonprofit funders to fold into existing funding policies. The goals of this paper 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.

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Section: The Current State Of Clinical Trial Data Sharingmentioning

confidence: 99%

Data Sharing Goals for Nonprofit Funders of Clinical Trials

et al. 2021

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“…Another individual motivation put forward by the original data collectors for sharing data was that of having transparency rules on the re-use and storage modalities of the shared datasets by data recipients. Transparency has been extensively discussed in many data sharing frameworks, often in the form of data availability statements for external researchers to confirm or refute the validity and test the reproducibility of certain research findings [ 30 – 32 ]. In contrast, our study shows that there is another dimension to transparency.…”

Section: Discussionmentioning

confidence: 99%

Individual notions of fair data sharing from the perspectives of Swiss stakeholders

Geneviève

Martani

Elger

et al. 2021

BMC Health Serv Res

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Background The meaningful sharing of health data between different stakeholders is central to the advancement of science and to improve care offered to individual patients. However, it is important that the interests of individual stakeholders involved in this data sharing ecosystem are taken into account to ensure fair data sharing practices. In this regard, this qualitative study investigates such practices from the perspectives of a subset of relevant Swiss expert stakeholders, using a distributive justice lens. Methods Using purposive and snowball sampling methodologies, 48 expert stakeholders from the Swiss healthcare and research domains were recruited for semi-structured interviews. After the experts had consented, the interviews were audio-recorded and transcribed verbatim, but omitting identifying information to ensure confidentiality and anonymity. A thematic analysis using a deductive approach was conducted to identify fair data sharing practices for secondary research purposes. Themes and subthemes were then identified and developed during the analysis. Results Three distributive justice themes were identified in the data sharing negotiation processes, and these are: (i) effort, which was subcategorized into two subthemes (i.e. a claim to data reciprocity and other reciprocal advantages, and a claim to transparency on data re-use), (ii) compensation, which was subcategorized into two subthemes (i.e. a claim to an academic compensation and a claim to a financial compensation), and lastly, (iii) contribution, i.e. the significance of data contributions should be matched with a corresponding reward. Conclusions This qualitative study provides insights, which could inform policy-making on claims and incentives that encourage Swiss expert stakeholders to share their datasets. Importantly, several claims have been identified and justified under the basis of distributive justice principles, whilst some are more debatable and likely insufficient in justifying data sharing activities. Nonetheless, these claims should be taken seriously and discussed more broadly. Indeed, promoting health research while ensuring that healthcare systems guarantee better services, it is paramount to ensure that solutions developed are sustainable, provide fair criteria for academic careers and promote the sharing of high quality data to advance science.

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“…Other studies of researcher reservations about data sharing report similar findings [ 6 , 23 ]. Having an access wall accompanied by a preview, rather than allowing for the unrestricted download of data, is one way of maintaining some level of control over how datasets may be reused [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the mode of data access depends greatly on the exact language used in the informed consent form signed by participants during trial enrollment and whether a platform devotes resources to perform data de-identification. However, as noted by Shebani et al, such controlled access models, especially if the models are administratively heavy and excessive in their requirements, can increase the burden placed on users and may dissuade them from requesting access to datasets [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Discoverability of information on clinical trial data-sharing platforms

Wilson¹,

Huser²

2021

jmla

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Objective: This study was intended to (1) provide clinical trial data-sharing platform designers with insight into users’ experiences when attempting to evaluate and access datasets, (2) spark conversations about improving the transparency and discoverability of clinical trial data, and (3) provide a partial view of the current information-sharing landscape for clinical trials.Methods: We evaluated preview information provided for 10 datasets in each of 7 clinical trial data-sharing platforms between February and April 2019. Specifically, we evaluated the platforms in terms of the extent to which we found (1) preview information about the dataset, (2) trial information on ClinicalTrials.gov and other external websites, and (3) evidence of the existence of trial protocols and data dictionaries.Results: All seven platforms provided data previews. Three platforms provided information on data file format (e.g., CSV, SAS file). Three allowed batch downloads of datasets (i.e., downloading multiple datasets with a single request), whereas four required separate requests for each dataset. All but one platform linked to ClinicalTrials.gov records, but only one platform had ClinicalTrails.gov records that linked back to the platform. Three platforms consistently linked to external websites and primary publications. Four platforms provided evidence of the presence of a protocol, and six platforms provided evidence of the presence of data dictionaries.Conclusions: More work is needed to improve the discoverability, transparency, and utility of information on clinical trial data-sharing platforms. Increasing the amount of dataset preview information available to users could considerably improve the discoverability and utility of clinical trial data.

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Transparency and objectivity in governance of clinical trials data sharing: Current practices and approaches

Cited by 13 publications

References 15 publications

Data Sharing Goals for Nonprofit Funders of Clinical Trials

Data Sharing Goals for Nonprofit Funders of Clinical Trials

Individual notions of fair data sharing from the perspectives of Swiss stakeholders

Discoverability of information on clinical trial data-sharing platforms

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