Sharing of clinical trial data and results reporting practices among large pharmaceutical companies: cross sectional descriptive study and pilot of a tool to improve company practices

Abstract Objectives To develop and pilot a tool to measure and improve pharmaceutical companies’ clinical trial data sharing policies and practices. Design Cross sectional descriptive analysis. Setting Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. Data sources Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. Main outcome measures Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. Results Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. Conclusions It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study’s measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.

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Predictors of clinical trial data sharing: exploratory analysis of a cross-sectional survey

Trials ◽

10.1186/1745-6215-15-384 ◽

2014 ◽

Vol 15 (1) ◽

pp. 384 ◽

Cited By ~ 14

Author(s):

Vinay K Rathi ◽

Kelly M Strait ◽

Cary P Gross ◽

Iain Hrynaszkiewicz ◽

Steven Joffe ◽

...

Keyword(s):

Clinical Trial ◽

Data Sharing ◽

Clinical Trial Data ◽

Trial Data ◽

Exploratory Analysis ◽

Cross Sectional Survey ◽

Cross Sectional

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Clinical Trial Data Sharing: A Cross-Sectional Study of Outcomes Associated with Two NIH Models

10.1101/2021.09.10.21263404 ◽

2021 ◽

Author(s):

Anisa Rowhani-Farid ◽

Alexander C. Egilman ◽

Audrey D. Zhang ◽

Cary P. Gross ◽

Harlan M. Krumholz ◽

...

Keyword(s):

Clinical Trial ◽

Data Sharing ◽

Clinical Trial Data ◽

Cross Sectional Study ◽

Trial Data ◽

Cross Sectional ◽

Shared Data ◽

Using Data ◽

Meta Analyses ◽

The Impact

AbstractBackgroundThe impact and value of clinical trial data sharing, including the number and quality of publications that result from shared data – “shared data publications” – may differ depending on the data sharing model used.MethodsWe characterized the outcomes associated with two data sharing models previously used by Institutes of the U.S. National Institutes of Health (NIH): NHLBI’s centralized model, which uses a repository to manage data sharing requests, and NCI’s decentralized model, which entrusted research groups to independently manage data sharing requests. We identified trials completed in 2010 that met NIH data sharing criteria and matched studies sponsored by each Institute based on cost or size, determining whether trial data were shared and the frequency of shared data publications.ResultsWe identified 14 NHLBI-funded trials and 48 NCI-funded trials that met NIH data sharing criteria. We matched 14 NCI-funded trials to the 14 NHLBI-funded trials; among these, 4 NHLBI-sponsored trials (29%) and 2 NCI-sponsored trials (14%) shared data. From the 2 NCI-sponsored trials sharing data, we identified 2 shared data publications, one per trial, both of which were meta-analyses. From the 4 NHLBI-sponsored trials sharing data, we identified 7 shared data publications, all using data from 1 trial, 5 of which were pooled analyses and 2 reported secondary outcomes.ConclusionWhen characterizing the outcomes associated with two NIH data sharing models, both the NHLBI and the NCI models resulted in only 21% of trials sharing data and few shared data publications. There are opportunities to optimize clinical trial data sharing efforts both to enhance clinical trial data sharing and increase the number of shared data publications.

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Clinical Trial Data Transparency and GDPR Compliance: Implications for Data Sharing and Open Innovation

SSRN Electronic Journal ◽

10.2139/ssrn.3413035 ◽

2019 ◽

Author(s):

Timo Minssen ◽

Rajam Neethu ◽

Marcel Bogers

Keyword(s):

Clinical Trial ◽

Data Sharing ◽

Open Innovation ◽

Clinical Trial Data ◽

Trial Data ◽

Data Transparency

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Can research ethics committees enable clinical trial data sharing?

Ethics Medicine and Public Health ◽

10.1016/j.jemep.2017.02.010 ◽

2017 ◽

Vol 3 (1) ◽

pp. 56-63 ◽

Cited By ~ 1

Author(s):

A. Thorogood ◽

B.M. Knoppers

Keyword(s):

Clinical Trial ◽

Research Ethics ◽

Data Sharing ◽

Clinical Trial Data ◽

Ethics Committees ◽

Trial Data ◽

Research Ethics Committees

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Regulatory frameworks for clinical trial data sharing during the Pandemic and beyond: A scoping review (Preprint)

10.2196/preprints.33591 ◽

2021 ◽

Author(s):

Nachiket Gudi ◽

Prashanthi Kamath ◽

Trishnika Chakraborty ◽

Anil G. Jacob ◽

Shradha Parsekar ◽

...

Keyword(s):

Clinical Trial ◽

Data Sharing ◽

Scoping Review ◽

Data Extraction ◽

Grey Literature ◽

Clinical Trial Data ◽

Trial Data ◽

Policy Documents ◽

Regulatory Documents ◽

Share Data

BACKGROUND Data sharing from clinical trials is well recognized and has widely gained recognition amid the COVID-19 pandemic. The competing interests of powerful stakeholders expressed through data exclusivity practices make clinical trial data sharing a complex phenomenon. The wider acceptance of data sharing practices in the absence of mandated policy creates uncertainty among trial investigators to count for risks vs benefit from sharing trial data. Data sharing becomes further complex as the trial data sharing is governed by the regional policies. This drew our attention to explore policies for informed data sharing. OBJECTIVE This scoping review aimed to map the existing literature around the regulatory documents that guide trial investigators to share clinical trial data. METHODS We followed a Joanna Briggs Institute scoping review approach and have reported the article according to the PRISMA extension for Scoping reviews (PRISMA-ScR). In addition to the use of the electronic databases, a targeted website search was performed to access relevant grey literature. The articles were screened at the title-abstract and the full text stages based on the selection criteria. All the included articles for data extraction were in English language. Data extraction was done independently using a pre-tested data extraction sheet. Included literature focused on clinical trial data sharing policies, guidelines, or SOPs. A narrative synthesis approach was used to summarize the findings. RESULTS This scoping review identified four articles and 13 policy documents from the grey literature. A majority of the clinical trial agencies require an agreement for data sharing between the data requestor/organization and trial agency. None of the policy documents mandates informed consent for data sharing. The time interval to share data underlying results, varies from six to 18 months from the time of trial publication. Depending upon trial data, policies follow both controlled and open access models. Regulatory documents identified in both scientific and grey literature emphasized on good research principles of protection of privacy of participant data and data anonymization through data sharing agreement between the data requester and trial agency. Need for an informed consent and cost of data sharing, timeline to share data, incentives, or reward to promote data sharing and capacity building for data sharing have remained grey areas in these policy documents. CONCLUSIONS This paper acknowledges the vital role of clinical data sharing from a public health perspective. We found that given the challenges around clinical trial data sharing, developing a feasible mechanism for data sharing is important. We suggest that standardizing data sharing processes by framing a concise policy with key elements of data sharing mechanisms could be easier to practice rather than a rigid and comprehensive data sharing policy. CLINICALTRIAL This scoping review protocol has not been registered and published.

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How Clinical Trial Data Sharing Platforms Can Advance the Study of Biomarkers

The Journal of Law Medicine & Ethics ◽

10.1177/1073110519876165 ◽

2019 ◽

Vol 47 (3) ◽

pp. 369-373 ◽

Cited By ~ 2

Author(s):

Rebecca Li ◽

Ida Sim

Keyword(s):

Clinical Trial ◽

Data Sharing ◽

Biomarker Discovery ◽

Clinical Trial Data ◽

Trial Data ◽

Data Types ◽

Current State ◽

Biomarker Research ◽

Diverse Data

Although data sharing platforms host diverse data types the features of these platforms are well-suited to facilitating biomarker research. Given the current state of biomarker discovery, an innovative paradigm to accelerate biomarker discovery is to utilize platforms such as Vivli to leverage researchers' abilities to integrate certain classes of biomarkers.

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Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume I—conceptual challenges

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-001389 ◽

2020 ◽

Vol 8 (2) ◽

pp. e001389 ◽

Cited By ~ 1

Author(s):

Sergio Rutella ◽

Michael A Cannarile ◽

Sacha Gnjatic ◽

Bruno Gomes ◽

Justin Guinney ◽

...

Keyword(s):

Clinical Trial ◽

Data Sharing ◽

Clinical Trial Data ◽

Trial Data ◽

Data Sets ◽

Private Industry ◽

Stakeholder Groups ◽

Professional Societies ◽

Immunotherapy Of Cancer ◽

Biomarker Data

The sharing of clinical trial data and biomarker data sets among the scientific community, whether the data originates from pharmaceutical companies or academic institutions, is of critical importance to enable the development of new and improved cancer immunotherapy modalities. Through data sharing, a better understanding of current therapies in terms of their efficacy, safety and biomarker data profiles can be achieved. However, the sharing of these data sets involves a number of stakeholder groups including patients, researchers, private industry, scientific journals and professional societies. Each of these stakeholder groups has differing interests in the use and sharing of clinical trial and biomarker data, and the conflicts caused by these differing interests represent significant obstacles to effective, widespread sharing of data. Thus, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to identify the current barriers to biomarker data sharing in immuno-oncology (IO) and to help in establishing professional standards for the responsible sharing of clinical trial data. The conclusions of the committee are described in two position papers: Volume I—conceptual challenges and Volume II—practical challenges, the first of which is presented in this manuscript. Additionally, the committee suggests actions by key stakeholders in the field (including organizations and professional societies) as the best path forward, encouraging the cultural shift needed to ensure responsible data sharing in the IO research setting.

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