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Regulatory Frameworks for Clinical Trial Data Sharing: Scoping Review.
Gudi, Nachiket; Kamath, Prashanthi; Chakraborty, Trishnika; Jacob, Anil G; Parsekar, Shradha S; Sarbadhikari, Suptendra Nath; John, Oommen.
  • Gudi N; The George Institute for Global Health, New Delhi, India.
  • Kamath P; The George Institute for Global Health, New Delhi, India.
  • Chakraborty T; The George Institute for Global Health, New Delhi, India.
  • Jacob AG; The George Institute for Global Health, New Delhi, India.
  • Parsekar SS; Public Health Evidence South Asia, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India.
  • Sarbadhikari SN; The George Institute for Global Health, New Delhi, India.
  • John O; The George Institute for Global Health, University of New South Wales, New Delhi, India.
J Med Internet Res ; 24(5): e33591, 2022 05 04.
Article in English | MEDLINE | ID: covidwho-1834163
ABSTRACT

BACKGROUND:

Although well recognized for its scientific value, data sharing from clinical trials remains limited. Steps toward harmonization and standardization are increasing in various pockets of the global scientific community. This issue has gained salience during the COVID-19 pandemic. Even for agencies willing to share data, data exclusivity practices complicate matters; strict regulations by funders affect this even further. Finally, many low- and middle-income countries (LMICs) have weaker institutional mechanisms. This complex of factors hampers research and rapid response during public health emergencies. This drew our attention to the need for a review of the regulatory landscape governing clinical trial data sharing.

OBJECTIVE:

This review seeks to identify regulatory frameworks and policies that govern clinical trial data sharing and explore key elements of data-sharing mechanisms as outlined in existing regulatory documents. Following from, and based on, this empirical analysis of gaps in existing policy frameworks, we aimed to suggest focal areas for policy interventions on a systematic basis to facilitate clinical trial data sharing.

METHODS:

We followed the JBI scoping review approach. Our review covered electronic databases and relevant gray literature through a targeted web search. We included records (all publication types, except for conference abstracts) available in English that describe clinical trial data-sharing policies, guidelines, or standard operating procedures. Data extraction was performed independently by 2 authors, and findings were summarized using a narrative synthesis approach.

RESULTS:

We identified 4 articles and 13 policy documents; none originated from LMICs. Most (11/17, 65%) of the clinical trial agencies mandated a data-sharing agreement; 47% (8/17) of these policies required informed consent by trial participants; and 71% (12/17) outlined requirements for a data-sharing proposal review committee. Data-sharing policies have, a priori, milestone-based timelines when clinical trial data can be shared. We classify clinical trial agencies as following either controlled- or open-access data-sharing models. Incentives to promote data sharing and distinctions between mandated requirements and supportive requirements for informed consent during the data-sharing process remain gray areas, needing explication. To augment participant privacy and confidentiality, a neutral institutional mechanism to oversee dissemination of information from the appropriate data sets and more policy interventions led by LMICs to facilitate data sharing are strongly recommended.

CONCLUSIONS:

Our review outlines the immediate need for developing a pragmatic data-sharing mechanism that aims to improve research and innovations as well as facilitate cross-border collaborations. Although a one-policy-fits-all approach would not account for regional and subnational legislation, we suggest that a focus on key elements of data-sharing mechanisms can be used to inform the development of flexible yet comprehensive data-sharing policies so that institutional mechanisms rather than disparate efforts guide data generation, which is the foundation of all scientific endeavor.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials / Reviews / Systematic review/Meta Analysis Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 33591

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials / Reviews / Systematic review/Meta Analysis Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 33591