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

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.

scholarly journals 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

BMJ ◽  
2019 ◽  
pp. l4217 ◽  
Author(s):  
Jennifer Miller ◽  
Joseph S Ross ◽  
Marc Wilenzick ◽  
Michelle M Mello
Keyword(s):  
Clinical Trial ◽  
Data Sharing ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Interquartile Range ◽  
Pharmaceutical Companies ◽  
Cross Sectional ◽  
New Drug ◽  
Results Reporting ◽  
Policies And 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.

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

Trials ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 384 ◽  
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

scholarly journals Setting the IMPACT (IMProve Access to Clinical Trial data) Observatory baseline

2018 ◽  
Vol 28 (1) ◽  
Author(s):  
Mersiha Mahmić-Kaknjo ◽  
Josip Šimić ◽  
Karmela Krleža-Jerić
Keyword(s):  
Clinical Trial ◽  
Clinical Trial Data ◽  
Trial Data ◽  
The Impact ◽  
Improve Access

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

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.

scholarly journals The impact of patient-reported outcome (PRO) data from clinical trials: a systematic review and critical analysis

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Samantha Cruz Rivera ◽  
Derek G. Kyte ◽  
Olalekan Lee Aiyegbusi ◽  
Anita L. Slade ◽  
Christel McMullan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Case Studies ◽  
Real World ◽  
Clinical Trial Data ◽  
Research Impact ◽  
Trial Data ◽  
Patient Reported ◽  
The Impact

Abstract Background Patient-reported outcomes (PROs) are commonly collected in clinical trials and should provide impactful evidence on the effect of interventions on patient symptoms and quality of life. However, it is unclear how PRO impact is currently realised in practice. In addition, the different types of impact associated with PRO trial results, their barriers and facilitators, and appropriate impact metrics are not well defined. Therefore, our objectives were: i) to determine the range of potential impacts from PRO clinical trial data, ii) identify potential PRO impact metrics and iii) identify barriers/facilitators to maximising PRO impact; and iv) to examine real-world evidence of PRO trial data impact based on Research Excellence Framework (REF) impact case studies. Methods Two independent investigators searched MEDLINE, EMBASE, CINAHL+, HMIC databases from inception until December 2018. Articles were eligible if they discussed research impact in the context of PRO clinical trial data. In addition, the REF 2014 database was systematically searched. REF impact case studies were included if they incorporated PRO data in a clinical trial. Results Thirty-nine publications of eleven thousand four hundred eighty screened met the inclusion criteria. Nine types of PRO trial impact were identified; the most frequent of which centred around PRO data informing clinical decision-making. The included publications identified several barriers and facilitators around PRO trial design, conduct, analysis and report that can hinder or promote the impact of PRO trial data. Sixty-nine out of two hundred nine screened REF 2014 case studies were included. 12 (17%) REF case studies led to demonstrable impact including changes to international guidelines; national guidelines; influencing cost-effectiveness analysis; and influencing drug approvals. Conclusions PRO trial data may potentially lead to a range of benefits for patients and society, which can be measured through appropriate impact metrics. However, in practice there is relatively limited evidence demonstrating directly attributable and indirect real world PRO-related research impact. In part, this is due to the wider challenges of measuring the impact of research and PRO-specific issues around design, conduct, analysis and reporting. Adherence to guidelines and multi-stakeholder collaboration is essential to maximise the use of PRO trial data, facilitate impact and minimise research waste. Trial registration Systematic Review registration PROSPERO CRD42017067799.

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

2019 ◽  
Vol 47 (3) ◽  
pp. 369-373 ◽  
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.

Differential Effect of Recurrent Concussions on Symptom Clusters in Sport Concussion Assessment Tool

2019 ◽  
Vol 28 (7) ◽  
pp. 735-739
Author(s):  
Jian Chen ◽  
Bruce Oddson ◽  
Heather C. Gilbert
Keyword(s):  
Assessment Tool ◽  
Cross Sectional Study ◽  
Physical Symptoms ◽  
Symptom Clusters ◽  
Cross Sectional ◽  
Sport Concussion ◽  
Comprehensive Survey ◽  
Using Data ◽  
Composite Scores ◽  
The Impact

Context: Symptom checklist in Sport Concussion Assessment Tool has been widely used in preseason assessment and in concussion diagnosis, but the impact of prior concussions on the graded symptoms after a new concussion has not been evaluated. Objective: This study was undertaken to examine reported symptoms associated with recurrent concussions using data of a comprehensive survey among athletes. Design: Retrospective survey and cross-sectional study. Setting: College athletes. Participants: Student athletes who sustained one or more concussions. Main Outcome Measures: Concussion history and graded symptoms of the most recent concussion at time of the survey were surveyed. The impact of prior concussions was examined over symptoms and aggregated symptoms. Results: Multiple concussions were associated with greater reporting of individual symptoms related to emotion and physical symptoms of sensitivity to light and noise: more emotional (z = 2.3, P = .02); sadness (z = 2.4, P = .02); nervousness (z = 2.4, P = .02); irritability (z = 3.6, P = .01); sensitivity to light (z = 2.6, P = .01); and sensitivity to noise (z = 2.4, P = .04). The composite scores of emotional symptom and sensitivity symptom clusters were significantly higher: t = 2.68 (P < .01) and t = 3.35 (P < .01), respectively. Conclusions: The significant rises in emotional and sensitivity symptoms may be an important additive effect of concussive injury. Closer attention should be given to these symptom clusters when evaluating concussion injury and recovery.

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