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

2020 ◽  
Vol 8 (2) ◽  
pp. e001389 ◽  
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.

scholarly journals A systematic literature review of researchers’ and healthcare professionals’ attitudes towards the secondary use and sharing of health administrative and clinical trial data

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Elizabeth Hutchings ◽  
Max Loomes ◽  
Phyllis Butow ◽  
Frances M. Boyle
Keyword(s):  
Clinical Trial ◽  
Literature Review ◽  
Data Sharing ◽  
Data Storage ◽  
Systematic Literature Review ◽  
Healthcare Professionals ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Original Research ◽  
Secondary Use

Abstract A systematic literature review of researchers and healthcare professionals’ attitudes towards the secondary use and sharing of health administrative and clinical trial data was conducted using electronic data searching. Eligible articles included those reporting qualitative or quantitative original research and published in English. No restrictions were placed on publication dates, study design, or disease setting. Two authors were involved in all stages of the review process; conflicts were resolved by consensus. Data was extracted independently using a pre-piloted data extraction template. Quality and bias were assessed using the QualSyst criteria for qualitative studies. Eighteen eligible articles were identified, and articles were categorised into four key themes: barriers, facilitators, access, and ownership; 14 subthemes were identified. While respondents were generally supportive of data sharing, concerns were expressed about access to data, data storage infrastructure, and consent. Perceptions of data ownership and acknowledgement, trust, and policy frameworks influenced sharing practice, as did age, discipline, professional focus, and world region. Young researchers were less willing to share data; they were willing to share in circumstances where they were acknowledged. While there is a general consensus that increased data sharing in health is beneficial to the wider scientific community, substantial barriers remain. Systematic review registration PROSPERO CRD42018110559

Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

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.

Mining Clinical Trial Data

2011 ◽  
pp. 31-47
Author(s):  
Jose Ma. J. Alvir ◽  
Javier Cabrera
Keyword(s):  
Data Mining ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Trial Data ◽  
Treated Group ◽  
Trial Data ◽  
Controlled Trials ◽  
Clinical Trails ◽  
Important Objective

Mining clinical trails is becoming an important tool for extracting information that might help design better clinical trials. One important objective is to identify characteristics of a subset of cases that responds substantially differently than the rest. For example, what are the characteristics of placebo respondents? Who have the best or worst response to a particular treatment? Are there subsets among the treated group who perform particularly well? In this chapter we give an overview of the processes of conducting clinical trials and the places where data mining might be of interest. We also introduce an algorithm for constructing data mining trees that are very useful for answering the above questions by detecting interesting features of the data. We illustrate the ARF method with an analysis of data from four placebo-controlled trials of ziprasidone in schizophrenia.

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.

scholarly journals Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume II—practical challenges

2020 ◽  
Vol 8 (2) ◽  
pp. e001472
Author(s):  
Alessandra Cesano ◽  
Michael A Cannarile ◽  
Sacha Gnjatic ◽  
Bruno Gomes ◽  
Justin Guinney ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Data Sharing ◽  
Trial Data ◽  
Professional Standards ◽  
Data Sets ◽  
Clinical Use ◽  
Conceptual Level ◽  
Immunotherapy Of Cancer ◽  
Clinical Biomarker ◽  
Derived Data

The development of strongly predictive validated biomarkers is essential for the field of immuno-oncology (IO) to advance. The highly complex, multifactorial data sets required to develop these biomarkers necessitate effective, responsible data-sharing efforts in order to maximize the scientific knowledge and utility gained from their collection. While the sharing of clinical- and safety-related trial data has already been streamlined to a large extent, the sharing of biomarker-aimed clinical trial derived data and data sets has been met with a number of hurdles that have impaired the progression of biomarkers from hypothesis to clinical use. These hurdles include technical challenges associated with the infrastructure, technology, workforce, and sustainability required for clinical biomarker data sharing. To provide guidance and assist in the navigation of these challenges, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to outline the challenges that researchers currently face, both at the conceptual level (Volume I) and at the technical level (Volume II). The committee also suggests possible solutions to these problems in the form of professional standards and harmonized requirements for data sharing, assisting in continued progress toward effective, clinically relevant biomarkers in the IO setting.

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