trial management
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Author(s):  
Sharon V Medendorp ◽  
Allison Crumpler

Effective management of a clinical trialrequires having real time access to information that provides useful insightsinto trial progress and that lends itself to collaborative decisionmaking.  Data visualizations using datafrom multiple source systems employed during the conduct of a clinical trialhave become an essential tool in the recent past as support for collaborativedecision making by project teams. Having the ability to access, analyze, read,work with, and present data to support an argument are  important skills that ensure datavisualizations fulfill their purpose in clinical trial management. There is anexpectation that members of the clinical trial team either possess or developthe data literacy skill sets necessary to collaborate on the successfulexecution of a clinical drug development trial. Here we describe thedevelopment of a Data Learning Series program targeted to increase the data literacyskills within a Contract Research Organization in support of the digitalevolution of the drug development industry.


2022 ◽  
Vol 75 (1) ◽  
pp. 1-119

In this report, the COVID-19 Continuity of Court Operations During a Public Health Emergency Workgroup (Plan B Workgroup) makes recommendations about best practices and technologies that should be retained or adapted post-pandemic. The recommendations in this final Plan B Workgroup whitepaper are based on experience and feedback from Arizona’s courts addressing pandemic and post-pandemic practices. Although the original report, issued on June 2, 2021, included a May 2021 Survey of Arizona’s Courts, this updated report also includes information from a July 2021 State Bar of Arizona Survey and a September 2021 State of Arizona Public Opinion Survey addressing those practices. The workgroup’s findings and recommendations, which remain unchanged, can be summarized in five major categories: (1) Increasing Access to Justice, (2) Expanding Use of Technology, (3) Jury and Trial Management, (4) Communication Strategies and Disaster Preparedness, and (5) Health, Safety, and Security Protocols.


BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Nina Wilson ◽  
Katie Biggs ◽  
Sarah Bowden ◽  
Julia Brown ◽  
Munyaradzi Dimairo ◽  
...  

Abstract Background Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials. Methods We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences. Results Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had. Conclusions This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive trials.


Horticulturae ◽  
2021 ◽  
Vol 7 (11) ◽  
pp. 422
Author(s):  
Virginia R. Sykes ◽  
Natalie R. Bumgarner ◽  
Stefanie Brooke Keadle ◽  
Aleksandra Wilson ◽  
Francisco Palacios

Edible food production is a growing area of horticultural interest that can engage multiple generations of rural to urban residents with varying levels of experience. Residential or community garden food production can provide many benefits, including the production of healthy produce, establishment of community or social connections, and increased physical activity. Regardless of experience, food gardeners are interested in growing crops and cultivars well-suited to their region and which provide both productivity and crop quality. This means that cultivar selection is a common question for gardeners. However, formal cultivar evaluation is relatively rare in the non-commercial food production sector due to the number of cultivars, the challenges of replicated trial management, and the scarcity of public researchers focused on consumer horticulture. This limits the information available to support new gardeners, which lowers the chances of overall success including high-quality harvests. Such crop and variety selection questions are common for Extension personnel in the United States as well as many others who work with gardeners. Even with this high level of interest, funding for consumer garden trials is limited and the cost of replicated trials across various geographical sites is high. To fill this gap in research and address the need for high-quality data to support education, University of Tennessee Extension and research faculty have developed a citizen science approach called the Home Garden Variety Trial (HGVT) program. The HGVT is a collaborative effort between Extension and research faculty and educators, who select trials, provide seeds, and compile data, and citizen scientists around the state, who conduct the trials using their usual gardening practices in their own home or community gardens. Beginning in 2017, the collaborators have conducted five years of research involving over 450 individual gardeners in more than half of the counties in Tennessee. The HGVT is a novel and effective tool to introduce gardeners to new crops and cultivars while providing previously unavailable data to researchers. Together, researchers and home gardeners collect and compile data that supports residential and community food production success while engaging new and experienced gardeners in participatory science research.


Author(s):  
Baldwin C. Mak ◽  
Bryan T. Addeman ◽  
Jia Chen ◽  
Kim A. Papp ◽  
Melinda J. Gooderham ◽  
...  

Objective: Despite the implementation of quality assurance procedures, current clinical trial management processes are time-consuming, costly, and often susceptible to error. This can result in limited trust, transparency, and process inefficiencies, without true patient empowerment. The objective of this study was to determine whether blockchain technology could enforce trust, transparency, and patient empowerment in the clinical trial data management process, while reducing trial cost. Design: In this proof of concept pilot, we deployed a Hyperledger Fabric-based blockchain system in an active clinical trial setting to assess the impact of blockchain technology on mean monitoring visit time and cost, non-compliances, and user experience. Using a parallel study design, we compared differences between blockchain technology and standard methodology. Results: A total of 12 trial participants, seven study coordinators and three clinical research associates across five sites participated in the pilot. Blockchain technology significantly reduces total mean monitoring visit time and cost versus standard trial management (475 to 7 min; P = 0.001; €722 to €10; P = 0.001 per participant/visit, respectively), while enhancing patient trust, transparency, and empowerment in 91, 82 and 63% of the patients, respectively. No difference in non-compliances as a marker of trial quality was detected. Conclusion: Blockchain technology holds promise to improve patient-centricity and to reduce trial cost compared to conventional clinical trial management. The ability of this technology to improve trial quality warrants further investigation.


2021 ◽  
Vol 5 ◽  
Author(s):  
Medha Devare ◽  
Céline Aubert ◽  
Omar Eduardo Benites Alfaro ◽  
Ivan Omar Perez Masias ◽  
Marie-Angélique Laporte

Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing.


BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e048256
Author(s):  
Quang Ngo Nguyen ◽  
Nam Vinh Nguyen ◽  
Dung Thi Phuong Nguyen ◽  
Celine Vidaillac ◽  
Phuong Thi Minh Trinh ◽  
...  

BackgroundAssessing the capacity of a healthcare institution to conduct and manage clinical research studies is challenging, especially in developing countries where resources are limited. The objective of this study was to develop a practical and transparent tool for the Vietnam Ministry of Health (MOH) to assess institutions’ capacity to lead clinical trials in line with local and international regulations.MethodsWe reviewed the literature, relevant official international and national guidelines, regulations and checklists for clinical sites’ assessment to identify key indicators of clinical research capacity. We developed a Good Clinical Practice (GCP) inspection checklist consisting of a questionnaire with 30 key criteria, including 16 core criteria and 14 recommended criteria, related to four central aspects of clinical research management (ie, governance, operations, infrastructures and human resources). Following a detailed review and assessment by a panel of experts, sponsors and academic investigators, we assessed the checklist’s applicability in a pilot study involving 10 sites with various clinical research experiences.ResultsIndependently of their clinical research experience, all participating institutions fulfilled most of the core criteria. In contrast, a significant variability was observed in the compliance to recommended capacity criteria, especially those related to governance (certifications and reporting) as well as operations (existence of a clinical research coordination unit or electronic trial management system).ConclusionsA GCP inspection checklist was successfully developed to support the MOH in the assessment of institutions’ capacity to conduct clinical research. Additional efforts from all stakeholders are now warranted to provide local sites with sustainable capacity development resources that will further build up and harmonise Vietnamese clinical research settings.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Weina Jiang ◽  
Qi Yong ◽  
Ning Liu ◽  
Yuze Luo

Since public opinion from social media has a growing impact and supervision on trial, risk assessment on public opinion is increasingly important in refined trial management. However, the tremendous amount of public opinion and the insufficient historical logs of trial procedures bring challenges to risk assessment on public opinion. To address this, we propose an adaptive multifactor risk assessment framework on public opinion with fuzzy numbers. Initially, we establish a multilayer indicator model for assessing the risk of public opinion (POR) with multilayer analysis and decision methods. Then, we explore the association rules hidden in the process logs to update the indicator model periodically. Moreover, we design a public opinion analysis module for indicator evaluation, including analysis in public opinion sentiment, hot search, and social media coverage to deal with big data on social media. Especially, the public opinion sentiment is classified by topic-based BiLSTM (T-BiLSTM), which is more accurate. Finally, the fuzzy number similarity is employed to determine POR’s level in the nine-level risk system. Experimental results validate the efficiency of our framework when assessing the POR.


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