Supportive measures to enhance clinical trial data management in oncology community network practices.

38 Background: As an innovative industry leader of Research Site Management Organizations, US Oncology Research (USOR) sought to develop a best practice to support over 65 community practice network sites to comply with Sponsor data entry expectations. Efforts led by the Clinical Trial Manager (CTM) focused on bringing research site awareness to priority data, Sponsor deadlines, and weekly performance feedback via the ‘Healthy Data Habits Pathway’. Methods: The Healthy Data Habits Pathway was created to define the role of CTM involvement in data management operations on behalf of USOR. In collaboration with Sponsor, the USOR CTM reviewed data management reports weekly for data entry trends. Results: Two separate oncology clinical trials (different Sponsors) implemented the Pathway with renal and breast oncology indications. The renal trial demonstrated an almost 90% reduction in total query count for 8 sites in 7 months while the breast trial reported 7 of 14 sites (50%) meeting Sponsor goal of 20 or fewer total queries in 13 weeks. Interventions included emailed notifications, site manager calls, and weekly CTM-Sponsor collaboration teleconferences. Conclusions: Clinical research Sponsors and sites both strive towards maintaining data integrity in an ever-evolving atmosphere of complex oncology trials and increasingly-detailed data capture requirements. Implementation of Pathway interventions increases site and Sponsor engagement in daily data management operations. [Table: see text]

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

Reviews on Recent Clinical Trials ◽

10.2174/1574887114666190207151500 ◽

2019 ◽

Vol 14 (3) ◽

pp. 160-172 ◽

Cited By ~ 1

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.

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Best practice for analysis of shared clinical trial data

BMC Medical Research Methodology ◽

10.1186/s12874-016-0170-y ◽

2016 ◽

Vol 16 (S1) ◽

Cited By ~ 5

Author(s):

Sally Hollis ◽

Christine Fletcher ◽

Frances Lynn ◽

Hans-Joerg Urban ◽

Janice Branson ◽

...

Keyword(s):

Clinical Trial ◽

Best Practice ◽

Clinical Trial Data ◽

Trial Data

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Use of relational database “views” in clinical trial data management

Controlled Clinical Trials ◽

10.1016/0197-2456(91)90079-2 ◽

1991 ◽

Vol 12 (5) ◽

pp. 620-621

Author(s):

Frances LoPresti ◽

Evelyn D. Mirenzi

Keyword(s):

Clinical Trial ◽

Data Management ◽

Relational Database ◽

Clinical Trial Data ◽

Trial Data ◽

Database Views

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Invisible Barriers to Clinical Trials: The Impact of Structural, Infrastructural, and Procedural Barriers to Opening Oncology Clinical Trials

Journal of Clinical Oncology ◽

10.1200/jco.2005.05.0104 ◽

2006 ◽

Vol 24 (28) ◽

pp. 4545-4552 ◽

Cited By ~ 106

Author(s):

David M. Dilts ◽

Alan B. Sandler

Keyword(s):

Clinical Trials ◽

The United States ◽

Comprehensive Cancer Center ◽

Cancer Center ◽

Community Practice ◽

Home Office ◽

Scientific Review ◽

Oncology Research ◽

Oncology Clinical Trials ◽

The Impact

Purpose To investigate the administrative barriers that impact the opening of clinical trials at the Vanderbilt-Ingram Cancer Center (VICC) and at VICC Affiliate Network (VICCAN) sites. Methods VICC, a National Cancer Institute–designated comprehensive cancer center, and three VICCAN community practice sites were studied. Methodology used was identification and mapping of existing processes and analysis of historical timing data. Results At course granularity, the process steps required at VICC and VICCAN main office plus local sites are 20 v 17 to 30 steps, respectively; this gap widens with finer granularity, with more than 110 v less than 60 steps, respectively. Approximately 50% of the steps are nonvalue added. For example, in the institutional review board (IRB) process, less than one third of the steps add value to the final protocol. The numbers of groups involved in the approval processes are 27 (VICC) and 6 to 14 (VICCAN home office and local sites). The median times to open a trial are 171 days (95% CI, 158 to 182 days) for VICC and 191 days (95% CI, 119 to 269 days) for the VICCAN sites. Contrary to expectations, the time for IRB review and approval (median, 47 days) is the fastest process compared with the scientific review committee review and approval (median, 70 days) and contracts and grants review (median, 78.5 days). Opening a cooperative group clinical trial is significantly (P = .05) more rapid because they require fewer review steps. Conclusion There are numerous opportunities to remove nonvalue-added steps and save time in opening clinical trials. With increasing numbers of new agents, fewer domestic principal investigators, and more companies off-shoring clinical trials, overcoming such barriers is of critical importance for maintenance of core oncology research capabilities in the United States.

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Towards agreement on best practice for publishing raw clinical trial data

Trials ◽

10.1186/1745-6215-10-17 ◽

2009 ◽

Vol 10 (1) ◽

Cited By ~ 40

Author(s):

Iain Hrynaszkiewicz ◽

Douglas G Altman

Keyword(s):

Clinical Trial ◽

Best Practice ◽

Clinical Trial Data ◽

Trial Data

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Community and academic partnerships: Moving a new generation of clinical trials in NCI Community Oncology Research Program (NCORP) into community oncology practices.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.2551 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 2551-2551

Author(s):

Worta J. McCaskill-Stevens ◽

Ann M. Geiger

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Cancer Care ◽

Cost Of Care ◽

Community Practice ◽

Effective Strategies ◽

Oncology Research ◽

Research Activities ◽

Community Oncology ◽

Catchment Areas

2551 Background: NCORP is a model program that bridges academic and community oncology practices and research. Over the past decade, community cancer investigators have adopted new technology, encountered new treatment sequalae, and faced rising cost of care with its financial toxicity imposed upon individuals seeking care. Opportunities are abundant for community investigators to assess feasibility and uptake of research advances into community practice settings, yet these opportunities are met with the challenges of dynamic changes in types of organizations delivering cancer care and diversity of populations within their catchment areas. Little information is shared about how and to what extent the health environment influences this partnership and the implementation of a broad cancer research portfolio. Methods: This abstract reports on the continued interest and participation of community oncologists in research which is demonstrated by 987 practices with over 4000 investigators in NCORP. Since 2014, over 30,000 individuals enrolled in symptom management, screening, surveillance, quality of life, and treatment trials. An additional 4500 patients and clinicians have enrolled in care delivery studies. Results: NCORP has been central in evaluating the most effective strategies for investigators to effectively communicate to patients the science of genomically-driven trials. It has also provided ways of bringing the pediatric and AYA patients access to the most up-to-date treatment strategies and new therapies in their community. This creates the least disruption on family structure/dynamics, diminished traveling requirements/costs, and reduced the financial burden. NCORP promotes involvement of treating oncologists in research activities. This also improves care for patients not enrolled in clinical trials. Therefore, NCORP serves as a laboratory to determine the most effective strategies for co-management of cancer patients and survivors. Conclusions: Several questions however remain to be addressed using this clinical trial model. These include: how to continue to reduce disparities in cancer care and clinical trial participation; and, what are the best strategies for fostering implementation of cancer care models in community practice.

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The development of CLINDATA, a clinical trial data management system

Journal of Information Science ◽

10.1177/016555158100300303 ◽

1981 ◽

Vol 3 (3) ◽

pp. 129-136 ◽

Cited By ~ 3

Author(s):

T. Ravenscroft ◽

D.E. Smith

Keyword(s):

Clinical Trial ◽

Data Management ◽

Management System ◽

Clinical Trial Data ◽

Trial Data ◽

Data Management System ◽

Long Term Follow Up ◽

On Line

The paper describes the design and implementation of a clinical trial data management system at the Wellcome Research Laboratories. Based on an IBM 3031 computer, the system provides the capability for on-line data input, search ing and comprehensive data analysis. The database also performs an adverse reaction reporting function and provides for long term follow-up of patients.

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CLINICAL DATA MANAGEMENT IMPORTANCE IN CLINICAL RESEARCH

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2016.v9s2.13940 ◽

2016 ◽

pp. 59

Author(s):

Deepa Murugesan ◽

Ranganath Banerjee ◽

Gopal Ramesh Kumar

Keyword(s):

Clinical Trial ◽

Data Management ◽

Clinical Data ◽

Clinical Trial Data ◽

Drug Approval ◽

Trial Data ◽

Data Capture ◽

Data Management System ◽

Pharmaceutical Companies ◽

Management Tools

ABSTRACT Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and facilitates the drug discovery and development. Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.

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