A Model Data Management Plan Standard Operating Procedure: Results From the DIA Clinical Data Management Community, Committee on Clinical Data Management Plan

Brand, S.; Bartlett, Diana; Farley, Mary; Fogelson, Miriam; Hak, Jan Bart; Hu, Grace; Montana, Olivia D.; Pierre, Jan Holladay; Proeve, Johann; Qureshi, Samina; Shen, Anita; Stockman, Peter; Chamberlain, Richard; Neff, Kristin M

doi:10.1177/2168479015579520

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…A third review similar to the DCC overview (Knight) was also reported in 2012 [28]. The Knight review included funding agencies supporting research projects at the London School of Hygiene and Tropical Medicine and was similarly focused on post-publication data management [28] The fourth review by Brandt et al (2015) was limited to DMPs from several pharmaceutical and medical device companies and academic research institutions and resulted in a guideline for writing a standard operating procedure for a DMP that contained recommended topics to be covered in a DMP [29].…”

Section: Introductionmentioning

confidence: 99%

Data management plans: the missing perspective

Williams

Bagwell

Zozus

2017

Journal of Biomedical Informatics

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The National Institutes of Health requires data sharing plans for projects with over five hundred thousand dollars in direct costs in a single year and has recently released a new guidance on rigor and reproducibility in grant applications. The National Science Foundation outright requires Data Management Plans (DMPs) as part of applications for funding. However, there is no general and definitive list of topics that should be covered in a DMP for a research project. We identified and reviewed DMP requirements from research funders. Forty-three DMP topics were identified. The review uncovered inconsistent requirements for written DMPs as well as high variability in required or suggested DMP topics among funder requirements. DMP requirements were found to emphasize post-publication data sharing rather than upstream activities that impact data quality, provide traceability or support reproducibility. With the emphasis equalized, the forty-three identified topics can aid Data Managers in systematically generating comprehensive DMPs that support research project planning and funding application evaluation as well as data management conduct and post-publication data sharing.

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Section: Introductionmentioning

confidence: 99%

Data management plans: the missing perspective

Williams

Bagwell

Zozus

2017

Journal of Biomedical Informatics

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“…The DMP serves multiple purposes. First and foremost, the DMP comprehensively documents the collection and handling of the data such that every operation performed on data from the time it is collected until finalized as 12 2 The DMP should specify all operations performed on data [V] 12 3 The DMP should be developed in collaboration with involvement from clinical and statistical operations, project management, and scientific study leadership [V] 23 ; The procedure for creating a DMP should be documented in a company or institution's SOP [V] 12 4 An organizational DMP template should be used to ensure consistency and standardization across all projects [V] 12 5 The topics listed in this chapter should be documented in, or referenced by, the DMP [V]…”

Section: ) Purpose Of the Dmpmentioning

confidence: 99%

“…Correspondingly, review and synthesis of DMP requirements in various disciplines and sectors have appeared in the literature. 10,11,12,13 Most notably, Brand et al, 12 reviewed DMPs from several therapeutic development companies and academic research institutions and published a guideline for writing an SOP for DMP creation and maintenance. Williams, et al 13 , on the other hand, broadly reviewed research sponsor DMP requirements with an emphasis on data definition, collection, processing and traceability.…”

Section: ) Introductionmentioning

confidence: 99%

Data Management Plan

Lebedys

Famatiga-Fay

Bhatkar

et al. 2021

Journal of the Society for Clinical Data Management

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Every clinical study should have prospective plans for how data will be collected, processed and stored. Likewise, every study should have defined data elements and objective evidence of how data were processed. This chapter outlines the purpose of, and regulatory basis for, such documentation in the form of a Data Management Plan (DMP). Although the clinical data manager (CDM) may not personally prepare all sections of the data management plan, he or she is often responsible for assuring that comprehensive data documentation exists.

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“…An exercise to identify standard requirements for data management systems in clinical trials recommends mechanisms to identify and report on missing or late data, but does not mention maintenance of high data returns throughout a trial [16]. Two recent papers offering advice on data management plan development also do not provide guidance on this issue [17, 18].…”

Section: Introductionmentioning

confidence: 99%

Early warnings and repayment plans: novel trial management methods for monitoring and managing data return rates in a multi-centre phase III randomised controlled trial with paper Case Report Forms

Cragg

Cafferty

Díaz-Montaña

et al. 2019

Trials

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Background Monitoring and managing data returns in multi-centre randomised controlled trials is an important aspect of trial management. Maintaining consistently high data return rates has various benefits for trials, including enhancing oversight, improving reliability of central monitoring techniques and helping prepare for database lock and trial analyses. Despite this, there is little evidence to support best practice, and current standard methods may not be optimal. Methods We report novel methods from the Trial of Imaging and Schedule in Seminoma Testis (TRISST), a UK-based, multi-centre, phase III trial using paper Case Report Forms to collect data over a 6-year follow-up period for 669 patients. Using an automated database report which summarises the data return rate overall and per centre, we developed a Microsoft Excel-based tool to allow observation of per-centre trends in data return rate over time. The tool allowed us to distinguish between forms that can and cannot be completed retrospectively, to inform understanding of issues at individual centres. We reviewed these statistics at regular trials unit team meetings. We notified centres whose data return rate appeared to be falling, even if they had not yet crossed the pre-defined acceptability threshold of an 80% data return rate. We developed a set method for agreeing targets for gradual improvement with centres having persistent data return problems. We formalised a detailed escalation policy to manage centres who failed to meet agreed targets. We conducted a post-hoc, descriptive analysis of the effectiveness of the new processes. Results The new processes were used from April 2015 to September 2016. By May 2016, data return rates were higher than they had been at any time previously, and there were no centres with return rates below 80%, which had never been the case before. In total, 10 centres out of 35 were contacted regarding falling data return rates. Six out of these 10 showed improved rates within 6–8 weeks, and the remainder within 4 months. Conclusions Our results constitute preliminary effectiveness evidence for novel methods in monitoring and managing data return rates in randomised controlled trials. We encourage other researchers to work on generating better evidence-based methods in this area, whether through more robust evaluation of our methods or of others.

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A Model Data Management Plan Standard Operating Procedure: Results From the DIA Clinical Data Management Community, Committee on Clinical Data Management Plan

Abstract: The model DMP SOP produced addresses most data management issues that are present in any phase clinical trial while providing a flexible framework.

Cited by 9 publications

References 6 publications

Data management plans: the missing perspective

Data management plans: the missing perspective

Data Management Plan

Early warnings and repayment plans: novel trial management methods for monitoring and managing data return rates in a multi-centre phase III randomised controlled trial with paper Case Report Forms

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