Good data management practices are essential to the success of a trial because they help to ensure that the data collected is complete and accurate. Good data management requires proper planning and should begin in parallel with protocol development in order to ensure that all of the protocol-specified data is accurately captured. Data management begins with creating an overall data management plan, selecting and designing a database, the design of data capture instrument (e.g. Case Report Form) and continues with data collection and regular quality control procedures and ends with database cleaning and locking/freezing.
The objectives of good clinical data management are to ensure:
The European Clinical Research Infrastructures Network (ECRIN) has written some helpful and straightforward guidance on GCP-compliant data management in multinational clinical trials. It’s available here.
Choosing a Clinical Data Management System (CDMS)
There are many types of software that can be used for processing of clinical trial data and individual decisions will need to be made regarding the complexity and costs of different systems. However, there are some minimum requirements for systems that will be used for handling clinical trial data and it is highly recommended that clinical trial-specific software is used; generic spreadsheet and database software like Excel and Access do not meet these global standards because it is important to have an audit trail and for the data to be secure. Many trial sponsors and funding agencies request that a CDMS is 'FDA' compliant. Details of this are given below.
US Food and Drug Administration (FDA) 21 CFR Part 11 requirements for data management:
Whilst this appears a daunting set of requirements it assures the validity of the data and this is a strong incentive for having a robust CDMS. To this end, this list from the FDA is a useful benchmark.
In addition to the minimum requirement there are some other considerations to take into account such as the Clinical Data Interchange Standards Consortium (CDISC). CDISC is a non-profit organization, whose mission is "to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare". CDSIC has developed the Study Data Tabulation Model (SDTM) which is intended for standardisation of data submission to the US FDA.
GlobalHealthTrials supports the use of OpenClinica, a free, open source, web-based Electronic Data Capture (EDC) data management system that is fully compliant with FDA 21 CFR Part 11 and can export data in CDISC format. If you have experience with or would like to recommend other CDMS systems please share it here!
This is an area that proves quite difficult for researchers and so discussions on databases on the forum will help share issues and solutions, so please get invovled in these discussions. There are also experts on hand to answer questions or help troubleshoot.
Case report form design
A well designed Case Report Form (CRF) is key to obtaining accurate and complete trial data. CRF design should begin in parallel with protocol development as the CRF is essentially the data capture system for the protocol.
Paper or electronic CRFs?
The choice to use paper or electronic CRFs will be largely influenced by the CDMS in use and the local infrastructure. It might be less expensive to process electronic CRFs but the additional cost of computers or mobile devices and internet or GPRS access must be factored in and may mean that paper is cheaper for use in remote sites, where IT infrastructure may be poor.
When using paper CRFs, it is important to think ahead regarding the flow of data and to account for the extra time that will be required for data entry. For multi-site trials, it is also important to consider the time and expense required for the CRFs to be shipped to the coordinating centre. Once trial enrolment begins every effort should be made to enter the participant information onto the trial database as quickly as possible to enable accurate tracking of trial progress and monitoring of safety data. It is also of utmost importance to track the flow of paper CRFs from the study sites to the coordinating centre.
Data Management Plan
The Data Management Plan (DMP) is a very important piece of study documentation as it outlines the roles and responsibilities of all staff involved in gathering or handling study data. The DMP also describes the database structure and the procedures that will be used for system testing and validation and also for data entry, edit checks, data coding, data queries and query resolution. The DMP should give a complete picture of how the data is handled for a particular study and is of value for study planning and also in case of audit.
The Association for Clinical Data Management has prepared a very comprehensive template for a DMP. It is primarily aimed at commercial trial operations but could easily be adapted for use in non-commercial studies.
The sequence of data management should be as follows:
Data Management Plan
DBMS selection/Choosing
Study build and CRF design
Data Collection
Discrepancy Management & Data cleaning.
Many systems may be in place to manage and clean the data, ranging from double data entry and monitoring, to interim analyses and data safety monitoring.
It is also possible to set an automated nightly process called batch validation. At regular time intervals, the clinical trial management system/database will check and validate data points, as well as activating a univariate check and multivariate check. If any mismatch or missing data occurs, an automatic query is generated.
Lock & Freeze of Database
When all the clean data is placed in the database as per the protocol it is ready for locked and Database freezing is done before the database submission.
Useful templates are available here: http://globalhealthtrials.tghn.org/articles/data-management/
For information about the guidelines governing data management, and how to code data, you can read the article here: http://globalhealthtrials.tghn.org/articles/data-management-overview-coding-and-guidelines/
And you can also find more information and discuss with experts on The Global Health Network’s Data Management member area, ADMIT (the Assosciation for Data Management in the Tropics): http://admit.tghn.org/
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This eLearning Course provides a pragmatic introduction to data management within clinical research, including what to take into account when planning your data management and how to consider data analysis.
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This article is part of the network’s archive of useful research information. This article is closed to new comments due to inactivity. We welcome new content which can be done ...
This article is part of the network’s archive of useful research information. This article is closed to new comments due to inactivity. We welcome new content which can be done ...
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