Data management clinical trials

Subject Information Systems Data Management Clinical Trials Supplies Production Protocol Management... 

Electronic-based data collection and management systems use various computer hardware and software technologies. Although some organizations design and develop their own systems, others purchase well-established e-clinical trials software from a wide range of vendors. 

With the increased acceptance of the Internet and the huge innovations in web development tools, web-based data collection and management systems have become the choice of many CROs because of their capability for collecting clinical trial data in real time and disseminating critical clinical trial information to the participating sites and various oversight committees [27]. 

Web-based data collection and management systems provide a mechanism for remote data entry, where entered data are added to a centralized database once the submit button is pressed. They can be designed to automate the various aspects of clinical trials such as eligibility evaluation, data collection, and tracking specimens. They also serve as a resource site for participating sites to access trial-specific information, facilitate communication, track data queries and their resolutions, and allow administrative management of trials [28, 29]. For these reasons, they play an important role in facilitating the conduct of international clinical trials. 

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Abdellatif A, Motyka D, Williams D, Reda D, Kucmeroski D, Fye C, Clegg D. A data collection and management system for clinical trials in osteoarthritis. Clin Trials 2005 2 S71. 

Kiuchi T, Ohashi Y, Konishi M, Bandai Y, Kosuge T, Kakizoe T. A world wide web-based user interface for a data management system for use in multi-institutional clinical trials—development and experimental operation of an automated subjects registration and random allocation system. Controlled Clin Trials 1996 17 6 476-93. 

In an optimal world, the CRF is perfectly designed to answer the questions of the study and the clinical data management group will have cleaned the data to perfection. However, to be a good statistical programmer in the clinical trial arena, you must always keep a lookout for errant data and program defensively. Defensive programming lets you account for all possible clinical data permutations. 

The problem is that the regular-trial adverse events database and the serious adverse events database do not join well if at all programmatically. You can attempt to join or merge the two databases by event start date and coded term, and that will join many regular-trial adverse events to the serious events. However, this is far from foolproof, because of mismatches in adverse event start dates and because the adverse events may have been coded slightly differently in the two systems. The best way to link the serious adverse events and adverse events databases is to have the clinical data management system create a linking variable key for you. In lieu of that, the only way to reliably link the two data sources is manually. 

The randomization of a patient in a given therapy is the cornerstone of a randomized clinical trial. You may find these data in more than one place. They are often found within some form of Interactive Voice Response System (IVRS), but they may also be found in an electronic file containing the treatment assignments or on the CRF itself. If randomization data are found on the CRF, they usually consist only of the date of randomization for treatment-blinded trials. IVRS data are often found outside the confines of the clinical data management system and usually consist of the following three types of data tables. 

Most clinical data management systems used for clinical trials today store their data in relational database software such as Oracle or Microsoft SQL Server. A relational database is composed of a set of rectangular data matrices called tables that relate or associate with one another by certain key fields. The language most often used to work with relational databases is structured query language (SQL). The SAS/ACCESS SQL Pass-Through Facility and the SAS/ACCESS LIBNAME engine are the two methods that SAS provides for extracting data from relational databases. 

Supportive trial data not in the clinical data management system... 

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