Databases data management

Costing investment analysis Data acquisition Database management Data conversion Development tools Dispersion models Distillation Drafting... 

Pure paper-based data collection systems are most suitable for small and short-term studies. Their advantages are that no computer hardware or software is needed at the participating sites because data are recorded manually on paper forms that are transferred to the centralized location in batches. A major drawback is that participating sites do not have real-time access to their data because no database is created locally. However, both hardware and software are needed at the centralized location for the data management system. The type of hardware and software used is determined by the configuration of the centralized computer. The most commonly used platforms include Open VMS, Unix, or PC, and one of the most widely used software packages is SAS [16]. 

The clinical data management group updates the database or CRF based on the response from the clinical site. 

In order to reduce unnecessary data queries, the statistics group should be consulted early in the clinical database development process to identify variables critical for data analysis. Optimally, the statistical analysis plan would already be written by the time of database development so that the queries could be designed based on the critical variables indicated in the analysis plan. However, at the database development stage, usually only the clinical protocol exists to guide the statistics and clinical data management departments in developing the query or data management plan. 

There is only one good solution to handling free-text variables that are needed for statistical analysis. The free-text variables need to be coded by clinical data management in the clinical database. If the adverse events were coded with a dictionary such as MedDRA, the previous example might look like Program 2.3. 

In this example, it is known from non-database sources that at study termination, subject 101-1002 died. That information is hardcoded into the program and overrides the information coming from the clinical data management system. Here are two reasons why hardcoding is a bad practice ... 

Hardcoding overrides the database controls in a clinical data management system. With hardcoding there is no clear audit trail of data change and CFR 21 - Part 11 controls might be considered compromised. 

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. 

Importing Relational Databases and Clinical Data Management Systems 42 SAS/ACCESS SQL Pass-Through Facility 42 SAS/ACCESS LI BN AM E Statement 43 Importing ASCII Text 44... 

Importing Relational Databases and Clinical Data Management Systems... 

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. 

Medical dictionaries often need to be referenced when creating various analysis data sets For instance, perhaps the raw adverse event database in your clinical data management system contains only the MedDRA code. The code is worth having, but you would need the adverse event body system and preferred medical term to provide a useful summary of events. 

Combinatorial chemistry and HT E are powerful tools in the hands of a scient ist, as they are a source for meaningful consistent records of data that would be hard to obtain via conventional methods within a decent timeframe. This blessing of fast data acquisition can turn into a curse if the experimentalist does not take precautions to carefully plan the experiments ahead and the means of handling the data and analyzing them afterwards. The two essential elements that ensure a successful execution of ambitious projects on a rational and efficient basis are, therefore, tools that enable the scientist to carefully plan experiments and get the most out of the minimum number of experiments in combination with the possibility of fast and reliable data retrieval from databases. Therefore, experimental planning and data management are complementary skillsets for the pre- and post experimental stages. 

Depending on what will happen to the CRFs when they are completed, data managers should also be involved. The clinical data will need to be coded and then entered into a database before being further checked for completeness and correctness. The next step will be the analysis of the data. The manner of presentation of the data in the CRF will avoid some of the mistakes that can occur during data entry, particularly if it is entered manually rather than by electronic transfer. 

Time invested by both the data manager and statistician in designing the structure of the database should also reap rewards at the analysis stage. In addition, a good-quality database is essential if the study is to pass the auditing process. 

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