Data management programs

Today there are alternatives to the after-the-fact method of inhibitor monitoring and control, with the advent of automatic, semicontinuous, and continuous control methods that employ tagging and tracing and similar techniques. These newer methods can often be linked to computer data management programs. 

Stoker, A.C. and Conrado, C.L., The Marshall Islands Data Management Program, Report UCRL-ID-120430, Sept. 1995. 

The main output from this stage is a detailed assessment of the human error problem areas. If possible this should include quantitative data on the incidence of errors and the significance of their consequences. This will provide a valuable baseline against which to evaluate the success of the error management program. 

Pareto Diagrams are used in quality management programs to reveal the pattern of variation in performance and any predominant tendency. The data are displayed in the form of a histogram. 

The host computer provides all of the data management, storage, report generation, and analysis capabilities of the predictive maintenance program. Therefore, care should... 

A well-developed records management program that defines rules by which records and documents are handled from creation to retirement is necessary for a successful electronic data archiving process. In a GLP-compliant system, records are classified by type, such as study-specific raw data, reports, personnel records, etc. Each record type has a defined retention time. 

Ph.D., performed the computer programming and statistical analysis. Candace Young and Frieda Fisher helped with data management. William Bax, Joseph Wade, Charles Bennett, Gertie King, Dennis Miles, Jimmie Pickett, Frank Porter, Deborah Richardson, and Alice Roberts collected urine samples. 

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. 

How clean the data must be depends on the importance of the data. Critical analysis variables must be clean, so this is where the site and data management groups should focus their resources. If the data are dirty at the time of statistical analysis, many inefficient and costly workarounds may need to be applied in the statistical programming, and the quality of the data analysis could suffer. However, if a variable is not important to the statistical analysis, then it is better to save the expense of cleaning that variable. 

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 ... 

SAS has always had and will maintain a central role in the data management, analysis, and reporting of clinical trial data. Because of the strong suite of SAS statistical procedures and the power of Base SAS programming, SAS remains a favorite of statisticians for the analysis of clinical trial data. Several companies have built their clinical trial data management and statistical analysis systems entirely with SAS software. More recently, SAS has offered SAS Drug Development as an industry solution that provides a comprehensive clinical trial analysis and reporting environment compliant with 21 CRF-Part 11. 

---