Data management and manipulation

Pharmaeeutieal organizations today are totally dependent on aeeurate data stored and manipulated by validated business applications. IT Infrastructure is the platform for business applications and hence must be qualihed and managed following approved procedures. 

Much of the discussion on data collection and data banks assumes acceptance of the accounting theory of data manipulation. In contrast, most, if not aU, of the available methods for handhng numerical data assume the statistical theory of data collection, management, and manipulation. 

Once the raw clinical data have been imported into SAS, the next step is to transform those raw data into more useful analysis-ready data. Raw data here mean data that have been imported without manipulation into SAS from another data source. That data source is likely to be a clinical data management system, but it could also be external laboratory data, IVRS data, data found in Microsoft Office files, or CDISC model data serving as the raw data. These raw data as they exist are often not ready for analysis. There may be additional variables that need to be defined, and the data may not be structured in a way that is required for a particular SAS analysis procedure. So once the raw data have been brought into SAS, they usually require some kind of transformation into analysis-ready files, which this chapter will discuss. 

In this chapter, we have advocated the use of a computer for plotting the standard curve and performing the least squares fit procedure. Indeed, computers play a central role in the analytical laboratory for acquiring and manipulating data generated by instruments and for information management. 

A principal components multivariate statistical approach (SIMCA) was evaluated and applied to interpretation of isomer specific analysis of polychlorinated biphenyls (PCBs) using both a microcomputer and a main frame computer. Capillary column gas chromatography was employed for separation and detection of 69 individual PCB isomers. Computer programs were written in AMSII MUMPS to provide a laboratory data base for data manipulation. This data base greatly assisted the analysts in calculating isomer concentrations and data management. Applications of SIMCA for quality control, classification, and estimation of the composition of multi-Aroclor mixtures are described for characterization and study of complex environmental residues. 

The role of Performance Qualification, which entails challenging the application within the scope of business processes, is harder to distinguish. As a consequence PQ for databases may be combined with User Acceptance testing. Items for consideration within a PQ include verification of data management within the application (actually checking manipulated data sets to determine they are correct), and examining the role of the application within the wider process flow. 

Open-access LC/MS formats have spawned new dimensions in access and data management. The use of a direct exposure probe (DEP) for automated sample introduction has been developed for quick (ca. 3 min) molecular weight determination of new lead compounds and quantitative analysis. Fig. 7 illustrates an automated direct probe system for molecular weight determination. Versatile software packages for data manipulation and processing has been a popular... 

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