SAS DATA Step

Program 3.5 Writing Custom SAS Code to Import Lab Normal Data 

---

PROC IMPORT and the Import Wizard 44 SAS DATA Step 52 SAS Enterprise Guide 53 Importing Microsoft Office Files 56 LIBNAME Statement 58 Import Wizard and PROC IMPORT 59 SAS/ACCESS SQL Pass-Through Facility 64 SAS Enterprise Guide 65 Importing XML 68... 

SAS provides many ways of importing ASCII text files. These methods include using PROC IMPORT and the Import Wizard, the SAS DATA step, and SAS Enterprise Guide. 

If you click Save in the window, the process is saved to the current project in SAS Enterprise Guide. If you select the Preview window in the lower-left comer of the window, you can see the SAS DATA step code that SAS Enterprise Guide generates and the results of that code. Click Run and SAS Enterprise Guide will import the data. If the results are not exactly what you want, then the saved Import Data process shown in the following window can be opened, changed, and rerun. 

Often you want to redefine an already existing variable within a SAS DATA step. As simple as this may sound, it can lead to unexpected results if not done carefully. The following example displays some unexpected behavior that may occur when you redefine a variable within a DATA step. In this example you want to flag the subject who had the Fatal MI adverse event as having died (death =1). 

Clinical trial data come in two basic forms numeric variables and text variables. Numeric variables are easy for the statistical programmer to handle. Numbers can be analyzed with SAS in a continuous or categorical fashion without much effort. If a numeric variable needs categorization, it is easy enough to categorize the data within SAS. For example, if you had to classify patient age, a simple DATA step such as the following might serve well. 

What you want is the actual DATA step code that SAS writes behind the scenes to import the data. That code can be found in the Log window when the PROC IMPORT executes. For Program 3.4, the SAS log looks like this ... 

There are two tools commonly used for performing data transpositions in SAS PROC TRANSPOSE, and a DATA step with ARRAY statements. PROC TRANSPOSE is a powerful tool that flips a data set with just a few lines of SAS code. On the other hand, DATA steps that employ arrays for data transposition are more flexible, in that they allow for more precise control of the transposition process. We will now examine PROC... 

PROC TRANSPOSE is an efficient way to transpose a SAS data set when the transposition process is simple. However, if the transposition process is more complicated, and involves transforming to a data set where all possible columns must be represented or where there are multiple records per BY group, then a DATA step with arrays is probably a better choice. 

Note that the implicit century DATA step produces dates in two different centuries, but that when you explicitly state the century there is no subsequent century confusion. You can define YEARCUTOFF as needed for your specific SAS applications, but it is wise to have a reasonable system-wide YEARCUTOFF default set. Also, if you use explicit centuries whenever possible, you minimize the implicit century risk. 

The following example relies on DATA step programming, a few SAS macro variables, and a final DATA NULL step with PUT statements for custom table presentation. Here are the adverse event summary annotated SAS program, notes for the program, and the output. 

Here is the SAS program that creates this box plot. The initial input DATA step is omitted because it is the same as in Program 6.4, and the changes necessary to produce this plot are highlighted. 

You can see how the (pipe) character is entered as the delimiter in the Options dialog box. If you have more complex requirements for the ASCII text file you want to export, you can invoke External File Interface in the Select Export Type window, write customized DATA step code with FILE and PUT statements, or use some of the ODS tagsets supplied by SAS, found at http //support.sas.com/md/base/topics/odsmarkup/, that have the ability to create numerous types of ASCII text formats. 

Figure 5 provides an example of AE monitoring data from 22.01.1997 to 03,03.1997, in terms of time history of the main plant parameters (fig.Sa), as well as of the AE RMS values (fig. 5b). Normally, very little or no events were recorded, with the exception of the above period, in which an AE activity, very much concentrated in time and space, could be observed a sharp step in cumulative AE events takes place in a short, well defined time interval. A smaller sharp step in EA events had been observed a few days earlier, in the same position. 

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. 

Get data This step involves pulling the data to be used into SAS. It often requires merging treatment or study population data with analysis data sets or some other data to be summarized/listed. 

Manipulate data On occasion the data being pulled into SAS for summarization and presentation are not ready for that purpose. In such cases, you may need to manipulate or create additional variables within the SAS program. Keep in mind that it is almost always better to create derived variables prior to this step in analysis data sets programming. 

The previous sections show you how to extract / -values for a commonly used set of statistical tests. This section describes a general step-by-step approach for getting your statistics from a SAS procedure into data sets for clinical trial table or graph reporting. Here are the steps to follow ... 

ShortName from step 3 is what your ODS table name is called. Simply wrap an ODS OUTPUT statement around your SAS procedure to create the needed data set ... 

---