DATA step

HyperChem run s the molecular dynain ics trajectory, averaging and analyzing a trajectory and creating the Cartesian coordinates and velocities, fhe period for reporting these coordinates and velocities is th e data collection period. At-2. It is a m iiltiplc of the basic time step. At = ii At], and is also referred to as a data step. The value 1I2 is set in the Molecular Dynamics options dialog box. 

At, etc. The Snapshot period is specified 111 the Snapshot dialog bo by n, data steps, i.e. as a ni ii Itiple of the data collection period, At4 = n4 i3t2. 

TlyperCi hem updates the screen diirin g a trajectory at regular in ter-vals so yon can visiiali/e the irajectory. Since this screen update may slow down a trajectory If it occurs too frequently, yon c.an specify the duration of the Screen Refresh period At.,. The screen updates at ilines tQ, Iq + Atj, to + 2Atj, etc. The Screen Refresh period is specified in the Molecular Dynamics options dialog box by n 5 data steps, i.e. as a m iiliiplc of the data collection period, At5 = n 5 At2-... 

To create a set of snapshots of any molecular dynamics run, press the Snapshot button of the Molecular Dynamics Options dialog box to bring up the Molecular Dynamics Snapshots dialog box for naming a snapshot file. A snapshot file contains snapshots of the coordinates and velocities of a molecular system along the trajectory. The dialog box allows you to name the file and decide at what frequency to take snapshots. For example, choosing the snapshot period to be two data steps implies that only every other time step is stored in the snapshot file. 

PRODUCT BFG.xls consists of the chemist s table, a reduced table obtained by casting out data that is not ammenable to statistical analysis such as subjective assessments ( off-white color, characteristic crystal form, pungent smell STEP 1), and the final table that was freed of all inconsistencies by going back to the original data (STEP 2). Various interesting items are highlighted ... 

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. 

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. 

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 ... 

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. 

O The cholesterol data set is also sorted by sample date. This is critical, as the samples must be in the proper chronological order for the DATA step that follows. 

This DATA step uses ARRAYS, RETAINS of the newly created baseline values, and a final subsetting IF to keep the proper baseline cholesterol variables. 

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... 

There may be times when a DATA step with arrays is a better means to transpose data. This is true when the data to be transposed have more than one record per BY group variable or when there is a need to have the resulting data set include data that are not in the source data set. In clinical trials missing data is a very common issue. Let s look at a derivation of the previous systolic blood pressure transposition problem where visit 2 is always missing. 

Notice the missing column for visit 2. This is exactly what you would expect PROC TRANSPOSE to give you. PROC TRANSPOSE transposed the data that were present and could not be expected to know about visits that are not represented in the data. However, often in clinical trials reporting you want to report on all visits, treatments, or other expected parameters whether they are represented in the actual data or not. In this case, a DATA step with arrays is a better choice to transform the data. Here is an example of the previous transposition that includes all visits 1-5, regardless of which visits are included in the underlying data. 

Program 4.7 Transposing Data with the DATA Step... 

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. 

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). 

Now you see that death = 1 only for the Fatal MI as desired. This was accomplished by changing the name of the death variable to death on the way into the aes DATA step and then using the death variable in defining a newly created death variable. Finally, death is dropped from the outgoing copy of the aes data set. 

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 again PROC SQL is used where DATA steps may have been used before. In this single PROC SQL the treatment data are joined with the concomitant medications data and unique medications are selected within a patient. 

This DATA step rearranges the counts data set created by PROC FREQ. The data set is essentially merged with itself three times in order to get each treatment into its proper column. A group variable is created to help separate the ANY MEDICATION row from the other true medications. Percentages are calculated, and the columns (coll-col3) are formatted as XXX (XXX%). Finally, the lastrec variable is created to help make a continuation flag in the PROC REPORT 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. 

This PROC TTEST runs a two-sample f-test to compare the LDL change-from-baseline means for active drug and placebo. ODS OUTPUT is used to send the p-values to a data set called pvalue and to send the test of equal mean variances to a data set called variance test. The final pvalue DATA step checks the test for unequal variances. If the test for unequal variances is significant at the alpha =. 05 level, then the mean variances are unequal and the unequal variances p-value is kept. If the test for unequal variances is insignificant, then the equal variances p-value is kept. The final pvalue data set contains the Probt variable, which is the p-value you want. 

DATA STEP METHOD TO CREATE A TRANSPORT FILE. data dm.dm ... 

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