Information is knowledge about a fact or facts that has been transmitted to one or more people. SAS is used to generate information from data. SAS-generated information can take the form of listings organized by sorting data or statistical analyses of data. The information (SAS output) can be transmitted in either a tabular or graphical representation. Frequently the output is stored in a database or spreadsheet and printed directly from these sources for viewing. End users of SAS output may also need to incorporate this information into documents created by word processing programs. The simplest form of information transfer to word processing programs has been to download data process reports (DPRs) that have been saved as ASCII text. However, simple ASCII downloads are difficult to use when complex formats are needed for in-text tables and summary documents, especially when the use of proportional fonts in the document text are required. An additional drawback to this approach is that the final format of the information is a display that is difficult to manipulate if further analysis of the data is required. A more sophisticated and useful approach is to transfer the SAS output into "tables feature", a mini spreadsheet feature available in high-end word processing programs such as WordPerfect (WP) or Microsoft Word (MSWord). Tables generated in tables feature allow editing by column as well as row. We have used several methods to transfer SAS output into tables feature in both WP and MSWord. A direct approach is to use delimited ASCII files that the word processing program recognizes as a database or spreadsheet format and incorporates as a translated spreadsheet. This process can be facilitated by macros written in the word processing program. We have also used an alternate approach in which SAS output that has been stored in simple ASCII format can be filtered through a spreadsheet program and then incorporated into a word processing-generated document. SAS, spreadsheets, and word processor tables features complement each other in the exchange of information, and information is the necessary goal of data analysis.

Dan Benau is a Senior Scientific Writer in the Wyeth-Ayerst Research Department of Clinical Communications. He received his Ph.D. in Biology from Boston University in 1984. He has had a long-term interest in the direct input of computer generated data in scientific documents and has reported on several novel methods for doing this using both the Macintosh and DOS-based computer platforms.

Dan DiPrimeo is principal statistician in the Clinical Biostatistics Department at Wyeth-Ayerst Research. He has a masters in and is currently a PhD candidate in mathematics. He has been working at Wyeth for over 5 years and has been programming in SAS for 7 years. He has authored or co-authored papers which describe getting SAS generated information into word processing programs.

SAS software contains many powerful functions for manipulating character data. This talk will demonstrate some of the more useful examples. Some of the functions we will explore are: VERIFY, TRANSLATE, COMPRESS, LENGTH, SUBSTR, INPUT, SCAN, UPCASE, LOWCASE, INDEX and INDEXC. We will also take a quick look at how the length of character variables is determined. This tutorial should be beneficial to beginning SAS programmers. However, some useful features and tricks will be included to interest those with more experience.

Dr. Ronald Cody is an Associate Professor in the department of functions using simple Environmental and Community Medicine at the Robert Wood Johnson Medical School, Piscataway, New Jersey. He has been a SAS user for more than 17 years and is the author of Applied Statistics and the SAS(r) Programming Language, third edition (fourth edition due this spring). Along with Ray Pass, he has written a book called SAS(r) Programming by Example, a book of annotated SAS examples, published by the SAS Institute as part of their Books by Users series. His latest book, The SAS Workbook, was published recently by the SAS Institute. Ron has presented invited papers for numerous local, regional, and national SAS conferences.

The subject of this paper is the analysis of data from a randomized, parallel group, multi center clinical trial in patients with Obsessive Compulsive Disorder (OCD). The purpose of the study was to evaluate the efficacy and safety of three dose levels of PaxilTM* (paroxetine HCl) versus placebo. The level of the patients' illness was measured using the Yale Brown Obsessive Compulsive Scale (YBOCS) at baseline and at weeks 1, 2, 3, 4, 6, 8, 10, and 12. The data set included data points which were missing due to skipped visits and patient dropouts.

PROC MIXED of the SAS System was used in two alternative approaches to the analysis of the data from this study. The first is a repeated measures analysis. Secondly, PROC MIXED was used in a random coefficient regression analysis. In this analysis the hypothesis of interest was the differences in slopes (rates of improvement) for the doses of Paxil and placebo.

Will Bushnell received his undergraduate degree in Biology from Mercer University in Macon Georgia in 1976 and received a Master of Applied Statistics degree from Louisiana State University in Baton Rouge in 1982. From 1982 until 1985 he worked in the Biometrics group at Schering Plough. In 1985 he went to work for Beecham Labs and moved to Philadelphia as a result of the SmithKline French and Beecham merger in 1990. The major part of his career has been in clinical development, he has been a leader in submissions for CNS and AI compounds. Currently he holds an Associate Director level position responsible for Phase 4 development of 9 compounds.

The new release of SAS software provides several additional functions and an interface for functions to be used in macro programming. These new features make it possible to use functions in new ways in SAS programming. This poster describes the macro language interface to data step functions and demonstrates several simple applications of data step functions outside of the data step.

Rick Aster is the author of several books on SAS software. He co-wrote "Professional SAS Programming Secrets", which is now available in an updated edition.

The purpose of this paper is to demonstrate the powerful features of PROC SQL in the creation of Ad-Hoc reports for clinical trial research. The Ad-Hoc Reports we create are for the summation and statistical analysis of Clinical Trial Research Data. Our programs are flexible, summarize and analyze Phase I to Phase III Clinical Trial data, the adverse events, study medications, and clinically significant laboratory test values. In these SAS programs, the use of PROC SQL affords for shorter, easier programs with short execution times, while making SAS program code efficient, flexible and maintainable.

Anne Mare S. Smith graduated from LaSalle College in 1980 with a B.A. in Mathematics and Computer Sciences. During the 1980's Anne taught Computer Science courses at LaSalle College in the evening division. She is currently working as an Information Systems Engineer for CANDA system at Wyeth-Ayerst Research. Prior to joining W-AR, Anne worked for 8 years at GE Space Systems and Sperry Univac Corporations in their Management and Data Systems groups as a Systems Analyst. She worked on various projects; International Airline and Weather Realtime Systems, Office Information Operating Systems, Compilers and Realtime Satellite Communication Systems.