Title: Managing very large EXCEL files using the XLS engine

The use of SAS V5 transport files, unfortunate, is still currently required for FDA submissions. Many users in the pharmaceutical industry, however, are not well versed on the structure or detemining the contents of transprort files. It is quite common for many of us to receive a SAS transport file from a vendor or a CRO. The first question is always – is it a .XPT file format or a .CPT file format. Next, we don’t know what the contents are. Is there one dataset or are there multiple datasets. Of course, a proficient programmer could quickly write some code to look into and /or extract the desired datasets. The average user that doesn’t do this task very often, however , needs a simpler solution. This paper describes a utility macro that automatically determines the transport file type (.XPT or .CPT), the list of datasets it contains, the list of variables (plus their attributes) in each dataset and allows the user to extract any or all datasets. Author(s): John H Adams Title: %ANYTL: A versatile Table/Listing Macro Paper Number: AD09 Abstract: Unlike traditional table macros, %ANTL requires only 3 parameters (corresponding to the row, column and title/footnote of a table respectively), but can handle most typical tables/listing production tasks. It is more like a simple language, using a grammar similar to PROC TABUALTE: distinguish continuous, categorical data and text with just a few specific special characters. %ANYTL is also very flexible in that it allows the user to manipulate the summary result prior to output to a table. The big N, column width, pagination, wrapping of the table can be either automatically calculated or specified by the user. This macro has proven to be powerful while succinct and easy to use. Author(s): Yang Chen Title: Data capture to study-specific metadata for analysis datasets: Automation along the way Paper Number: AD10 Abstract: Development of analysis datasets and associated metadata that conform to industry (CDISC) standards is one of the essential steps in preparation and analysis of clinical trial data intended for FDA submission. Data captured at investigation sites through case report forms (CRFs) and stored in a database is often subjected to a sequence of processing steps eventually resulting in analysis datasets. Metadata of the datasets and variables in the analysis datasets form a vital part of the Define document (of any study / trial) that is submitted to FDA. We outline a strategy (based on a SAS macro we developed) towards automating some of the steps involved in generation of study specific metadata for analysis datasets. Author(s): Sathish Sundaram, Ganesh Munuswamy, Mominul Islam Title: Converting CDISC Controlled Terminology to SAS Formats