PharmaSUG 2010 Papers - Index

158 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	"Had a Strange DATE" - problem solved. Now as SAS gets older will ANYDT do?	Venky Chakravarthy	This is a sequel to a 2002 paper that was written before the ANYDTxxx INFORMATS were introduced. This paper draws on the salient points from that paper and exa mines the pros and cons of the ANYDTDTE INFORMAT. While recognizing the uses of this new family, caution is advised in using them as there are some pitfalls f or the unwary	date variable, sysdate9	5
2	%disposition_plot: A Macro Application for Clinical Study Disposition Analysis	Yao Huang	Patient disposition information (e.g. enrollment, randomization, discontinuation and completion) is indispensable in analyzing clinical studies. Usually we summarize this information in one table after data lock. The table may include total number of patients and percentage in each disposition category by treatment. However, sometimes sponsors or investigators need to find out time-dependent patient disposition information. For example, patient enrollment is usually a time consuming process for large late phase multicenter clinical trials. Investigator sites with efficient enrollment need to be identified for future similar or ongoing clinical trials. Another example is about patient discontinuation. It is well known that patients drop out during clinical trial process due to different reasons such as TEAE or death. Investigators need to compare dropout rates by treatment during the study. Therefore, we need to take the study time into consideration	clinical trial, randomization, disposition table	8
3	%OBJECT_EXIST: A Macro to Check if a Specified Object Exists	James R. Johnson	This paper describes a macro designed to quickly tell the program whether or not an object exists. The types of objects the macro can detect are SAS datasets, external files, open libraries, open filerefs, macro variables, formats, informats, and specific variables in a dataset. The macro is designed to either print a message to the log and set a user error flag, or set a user defined 'found flag' and allow the calling program to handle the results of the OBJECT_EXIST macro. OBJECT_EXIST is a useful macro for applications, utilities, and one-time only programs because all the search algorithms are in one place, validated, and being a macro, only the section of code needed to search for a specific item will be included in the job stream	dictionary tables, proc sql, proc step	7
4	%rtf2data : A utility macro to convert RTF Table to SAS dataset	Neeral Beladia	Often in the pharmaceutical industry, we face the cha llenge of reverse engineering, where we have archived summary Tables or Listings in the form of RTF Table, however the programs that led to their generation are untraceable. In scenarios like these or when there is a need to perform extended statistical analyses on the RTF Table data or when it is critical to ens ure a match between multiple versions of RTF Table, it would be nice if we would be able to convert the RTF Table into a SAS dataset. This paper focuses on presenting a utility macro(%rtf2data) that converts most co mmonly used RTF Table templates to SAS dataset. The macro executes in a 2-phase process. In the first phase, the macro reads in a user-defined meta-data that defines the structure of the RTF file and also defines the attributes of variables in the output SAS dataset	proc report, macro variable, excel, proc step, adverse events	11
5	%TABLEMAKER: Data Reporting Made Easy	Bernadette H. Johnson	Reporting data can be challenging, especially when analyzing both continuous and categorical data with the goal of presenting all of the results in the same table. In three steps, the TABLEMAKER macro generates user -selected descriptive statistics and presents results as an integrated table. In Step 1, the user provides overall table parameters such as: page orientation and data set name. In Step 2, the SAS variables and the desired list of descriptive statistics are defined for each horizontal table section. Step 3 generates and executes the code to create the table. The TABLEMAKER macro is a user-written tool that provides an easy way to create tables for analysts, statisticians, researchers	proc means, proc freq, frequency analysis, where statement, macro variable, out=, proc step, macro definition, by group processing	6
6	%whatChanged: A Tool for the Well-Behaved Macro	Frank C. DiIorio		macro variable, create table, order by, sas log, macro facility, out=, %if-%then-%else, proc sql, macro definition	14
7	A Methodology of Quantifying Subjective Measurements	Sudhir Singh	SAS programmers are frequently in the situation to build a macro or system that is known to improve cycle time or quality. Although a lot of time is spent in building such automation, many times we do not put our attention toward demonstrating the actual cost savings to our stakeholders who truly have such interest, say, executive management. We have a simple yet effective systematic approach on how to turn subjective gains or losses into objective measurements. This methodology may be helpful for programmers to supplement the development of business cases and make reliable judgment calls when building automation that can be used to demonstrate the quantifiable cost savings in quality and efficiency as a result of our work	sample, data cleaning	5
8	A SAS macro to generate Caterpillar plots	Guochen Song	Caterpillar plots are widely used in meta-analysis and it only requires a click in software like Winbugs. However, there is no SAS procedures that generate such graphs directly, and SAS users usually get around of this by applying `PROC` GPLOTS twice. This paper presents a macro to draw this type of graph completely using the annotate facility	proc step, data step, macro definition	5
9	Accessing and using the metadata from the define.xml	Lex Jansen	When submitting clinical study data in electronic format to the FDA, not only information from trials has to be submitted, but also information to help understand the data. Part of this information is a data definition file, which is the metadata describing the format and content of the submitted data sets. When submitting data in the CDISC SDTM format it is required to submit the data definition file in the Case Report Tabulation Data Definition Specification (define.xml) format as prepared by the CDISC define.xml team. This paper illustrates how SAS XML Mapper technology can be used to turn the metadata from the define.xml file into relational SAS data sets. SAS can use the metadata from the define.xml in various ways. Examples include: - Building SAS formats from the define.xml code lists - Building dataset templates from the define.xml itemgroups and items	dataset, format statement, csv, clinical trial, data cleaning, annotated crf, data standard, surrogate key, proc sql, variable, cdisc	22
10	Accessing and using the metadata from the define.xml	Lex Jansen	Presentation	dataset, crf, data standard, structured data, proc format, ods pdf, data cleaning	74
11	Accessing the metadata from the define.xml using XSLT transformations	Lex Jansen	When submitting clinical study data in electronic format to the FDA, not only information from trials has to be submitted, but also information to help understand the data. Part of this information is a data definition file, which is the metadata describing the format and content of the submitted data sets. When submitting data in the CDISC SDTM format it is required to submit the data definition file in the Case Report Tabulation Data Definition Specification (define.xml) format as prepared by the CDISC define.xml team. This paper illustrates how XML technology (XSLT) can be used to turn the metadata from the define.xml file into executable SAS code. We will create the following examples of executable SAS code from the define.xml file: - SAS formats from the define.xml code lists - Data set templates from t he define.xml itemgroups and items	proc step, proc format, controlled terminology, create table, dataset, proc sql, cdisc	24
12	Accessing the metadata from the define.xml using XSLT transformations	Lex Jansen		crf, proc format, clinical trial, cpu time, sample, csv, csr	70
13	Achieving Efficiency in CDISC-SDTM Conversion and its Challenges - Case Study	Ilango Ramanujam		raw data, data cleaning, cdisc	6
14	ADaM Dataset Checking Toolkit	Huei-Ling Chen	An analysis dataset in a clinical trial should be clear and logically unambiguous. The Clinical Data Interchange Standards Consortium (CDISC), a non-profit organization established to define standard data formats to support the exchange of clinical data among and within organizations such as pharmaceutical and medical device companies, contract research organizations, universities, medical research institutions, and regulatory agencies recently provided the ADaM Implementation Guide (ADaMIG), Version 1.0. to specify the preferred features of the Analysis Data Model (ADaM) for a clinical trial. The purpose of this paper is to describe macros to assist in validation of an ADaM dataset. Based on the ADaMIG v1.0, this paper provides six macros as a toolkit to evaluate whether the dataset complies with the CDISC Analysis Data Model	adam dataset, proc sql, proc step, character variable, numeric variable, missing value, sashelp, macro loop, cdisc	9
15	Add the Power of Regular Expressions to SAS Macro	Lei Zhang	Regular expressions, as a powerful, efficient, and flexible text manipulation facility in many applications, have been gaining popularity in the SAS community since Perl compatible regular expressions were introduced in SAS 9. However, many SAS users are unaware that the power of regular expressions can be made available in the SAS macro language. The goal of this paper is to get you star ted with using this facility in SAS macros. The paper first explores key concepts and syntaxes of Perl regular expres sions (PRX) and then describes the ways to use them in SAS macros by taking advantage of the PRX functions and CALL routines built in the `DATA STEP` via %SYSFUNC function and %SYSCALL statement. Numerous real-life examples are provided to expose the strengths and subtleties of Perl regular expressions when used in the SAS macros	macro definition, macro facility, macro variable, macro function, macro parameter, sas 9	16
16	An error bar macro with PROC GPLOT	Shuping Zhang, Xingshu Zhu	Error bar plots (e.g., SD, SE, and confidence intervals) are often used to visually represent and explore the relationship among data values, as well as to compare the outcomes of different studies or treatment groups over a period of time. While enhanced features in `SAS/GRAPH` procedures including statements and annotations can be used to make quick and informative graphics, challenges remain for SAS programmers to create graphics for sophisticated analysis purposes such as error bar plots. To address this challenge, we developed a customized mean measurement macro using the `PROC GPLOT` procedure in order to accomplish more comprehensive graphic outputs specifically targeting error bar plots	bar chart, inferential statistics, macro call, sample, proc step, proc gplot	11
17	An Overview of the CLASS, CONTRAST, and HAZARDRATIO Statements in the SAS 9.2 PHREG Procedure	Paul T. Savarese, Michael J. Patetta	The `PHREG` procedure fits a number of models collectively known as Cox regression models, including the well- known Cox proportional hazards model. This paper provides an overview of several new features, including three new statements (CLASS, CONTRAST, and HAZARDRATIO) in `PROC PHREG`. The emphasis is on illustrative examples of comparisons for main effects and interaction models via the new statements. The paper concentrates on common CLASS variable parameterization methods such as reference coding and GLM coding. Caveats regarding CLASS variables and time (including time-dependent covariates) are also discussed. This paper is intended for an intermediate-level audience that has some familiarity with Cox regression models and `PROC PHREG`	hazard ratio, class variable, proc phreg, proc genmod, proc step, nominal variable, interval variable, binary variable, time-dependent covariate, hazard function, estimate, options statement, regression analysis, main effect, likelihood function, confidence interval	23
18	Analytics in the Converging Health and Life Sciences Industries: The Catalyst for the Next Generation of Healthcare	Matt Gross	Learn how analytics serves as a catalyst for both healthcare reform and the foundation for the next generation of healthcare. Understand the hurdles and opportunities presented as health information converges from the health and life sciences industries and presents new business opportunities for each industry and improved quality of care for the healthcare consumer	clinical trial, clinical research, life sciences, adverse events	15
19	Appending Many Individual Graphs Into One PDF File, With Custom Bookmarks	Scott Burroughs	In my last paper (PharmaSUG 2007), I presented a method to use graphics catalogs and `PROC GREPLAY` to append many individual graphs into one (PDF) file, in addition to appending many `PROC GSLIDE` outputs into one PDF file. At the time, this appended PDF file did not have bookmarks to each individual graph (or subject). Now, my programs have been updated to create custom bookmarks of different types for each graph	proc step, catalog entry, ods pdf, proc greplay, macro definition	10
20	Application of Modular Programming in Clinical Trial Environment	Mirjana Stojanovic	This paper describes a modular approach to developing a complex data error checking program. A module is a collection of functions that perform re lated tasks. We use a series of SAS macros to develop each module. SAS macros are used intensively because they reduc e code volume and improve program reliability and readability. SAS programs generated from SAS macros are dynamic and flexible. In this way the application of the program is much more flexible than in the traditional design as one monolith program. Our program works for many studies with no any intervention into program code. In implementation of these checks, we have developed and use a specification file in which the user indicates the modules, and specific errors within the modules, to be performed. Then, where necessary, th e user will provide addition information within the specification file regarding the tables and variables to be used in performing the checks	macro definition, best response, data cleaning, master data management, adverse events, macro variable	8
21	Applying ADaM Principles in Developing a Response Analysis Dataset	Mei Dey, Lisa Pyle	The Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) is an emerging new industry standard for submitting analysis datasets to regulatory agencies, such as the U.S. Food and Drug Administration (FDA). While it provides many solutions for construction of analysis datasets, it is subject to interpretation by users during implementation. This paper explores an early application of ADaM principles in developing a response analysis dataset to meet the analysis requirements for reporting. It describes the process of constructing an analysis dataset: obtaining trace records, developing endpoint records and concatenating data into one final ADaM dataset. A specific example is provided, based on the draft ADaM Implementation Guide (IG) using oncology data but the solutions presented could be expanded to other therapeutic areas as well	adam dataset, adrs, efficacy summary, survival analysis, sample, sdtm domain, cdisc	8
22	Assessing superiority/futility in a clinical trial: from multiplicity to simplicity with SAS	Philip J. d'Almada, Laura Aberle	SAS macroprocessing was implemented to deal with a simple question that was demonstrated to be more extensive when pursued to completeness. That is, given the sample size, what proportion of successful events would need to be arrived at to consider a study drug as superior or the study as futile at some given significance level. Using the procedure that was developed, the consultant was able to provide the client with, not just a variety of scenarios by which to formulate clinical trial judgements but an exhaustive and complete review based upon the initial request. With this procedure, furthermore, the client was able to gain a sense of the scope of the enormity of a proper assessment of the request without the use of SAS, and within a reasonable amount of time	fisher exact test, treatment arm, exact test, sample, sample size, significance level, numeric functions, &variable, macro variable, clinical trial, p-value, data step, proc step, macro definition, proc freq, %if-%then-%else, output delivery system	9
23	Assigning NCI CTC Grades To Laboratory Results	Carol Matthews	Oncology studies often collect a significant amount of laboratory data for each patient. One of the key ways of analyzing this data includes classifying results into severity grades based on National Cancer Institute (NCI) Common Terminology Criteria (CTC) for Adverse Events. Assuming that all lab results have been recorded in (or converted into) common units, it can be challenging to properly assign CTC grades to your results. This paper will introduce programmers to CTC grade criteria, the issues surrounding assigning grades to the results, and methods to overcome these issues	adverse events, numeric variable, laboratory data	8
24	Automating Comparison of Integrated Studies vs. Individual Studies	Sandeep Kottam, Sree Lakshmi Kalyani Tripuraneni	Have you ever been asked to compare integrated study versus Individual studies during the process of clinical data submission? SAS macro facility along with metadata files with rules on how to compare can be used for orchestration of this process. The Rules metadata file contains information about study names, key variables to be used to compare each dataset and variables not to compare for each dataset. Focus on the metadata file will result in the tool providing meaningful output to review. This paper will discuss the process and identify how it provides confidence in the output	statistical programmer, call symput	4
25	Automating the Link Between Metadata and Analysis Datasets	Misha Rittmann	This paper introduces the %MGENZOD macro, a metadata management tool that generates a zero observation dataset from an `EXCEL` specification document. By using this automatically generated dataset, programming teams are one step closer to ensuring consistency between metadata and analysis data throughout the course of a project/delivery/submission. The tool offers benefits and process improvements to the team. Also, the inclusion of zero observation datasets in the development process is consistent with the process used in the CDISC SDTM/ADaM Pilot Project	adam dataset, macro variable, proc sql, proc step, cdisc	5
26	Automating the Process of Creating the Contents of Data Definition Document	David L. Wang	Creating a data definition document is an important step for any newly created dataset. It can either be done before the development of any datasets or after the actual creation of a dataset, but in any case, it must be done before the validation of those databases. The 1999/2003 FDA electronic submission (eSub) guidance and the electronic Common Technical Document (eCTD) documents clearly specify that a document describing the content and structure of the included data should be provided within a submission. Normally three important Data Definition Documents are needed for a submission to FDA. The first one is for ODM (formally called CRT DD and commonly referred as "define.xml") to provide Case Report Tabulations Data Definitions in XML format. The second one is for SDTM to provide Study Data Tabulation Data Definitions in XML format, which has been mandated since year 2003. The third one is for ADS/ADaM to provide Analysis Data Definitions in PDF format	create table, excel, proc step, cdisc	10
27	Building a Successful Remote Programming Site	Wenyun Ji	Building a statistical programming team from scratch is a big challenge, and building the team on a remote site is an even bigger challenge in all aspects of recruiting, training, roles & responsibilities, and communication. The author discusses, from her own experiences, the challenges and solutions in building a remote programming site that functions effectively as part of a large mult i-site global biopharmaceutical company	clinical programming, problem solving	4
28	Building Effective Statistical Programming Teams for Clinical Trials	Dawn Edgerton	In resourcing a team of statistical programmers for a clinical trial, management must consider many aspects. The dimensions to be considered are experience with the client, knowledge of therapeutic area, complexity of study, scope of deliverables, timeline, programming skill set, and availability of each team member. These dimensions must be ranked and project risks must be identified. This paper explores resourcing considerations and well as methods of team communication to keep the project on target with meeting milestones	adam dataset, clinical programming, adverse events, data cleaning	3
29	Building the Better Macro: Best Practices for the Design of Reliable, Effective Tools	Frank C. DiIorio	The SAS macro language has power and flexibility. When badly implemented, however, it demonstrates a chaos- inducing capacity unrivalled by other components of the SAS System. It can generate or supplement code for practically any type of SAS application, and is an essential part of the serious programmer's tool box. Collections of macro applications and utilities can prove invaluable to an organization wanting to routinize work flow and quickly react to new programming challenges. But the language's flexibility is also one of its implementation hazards. The syntax, while sometimes rather baroque, is reasonably straightforward and imposes relatively few spacing, documentation, and similar requirements on the programmer. In the absence of many rules imposed by the language, the result is often awkward and ineffective coding. Some amount of self-imposed structure must be used during the program design process, particularly when writing systems of interconnected applications	macro variable, macro facility, macro definition, macro loop, macro put, data cleaning, automatic macro variable, keyword parameter, %macro parameters, sas log, error message, sysrc, %if-%then-%else, symbol table, macro function, autocall macro, macro parameter, options statement, proc step, dictionary tables	16
30	Business Intelligence 2.0: Are we there yet?	Gregory S. Barnes Nelson	Business intelligence has been with us for some time now and, we would argue not much has changed. Advances in the consumer communications and the entertainment world have dwarfed commercial software applications. Consumers have unprecedented access to information and tools with which to consume information. Social media, mobile access, augmented reality and 3D views of pictures and video have created a disparity between our private and work personas. BI has seemed to lack in any real innovation. In this paper, we will outline what the next chapter of innovation should contain for business intelligence, analytics and data integration. Some hints of these features exist today, while others have lagged behind as the industry has gone through a number of mergers and acquisitions. Those vendors that have survived the economic turmoil can now turn from treading water to making waves. Table of Contents	business intelligence, data integration	10
31	Capturing Tabular Data from Graphical Output Part 2: Web and Windows-Based Tools	Brian P. Fairfield-Carter, Stephen Hunt	This paper serves as a sequel to "Reverse-engineer a Reference Curve: Capturing Tabular Data from Graphical Output", presented in Coder's Corner at PharmaSUG 2009, and provides greater detail on applications-programming techniques for capturing data from graphical output. In some cases, the raw data values that produced a graph are not available, and have to be 'reverse engineered' from the graphical output if they are to be used in further (i.e. comparative) analyses. One approach is to capture screen coordinates and translate these to plot coordinates, and while there are ubiquitous applications such as Windows Paint that display screen coordinates, there appear to be none that automatically log screen coordinates for selected data points. This paper describes two applications, one a web-based HTML Application and the other a Windows-API-based application, that enable the user to display graphical images and capture and log screen coordinates	dataset, clinical research	11
32	CDASH Standards for Medical Device Trials: CRF Analysis	Parag Shiralkar, Jennie Tedrow, Kit Howard, Laura Fazio, Daniela Luzi, Rhonda Facile		medical history, proc freq, frequency analysis, pharmaceutical industry, vital signs, adverse events	8
33	Checking for SDTM Compliance: The Need for Human Involvement	Fred Wood, Adrienne Boyance	An increasing number of sponsors are submitting clinical trials data to the FDA in the format of the CDISC (Clinical Data Interchange Standards Consortium) SDTM (Study Data Tabulation Model). In many cases, however, the data were not collected, stored, or extracted from the database in the SDTM format. As a result, the data must be converted, and must meet a number of structure and content requirements described in the SDTM and the SDTM Implementation Guide (SDTMIG). A number of companies/organizations have developed automated tools to check for what's commonly referred to as "SDTM compliance." Most of these tools check data as well as the define.xml file, and they play an important role in many organizations' validation processes. Because of the diversity of clinical trials data, however, even the best tools cannot identify all the areas where problems in SDTM compliance and accurate data representation may exist	controlled terminology, sdtm domain, data governance, clinical data, data transformation, label statement, adam dataset, clinical trial, concomitant medications, data cleaning, adverse events, cdisc	8
34	Clinical Programming For Novice	Ramesh Ayyappath	Clinical SAS programmers come from diverse background. As programmers step into this new field, they would have enough working knowledge about SAS techniques and how to program tables, listings, and graphs. However, as in any other field, there are lots of everyday activities, terminologies and processes that a programmer should be aware of in order to be successful and will learn on the job over a period of time, depending on the work environment. This paper is primarily targeted towards programmers who are relatively new to the field of clinical programming and the objective is to provide an early introduction to the various aspects of clinical programming	clinical trial, adverse events, clinical data, clinical programming, clinical research, data science	5
35	ClinicalTrials.gov: Creating an XML upload from SAS	Daniel J. Boisvert, Andy Illidge, Troy A. Ruth	On September 27, 2007, a law was enacted requiring expanded registry of clinical trials, in order to support increased transparency. The law included a timeline requiring all clinical trial sponsors to register Phase II-IV drug and device trials to ClinicalTrials.gov in December 2007, report basic results for registered trials in September 2008 and, most recently, post adverse event information by September 27, 2009. Enhanced result reporting is scheduled for September 2010. The increasing demands of this law has sent the industry scrambling to figure out a way to report the necessary results for thousands of clinical trials and millions of data points, in a short time frame. In most companies, responsibility for this task has been placed with Regulatory Affairs, who alone have tried to meet the challenges. Ideas such as outsourcing data entry, to hiring a huge team of data entry personnel have been floated around	clinical trial, adverse events, phase ii	11
36	Combining RTF files from Different Sources	John Iwaniszek, Natalie Walker, Laura Lovette	Statistical output in the form of Rich Text Format files has superior utility over plain text output. SAS programmers have generated RTF using their own SAS software tools and more recently, SAS `ODS`. The problem of combining independent RTF files into a master document is solved by removing the document header and closing brace (}) for component files before combining. A unique problem exists with SAS `ODS RTF` output where section breaks are used in place of simple page breaks. This problem is solved by removing section breaks along with redundant section-related tags before independent RTF files are concatenated. This poster describes combining both types of RTF files into the same master documents	data step, txt, dataset, ods rtf, proc report, proc step	14
37	Considerations for Building an Integrated Safety Database Using SAS	Denise J. Smith, Daniel Schulz, Gayle Kloss, Wei Cheng	As our company's pipeline of drugs, which all utilize the same unique technology, continues to expand, access to an Integrated Safety Database (ISDB) has become an invaluable resource. The ISDB includes all available clinical trial data collected across multiple compounds and trial phases. In practical terms, this means we have the ability to gain access to safety data across different protocols and subpopulations of patients. We have the ability to track single subjects through several studies, pool data for adverse event (AE) tracking, and gather lab results over longer durations of time than those shown in a single study. Using SAS, we can quickly and easily access and analyze data from any of our nine core, harmonized datasets to create SAS datasets, SAS data views, tables, listings, and graphs. This paper will discuss the tools and procedures we used to build a solid, and continuously expanding, ISDB using SAS	adam dataset, laboratory data, clinical trial, macro call, excel	10
38	Countdown of the Top 10 Ways to Merge Data	David W. Franklin	Joining or merging data is one of the fundamental actions carried out when manipulating data to bring it into a form for either storage or analysis. The use of the `MERGE` statement inside a datastep is the most common way data is merged within the SAS language but there are others. Based on an unscientific poll of SAS programmers and others who use SAS, this paper gives a countdown of the top 10 ways to merge two datasets, introducing the SAS code needed to join the datasets into one. Although discussion is focused on a one-to-one or one-to-many merge, some of these methods can be, and will be indicated as such, adapted to a many-to-many merge	proc sql, merge statement, hash table, set statement, proc step, point= option	6
39	Create Comprehensive Patient Profiles with SAS	Sanjay N. Matange, Daniel O'Connor	Patient profile reports visually display a patient's data along a common time axis, providing medical reviewers with an at-a-glance view of a patient's clinical assessments over time. SAS provides a rich toolset to efficiently create these important graphical reports. This approach can easily be used for multiple studies, and may be customized for a specific study's needs. By efficiently producing these reports, mountains of clinical data can be transformed into meaningful information to assist researchers in fully understanding how patients respond in clinical trials	adverse events, sample, clinical data, sas base, proc step, proc freq, vital signs, proc sgplot	12
40	Creating a Lean and Flexible Programming Environment for Analysis and Reporting	Lisa Pyle, Ginger Redner	Key elements of a highly efficient programming organization are a well organized SAS global macro library with a standardized infrastructure, standard macros, user support and a defined process regarding the maintenance of the library. This paper will cover how the macro library in our organization is designed, used, and maintained. Details of the benefits gained for investing in a global macro library will be discussed, as well as the challenges associated with developing, maintaining and using a highly standardized library. This paper is intended for members of any organization looking to increase programming process efficiency, create a more flexible programming staff and simplify the process of outsourcing programming duties	macro variable, data cleaning	6
41	Creating Customized Word Tables with Table of Contents using SAS ODS RTF for Clinical Studies	Sue X. Zhang	SAS `ODS RTF` destination allows the output tables to be directly copied into Word documents by the users. It creates the linked table of contents (TOC) pages that take you directly to the desired output with just a mouse click. The `TEMPLATE` procedure can customize SAS output reports by having control over the margins, font type, font size and justifications etc. This paper demonstrates the step by step procedures on how to apply the `ODS RTF` CONTENTS option and `PROC TEMPLATE` to generate informative TOC and customized reports with specified formats. It also describes the methods for generating publication quality tables using `PROC TABULATE` and `PROC REPORT` in addition to the `ODS RTF` CONTENTS option and `PROC TEMPLATE`. Some issues with these procedures will be discussed. The data used in this paper is either directly obtained or modified from SAShelp.CLASS for demonstration purposes only	proc step, proc print, proc template, proc report, proc tabulate, title statement, output delivery system, clinical trial	12
42	Creating SV and Se First	Henry B. Winsor, Mario Widel	One current concern of the FDA is that many sponsors are not submitting both the SV and SE data sets when submitting studies for review. The Authors address reasons why the data sets are useful other than as a piece of a final submission, even to the point of encouraging the sponsor to create these data sets as soon as clinical data is available. One method for easy creation is demonstrated, along with a verification method	crf, clinical data, raw data, adverse events	7
43	Data Annotations in Clinical Trial Graphs	Sudhir Singh	Graphical representation of clinical data is used for concise visual presentations of the results. SAS programmers are frequently asked to generate Kaplan-Meier plots, along with graphs of ECG, and clinical laboratory data. Although Kaplan- Meier plots are easily generated using `PROC LIFETEST`, reviewers often require additional information such as the number of patients at risk. The Annotate facility in `SAS/GRAPH` allows us to create customized modification to the graphics output. In this paper I will describe how to create annotations in Kaplan- Meier and line graphs from statistical procedures. Furthermore, these methods can be applied to annotations of other types of graphs	proc step, clinical trial, change from baseline, survival analysis, kaplan-meier plot	10
44	Do you want to do Matrix Calculations with Base SAS Software without SAS/IML? No Worries.	Sarfaraz Sayyed, Binoy Varghese	Matrix calculations are required in various circumstances e.g. while solving a set of equations, for statistical analysis, etc. Matrix calculations can be done using `PROC IML`, however this procedure is not available in Base SAS as `PROC IML` is part of `SAS/IML` software. This paper will discuss how to do various types of matrix calculations using Base SAS software	sas base, proc step, proc sql, proc iml, macro definition	11
45	Don't run away, it's only Labs	Lucy Wang, Paul K. Slagle	Regardless of what clinical trial you are doing, you are almost assured of laboratory data to incorporate into the tables and listings for a Clinical Study Report (CSR). Sometimes they are central labs that are nice and clean and other times, or most of the time, they are local, dirty, messy, globs of data requiring the skills of a programmer to massage this data into information that can be used in a study. Sometimes, it comes very close to magic. In this presentation, we will provide guidance on how to take laboratory data as it is received and convert it into data that can be used. Methods for converting laboratory results to standardized results, apply laboratory normals, and then determine CTC grading will be shown. While not every situation that can be encountered will be covered, many of the more common ailments of laboratory data will be addressed	laboratory data, clinical trial, adam dataset	4
46	Dynamic Determination of Mixed Model Covariance Structures in Double-blind Clinical Trials	Matthew Davis	With the computing power of SAS software, the use of repeated measures models in double-blind randomized clinical trials is possible. However, with the large size of various clinical trials and the blinded nature of the data, it can be difficult to determine the covariance structure that should be used in the mixed model analysis. While the unstructured covariance matrix is considered the best structure to use for a blinded clinical trial, cases arise where this covariance fails to converge. In order to ensure that appropriate analysis is still performed on the data, alternate methods must be pre-specified before unblinding that will determine the best covariance structure if the unstructured covariance fails to converge. This paper will demonstrate strategies, recommendations and a macro for a dynamically programmed determination of the covariance structure that will best fit the data, which allows for flexibility in mixed model design before treatment unblinding	covariance, covariance structure, unstructured covariance, clinical trial, proc mixed, likelihood function, compound symmetry	7
47	ECG Feature Detection Using SAS	Suhas R. Sanjee, Sridhar Vijendra	ECG analysis continues to be a vital part of drug and devic e discovery especially in the cardiovascular franchise. Various parameters derived from the ECG waveform such as the P-R, Q-T, R-R, Q-R- S and S-T intervals, and heart rate variability serve as safety and e fficacy endpoints for many clinical tria ls. The detection of the R-peak and the QRS complex in the ECG is an important step in deriving these endpoints. A number of algorithms based on time and frequency domains are available for detecting various aspects of the ECG waveform. EC G features are routinely identified in core labs using al gorithms implemented in C, MATLAB , and other languages. This paper explores the implementation of ECG R-peak detection using SAS. The ECG data that is used for analysis and reporting in clinical trials is usually preprocessed. Implementing an ECG feature detec tion algorithm in-house using SAS provides the flexibility to perform exploratory analysis of parameters derived from the ECG	sample, rolling window, normalization, proc iml, proc freq, clinical trial	9
48	Editing SAS Metadata - Automated From CSV Files Using XML String in SAS Data Integration Studio	Annie S. Guo	The SAS Metadata Server introduces a new world to clinical data programmers. It is a storage centre to store information about every single object there is in the SAS System. Not only the table level and column level attributes such as SAS table labels and column lengths, extended attributes are also built in and extensible to keep valuable business data in this centralized location. To access the metadata, SAS Data Integration Studio or SAS Management Console provides a GUI environment. However, they both lack a single interface to manage all the related metadata for a table including the extended attributes. This paper presents an alternative approach. That is to maintain the metadata in text-based `CSV` files, dynamically generate XML strings with Data `_NULL_` based on the contents in the `CSV` files, and update, add or delete the metadata in SAS with the XML string. The entire process is modularized and incorporated into a SAS DI job as the front end to enable automation	dataset, sas data integration studio, macro variable, put statement, sdtm domain, proc step, macro loop, sas base, cdisc	11
49	Embedded Special Characters	Kiran K. Karidi, Mahipal Vanam, Sridhar R. Dodlapati	When the report generated from the clinical trial d ata requires to show lot of information in titles a nd column headers than they can accommodate, then Statisticians / Med ical writers use special characters such as subscri ption, superscription, Greek letters, mathematical symbols , and custom-designed symbols etc to flag, and the complete corresponding information is provided in the footno tes for that flag. There are various techniques ava ilable to embed the special characters in the clinical study report s and they will be discussed in this paper	clinical trial, clinical data	5
50	Estimation of Adherence to Antipsychotic and Diabetic Medications in a Sample of Schizophrenia Patients	Venky Chakravarthy, Thomas N. Taylor	We studied a sample of Schizophrenia patients and w ere interested in knowing (1) Are schizophrenia pat ients adherent to their medication if they are also takin g diabetic medication? And (2) How adherent to thei r medication are Schizophrenia patients who have no other co-mor bidity and are their significant differences betwee n these two groups? (3) Can we work with a simplified data structure using multiple records per patient and attain efficiency while maintaining accuracy? The results indicate that Schizophrenia patients wi th no co-morbidity are less likely to be adherent t o their medication than patients on multiple medications. However, thi s difference was not statistically significant. We were able to work with a simplified data structu re and were able to achieve efficiency in calculati ons using advanced techniques to calculate the medication pos session ration (MPR)	hash table, sample	8
51	Evolving role of statistical SAS programmers in the pharmaceutical industry	Venkata Sekhar Bhamidipati, Haiping Zhou	The high level of competition in pharmaceutical industry has recently led to mergers, organizational changes within companies, and other various types of cost-cutting measures. This trend has significantly impacted the role of statistical SAS programmers. To catch up with the fast-paced changes, a new or experienced statistical SAS programmer has to expand their skill set to meet the new expectations of the industry. Statistical SAS programmers no longer can contribute effectively with only SAS knowledge. In addition to SAS knowledge, statistical SAS programmers must have a good grasp of clinical trial database setup, eCRF/CRF development, the data collection process, etc. Most importantly, statistical SAS programmers need to hone their communication skills, in order to be able to interact effectively with different stakeholders in the new working environment	statistical programmer, sas programmer, clinical trial, pharmaceutical industry, clinical database, concomitant medications, cdisc	5
52	Exploring SAS PROC CDISC Model=ODM and Its Limitations	Elena Valkanova, Irene Droll	The CDISC Operational Data Model (ODM) is a well- established data standard amongst other known CDISC standards used within the clinical research industry. ODM is a platform-independent format that allows an exchange and archive of clinical trial electronic data from multiple sources, such as case report forms (CRFs), electronic patient diaries, or administrative data. There are three major components that are integrated in the CDISC ODM standard: a) study metadata (i.e. CRF elements and database definition); b) snapshot data (i.e. clinical data); c) transactional data (i.e. audit trail information). The ODM data structure is based on an XML (eXtensibl e Markup Language) schema to ensure consistency in how the data is represented and validated by XML applications. One important applic ation of the procedure `PROC` CDISC is the model ODM along with a combinat ion of statement parameters that allows the user to read the ODM XML data into SAS datasets	dataset, proc step, format statement, sas log, proc sql, variable, cdisc	9
53	Facilitating Data Integration for Regulatory Submissions	John R. Gerlach	The process of integrating data from multiple clinical trials for regulatory submissions poses many challenges, which is often labor intensive, as well as error prone. Even if the SAS data libraries are supposedly CDISC compliant, the process of integrating the data far exceeds a simple concatenation of data sets. Certainly, the integration process becomes even more intricate involving legacy studies that used different proprietary standards, as well as CDISC. Consequently, there are harmonization issues at both the metadata level and content level. More importantly, effective data integration is critical to the ISS / ISE analysis and the integrity of the submission. This paper explains a reporting tool, implemented as a SAS macro, that facilitates the data integration process by comparing pair-wise similar data sets at the metadata and content level	missing value, data integration, adverse events, character variable, numeric variable, libname statement, study day, verbatim term, macro definition, proc sql, proc step, %macro parameters, cdisc	9
54	From SAP to ADaM: The Nuts and Bolts	Nancy Brucken, Paul K. Slagle	Now that you have data that is compliant with the SDTM standards, and the statistician has provided you with the analysis plan, how do you develop the analyses that will meet FDA guidelines and yet make developing tables simple? The answer, of course, is to create analysis datasets using ADaM specifications. This workshop will introduce participants to the Analysis Data Model specifications version 2.1 and walk them through the creation of the ADaM datasets. Participants will create specifications and the programs needed to create the specifications. At the end of the workshop, they will create the metadata that is included with the submission	adam dataset, clinical trial, change from baseline, adverse events, treatment arm, vital signs, mock shell, adamig, sdtm domain, imputation, efficacy summary, bds, baseline, proc step, clinical data, crossover study, cdisc	9
55	Get your multi-sheet excel workbook with STYLE using ODS	Niraj J. Pandya	Working between SAS and Microsoft `EXCEL` sounds like day to day job in clinical trials data reporting. But it becomes a bit difficult when SAS is not installed on Windows platform. Also generating an `EXCEL` file with multiple work sheets becomes a daunting task and demands great set of programming skills and technical knowledge. This paper explains how to generate multi-sheet `EXCEL` workbook that contains SAS output and how to control the appearance of data in `EXCEL` from SAS using styles. This paper also explains briefly adding an `EXCEL` formula in the resulting workbook from SAS code. This paper explains how to generate such `EXCEL` workbook with the use of XML support in SAS 9 using ExcelXP type of `ODS` tagset. Technique explained here are useful irrespective of platform on which SAS is installed. Method explained in this paper generates `EXCEL` workbook that is compatible with `EXCEL` version 2002 and later	excel, xls, proc print, dataset, proc step, by group processing, date variable, class variable, format statement	11
56	Getting that New SAS Programming Job - A Management Perspective	Ellen Brookstein, Allan Glaser, Laurie Koch	It is often said that we have just one opportunity to make a good first impression, and this is certainly true when applying and interviewing for a new job. The programmer must understand the recruiting and interviewing process and be well-prepared. He/she must also submit a résumé that highlights important skills in a manner that really attracts the attention of the hiring manager. So what do hiring managers really look for in an applicant, and what are some hints for the job- seeker? This paper presents practical advice and guidance from the perspective of three experienced hiring managers	proc step, sas programmer, sample, data step	8
57	Good Clinical Practices OR "Why we do What we do the Way we do it"	Elaine Dempsey		clinical trial, gcp, sas programmer	7
58	Graphics Annotation: Tricks to determine the coordinates while using graphics/procedure output area as coordinate systems value	Sandeep Sawant	The graphics annotate facility allows the user to creat e customised modifications to the graphic output. The coordinate system values (e.g. XSYS=3, YSYS=3) in combination with the coordinates (e.g. X=50, YSYS=50) plays vital role in annotating the gr aphics. When the coordinate system assumes graphics/procedure output area (e .g. XSYS=3, YSYS=5 etc.), determining the coordinat es is often judgemental and is done by trial and error. Hence it is a time consuming task. There is no functionality in `SAS/GRAPH` which will provide the required coordinates to user instantly. This paper details the creation and use of dummy fr ames which can be superimposed on the graph (to be produced) in order to determine the coordinates quickly	forest plot, odds ratio	8
59	Handling Non-English Clinical Trials Data in SDTM Conversions for US-FDA Submissions	Ramanan D. Bathala	Converting Legacy clinical trial data in to CDISC-S DTM (Study Data Tabulations Model) for US-FDA submission purposes is challenge by itself. If these trials were conducted outside United States and that legacy data has non-English content, it poses additi onal challenges of translating them to English before converting to SDTM standard, and still maintaining the data integrity. This paper presents a case study on how this has been achieved for clinical trials data which had J apanese language contents. This paper also outlines the challenges faced and solutions adapted to address those challenges. Though this case study talks about how this has been done for clinical trial data in Japanese language, but the same concepts are applicable to c linical trials data collected in other languages as well	clinical data, service provider, clinical trial, macro variable, excel, dataset, cdisc	5
60	Help! The EX Domain is Now an Analysis Dataset! What Do I Do?!?	David C. Izard	The CDISC Study Data Tabulation Model (SDTM) [1] defines a standard structure for study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the FDA. These data tabulations, delineated in the CDISC SDTM and augmented by the clarity offered by the corresponding CDISC SDTM Implementation Guide (IG) [2], are considered electronic listings of individual observations for a subject that comprise the essential data reported from a clinical trial. To quote the CDISC SDTM IG v3.1.2, Section 2.1, "The SDTM is built around the concept of observations collected about subjects who participated in a clinical study." The challenge is that a key section has been added to the CDISC SDTM IG v3.1.2, Section 6.1.2.1 which details assumptions for the Exposure domain model, in general stating that exposure may be directly or indirectly determined and, regardless of how it is known, must be represented using the Exposure domain	clinical trial, sdtm domain, adam dataset, data science, cdisc	4
61	How to build ADaM from SDTM: A real case study	Jian Hua (Daniel) Huang	Building analysis data based on the ADaM model is highly recommended since ADaM supports a high transparency of derivation. It aims to be ready-for-analysi s and is clearly traceable to input data. This is a real application of ADaM on a phase III, randomized, double-blind, placebo-controlled clinical study. This article describes how to: create ADaM data based on f our fundamental principles from the ADaM guideline; prepare for ADaM metadata by creating analysis data specifications; understand the SDTM to derive ADaM properly; build ADaM data according to the statistical analysis plan (SAP); and re alize differences between the current and future ADaM guidelines to learn fr om the current study and make improvements for the future studies	adam dataset, efficacy summary, data science, change from baseline, sample, adamig, clinical trial, proc contents, raw data, data cleaning, cdisc	12
62	How to validate SDTM SUPPQUAL Data Set	Kevin Lee	The paper is intended for Clinical Trial SAS progr ammers who are interested in v alidating SDTM SUPPQUAL da ta set. The paper will show the basic structures of SDTM SUPPQUAL, and it will discuss the issues and the resolution of the validation of SDTM SUPPQUAL	sas programmer, data cleaning, sdtm domain, cdisc	4
63	Importing Data from RTF Output into SAS utilizing Microsoft Access/Word in an intriguing, efficient way	Ajay Gupta	In the pharmaceutical/CRO industries, SAS programmers often create summary tables and listings in Rich Text Format (RTF) using SAS `ODS`. Because of strict quality control requirements, these Microsoft word readable RTF documents are typically visually compared with the output generated on the same specifications from a second programmer or statistician. When large number (or size) of files are involved, this method is time consuming and prone to human error. However, SAS does not provide a built-in procedure to import data directly from a Microsoft word (RTF formatted) document into SAS, however use of `LIBNAME`/ `PROC IMPORT` reads data from Microsoft Access. Inspired by a published solution, this paper presents another creative way of converting word (.rtf) documents into sas datasets utilitizing Microsoft Access. The generated SAS datasets can then further be compared programmatically and by using `PROC COMPARE`	dataset, proc import, proc step, filename statement, macro call, quality control, macro definition	8
64	Improvements to a Survival Plot	Young Kim, Janice Englund	Survival plots have improved over the years with the addition of `ODS` graphics and macros to enhance their appearance and improve the eas e of creating ready-to-use te xt and graphic files. Two improvements not commonly seen are time-axes that are automatically generated from the data with an output below the time axis of the number of pati ents at risk, the number of events, and the number remaining. This paper describes one method to address these challenges. Key Words Survival Graph, SAS Graphs, Axis Scale, Automation, Number at Risk, Number of Events, Number Remaining	proc step, log-rank test, rank test, kaplan-meier plot	7
65	In for the Long Haul: Retaining a Talented SAS Programming Team	Lynn M. DiFinizio, John Hotaling	SAS programmers have a variety of skills, preferences, and communication styles, but there is a universal truth across the demographic - we enjoy programming so much that we stick with it for years, always learning and sharing new and better ways of using SAS. A team of talented programmers working well together is an extremely valuable asset, one which needs to be developed and protected over time. In this paper, we provide tips for managers/project leaders to build a strong a productive SAS programming team, retain that programming talent, and develop the team over time. Additionally, we suggest ways to protect your team's ability to continue to work well in the event that one of your key team members wins the lottery and decides to retire from the exciting life of SAS programming	sas programming, sas programmer	6
66	In-Depth Review of Validation Tools to Check Compliance of CDISC SDTM-Ready Clinical Datasets	Bhavin Busa, Kimberly Lindfield		data cleaning, macro definition, sdtm domain, clinical data, controlled terminology, sas base, clinical trial, cdisc	52
67	In-Depth Review of Validation Tools to Check Compliance of CDISC SDTM-Ready Clinical Datasets	Bhavin Busa, Kimberly Lindfield	Pharmaceutical organizations are proactively adopting Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) because of a broad variety of benefits to the industry and the FDA's intention to mandate submissions of SDTM within the Electronic Common Technical Document (eCTD) structure. Tools are needed to check compliance and streamline operations for the preparation of data for FDA submission. In June 2008, we presented a paper at PharmaSUG titled - 'Validating CDISC SDTM-Compliant Submission-Ready Clinical Datasets with an In-House SAS Macro-Based Solution'. The paper described two options (SAS - `PROC` CDISC and Lincoln Technologies WebSDM) available at that time along with an in-house SAS macro-based solution for validating the compliance of submission-ready clinical datasets. We concluded in our paper that the in- house SAS macro-based solution was more cost effective and provided greater flexibility than the other two options	data cleaning, sdtm domain, proc step, macro definition, staging area, cdisc	8
68	Insert Diagnostic Code Using Pipes and PRX (Regular Expression) Functions	Brian P. Fairfield-Carter			6
69	Insights into ADaM	Matthew J. Becker	ADaM (Analysis Dataset Model) is meant to describe the data attributes such as structure, content, and metadata that are typically found in clinical trial analysis datasets. The ADaM models are built from the CDISC SDTM baseline. In this workshop, we will look into the latest DRAFT ADaM Implementation Guide (v1.0) that is out for comment. The goal is to provide the attendees knowledge of the ADaM model, how it relates to the CDISC SDTM (study data tabulation model) base, and how it may help in reducing FDA review time. Depending on the timing of release of the final ADaM 1.0 implementation guide, the workshop will incorporate that guide	adam dataset, bds, clinical trial, proc contents, sdtm domain, adamig, label statement, data science, excel, raw data, cdisc	9
70	Introduction to Survival Analysis and Creating Kaplan-Meier Curves	Ojesh Upadhyay	A non-technical approach will be employed to introduce SAS programmers to the basic statistical concepts underlying survival analysis and to discuss common issues encountered when creating Kaplan-Meier curves. Programmers will become familiar with how the Kaplan-Meier estimates are derived at each timepoint, how to use the ANNOTATE option to build customized graphs and the OVERLAY option in `PROC GPLOT` to mark only censored subjects and not subjects with events	survival analysis, proc step	10
71	Keep it Clean	Angelina Cecilia Mauldin	An advantage of using Interactive PC SAS consists on using data sets, formats, macro variables, macros that have been created previously in the same session. This advantage, can be dangerous if don't pay attention and use one programs data, formats, macros that were intended for another program. Keeping unnecessary data can expensive because of the wasted storage space and can be dangerous if we use data/formats/macros that were created for one program in another. The purpose of this paper is to show different ways to 'clean' temporary data, formats, macro variables, macros	macro variable, proc step, proc sql, proc datasets, variable list, work library	4
72	Leave No Trace: A Set of Macros to Remove Evidence of Program Execution	James R. Johnson	The number 1 rule of camping is "leave no trace". When you leave a campsite, there should be no evidence remaining that you were ever there. As the author of an application or utility, you need to be sensitive to the SAS environment in which you are operating. Your application should leave nothing behind, other than expected results, that shows it was ever there. Your application may need to change certain environmental characteristics, generate some datasets, perhaps some formats, probably some macro variables, and maybe open some libnames or filerefs. Items such as these left behind by a program can adversely affect the SAS environment in which it was called. Things like datasets, macros, formats, and macro variables left behind can clutter and confuse the environment. Changed SAS options can cause significant problems to subsequently running steps	macro variable, dictionary tables, proc sql, proc step	10
73	Lessons Learned as a SAS Programming Lead Across Cultures	Eunice Ndungu	Business practices that are the norm in one country may not be normal business practices when dealing with international partners. This paper will compare and contrast experiences with two global groups and share lessons learned on how to effectively communicate, motivate, give feedback, and resolve conflicts. The goal of this paper is to enhance your effectiveness in working with global partners	csr, data science	5
74	Let your title macro report study progress	Yang Chen	It is tedious for the lead programmer to check the table/figure/listing(TFL) production status, especi ally when the clinical study involves a large number of TFLs. Thi s paper introduces a tool that uses title dataset t o report the TFL progress automatically. Whenever the programmer calls the title macro to ad d titles/footnotes, the title macro also updates th e production status of the TFL in a tracking sheet, with detaile d information such as table number, corresponding c reator name, creation date/time, validator name and validation d ate/time. This tool is useful to study management in that it provides accurate and timely progress of the study. The lead can benefit by easily finding out which TFL is missing, not validated or obsolete	data cleaning, dataset	5
75	Linear Mixed Models in Clinical Trials using PROC MIXED	Danyang Bing, Xiaomin He	This paper mainly illustrates how to use `PROC MIXED` to fit linear mixed models in clinical trials. We first introduce the statistical background of linear mixed models. We explore the situations under which the mixed effects are identified. A randomized complete block design is used to explain the difference between `PROC GLM` and `PROC MIXED` in dealing with the linear mixed models. Examples include applicat ions of `PROC MIXED` in four commonly seen clinical trials utilizing split- plot designs, cross-over designs, repeated measures analysis and multilevel hierarchical models	proc step, proc mixed, fixed effects, clinical trial, proc glm, treatment effect, covariance, proc genmod, repeated measures, estimate, crossover study, correlation, sample, regression analysis, maximum likelihood estimation, random intercept	12
76	Logging the Log Magic: Pulling the Rabbit out of the Hat	Adel Fahmy	Program Validation includes checking both program Log and Logic. Program Log should be free of any Error/Warning/Note messages, which is the focus of this paper. Program Logic is revised manually or by writing an independent rough code that confirms its results. During the development phase, programmers use SAS system options (MPRINT, MTRACE, MLOGIC, MACROGEN, SYMBOLGEN), and `PROC SQL` option (FEEDBACK), which result in lengthy log files. Although moving the final program into production should involve switching off all of those options, this is not the case all the time. Trying to visually scan thousands of pages of a log file is a tedious work that involves searching individual strings (Error, Warning, etc.) one at a time. All possible messages haven't necessarily been addressed, particularly those that do not start with the Error or Warning word	proc sql, proc step, sas log, stored macro, data cleaning, error message, input statement, options statement, sample, macro definition	12
77	Looking for a Function Called LEAD? Couldn't Find? Here You Go - Multiple Ways to Simulate It	Niraj J. Pandya	There are numerous amounts of functions available in SAS which makes data imputations, data comparison and data manipulation easier for a programmer. Though there are some tasks for which SAS does not have functions readily available for use. One such task is to read next record while working on the current record and at the first thought one can imagine an exact opposite processing of data to LAG function, which can be named as LEAD function. Unfortunately SAS does not offer any such function. But fortunately there are some tricks and techniques which can simulate opposite processing of LAG function and can read next couple of records while still processing current record in the dataset. By little addition of extra piece of code, some of these techniques can also handle cases where processing has to be done by grouping variables	by group processing, class variable, lag function, proc sql, proc step	5
78	Looking for columns with all missing values	Binoy Varghese	The objective of this paper is to pres ent a generic macro that w ill scan a data library to identify columns with all missing values, drop them from asso ciated datasets, out put lean datasets and generate a report for review. The metadata required to automate this processing is obtained using `PROC DATASETS` and Output Delivery System	missing value, proc step, proc sql, libname statement, data step, adam dataset	5
79	Macros Are Helpful - Tips to Avoid Giving Them a Bad Rep	Deb Cassidy	Some people love to use macros while other people would love to see them eliminated from SAS altogether and replaced by the use of SCL or CALL EXECUTE. Part of the negativity about macros stems from poorly written macros or using them when they aren't really beneficial. This paper will cover some examples of "bad" macros, why they would be considered "bad" and how to improve them. This paper is focused on using macros within individual rograms. It does not cover writing a system of macros. p	proc step, macro call, by group processing, macro loop, macro variable, macro parameter, %macro parameters, %if-%then-%else, macro definition, where statement	7
80	MAED Service: A SAS Tool for Implementing SMQs and Performing MedDRA-Based Analyses of AE Data	P. Chris Holland	The MAED Service is an award-winning SAS-based tool that was developed for FDA medical and statistical reviewers of adverse event (AE) data coded to the Medical Dictionary of Regulatory Activities (MedDRA). MAED (pronounced like "maid") stands for MedDRA-based Adverse Event Diagnostics. The MAED Service allows reviewers to look at AE data by each level of the MedDRA hierarchy and both narrow- and broad-scope standardized MedDRA queries (SMQs). All SMQs, including those with algorithms, are derived using each patient's AE data. For each level of the MedDRA hierarchy and each SMQ class (narrow- or broad-scope), terms are sorted and displayed in a summary report by the degree of disparity between comparison groups (e.g. test drug vs. placebo). So the first term displayed in the MAED Service report represents the term, for the given level, with the largest degree of disparity between the groups being compared	adverse events, if-then-else, adverse event summary, macro parameter, proc step, macro definition	9
81	Make Easy Style Templates Using SAS Macro	Barbara Harlan	Do time consuming style templates keep you from adding visual interest to your reports? Do you find yourself constantly looking up frame options or for that pesky SAS -defined color list? Why not create a macro that will easily generate a style template just by selecting options in a few simple steps? Even better, have the macro output lists with all the possible fonts, colors, frames, rules, and so on. This paper presents a SAS macro that generates custom style templates with user-specified settings in the macro call statement. In addition, the macro lists all style options and color lists for easy reference when creating your individualized template. Techniques on using your custom style template to create RTF and HTML output will also be discussed. This macro was created utilizing `SAS/MACRO` and `SAS/ODS` with version 9.1.3	macro call, keyword parameter, %macro parameters, sas output, proc template	11
82	Make Your SAS Code Environmentally Aware	Clarke Thacher	SAS global macro variables and operating system environment variables contain a wealth of information that can be used to add extra power to your SAS programs. This information can be used to add additional diagnostic information to your logs and add customization to reports. We will demonstrate a simple program to access all current operating system variables. We will describe some of the most common variables and suggest how they might be used. As an added bonus, we will show how to define SAS LIBNAMEs without writing a single line of SAS code	macro variable, error message	7
83	Making an RTF file Out of a Text File, With SAS	David W. Franklin	There are many times when we want to create an RTF file out of a text file. Many solutions involve running a Microsoft Office VBA macro, and there are some solutions that read in the text file and use SAS `ODS RTF` to create the RTF format file. This paper brings yet another solution (with code) to the fore where the text file is read in and the relevant RTF codes added, the advantage being that the solution is often quicker than other methods	txt, sas consultant	3
84	Managing and Sharing Information through the Use of a Wiki	Jim Worley	In the current global workforce, it can be difficult for a department to efficiently manage the sharing of information. Often times when a department spans multiple sites, information may be shared in one site but not others. There may be a collection of trainings saved at each office location of global organizations that are not easily searchable filling up a shared drive, or worse sitting on staff desktops. Staff might have great time-saving ideas but may not have a good venue to share these ideas with their colleagues. A wiki can be used to solve all of the aforementioned issues, helping to facilitate the sharing of information in a global environment. A wiki is a website that allows contributions from any user. It is easy to use, making the input of new information by other users fast and convenient. The search function allows users to quickly find what they are looking for. Due to the many open-source packages available, it is inexpensive to set up		4
85	Managing Clinical Data Standards: An Introduction to SAS Clinical Data Integration	Michael Kilhullen	As the adoption of industry data standards grows, organizations must consider how to effectively implement and manage data standards across a large- often global- user base. SAS Clinical Data Integration is built upon a centralized metadata repository that is ideal for centrally deploying and managing data standards. Built on SAS data integration technology, it provides a visual and metadata-driven environment that facilitates the conversion of data to standard formats while collecting more detailed information about the decisions you make during this process. SAS Clinical Data Integration provides specialized interfaces that further leverage the metadata to help you more effectively work with and manage clinical standards	data standard, data cleaning, clinical data	10
86	Manual to Automatic: Changing Your Program's Transmission	Arthur L. Carpenter	You have successfully created a SAS program that processes data and generates reports for a series items in the data. The program itself works fine, but the problem is that each time the program is executed, it must be manually modified specifically for that run. Perhaps the analysis variable changes or perhaps the name of the report's PDF file must reflect the name of the incoming data set. Making manual changes once in awhile is bothersome, but not onerous. But if find yourself making a great many of these manual changes, the process becomes both tedious and error prone. Through the use of the macro language, SAS has the power make the necessary changes on the fly, automatically, without manual intervention. Learning how to convert a program from one that needs manual modifications to one that adjusts and runs automatically is not difficult. Not difficult that is, if you know the steps	macro variable, macro loop, proc sql, proc step, do loop, variable list, macro facility, proc report, macro put, symbol table, macro definition	11
87	Medical Device and Diagnostic Industry 101	Carey G. Smoak	Medical devices and diagnostics are increasingly important industry to the healthcare industry. The number of devices approved by the FDA has increased by 52% in the past decade. While devices are important in and of themselves, they are also increasingly more and more important in pharmaceutical research including diagnostic imaging used to monitor therapies, devices used to deliver drugs and diagnostic tests to determine if a patient will respond to a particular therapy. Devices are different from pharmaceutical products in terms of the FDA approval process, the use of CDISC standards and types of studies	clinical trial, sdtm domain, cdisc	5
88	Methodologies to Create Multiple Symbols on a Single Line Plot	Sanjiv Ramalingam	The `GPLOT` procedure is widely used to create line plots. Situations may arise wherein multiple symbols are required within a single line plot to illustrate differences within the data. A typical example is to illustrate percent survival (or failu re) as a function of time . Current options in SAS limit the use of symbols to one symbol per line plo t. A possible solution is to create multiple lines per symbol and then overlay each plot. This solution is however data dependant and requires that there are no time points common to each of the individual plots. Two different approaches are presented. The first methodology uses the annotate feature and the other uses the `GPLOT` procedure	line chart, proc step, proc gplot	5
89	Missing Pages Report	David Gray, Zhuo Chen	In a clinical study it is important for data management teams to receive CRF pages from investigative sites in accordance with an agreed upon schedule so that the data may be entered and cleaned in a timely manner. It is therefore important to have a process in place that can project when CRF pages for a patient will be due and that can identify overdue expected CRF pages. This paper describes a macro developed in PC SAS 9.2 to generate a missing pages report, a page due report, and a summary report. Data management teams can utilize these reports to trace missing pages, predict data entry volume, and review the status of each patient. The reports are generated in `EXCEL` with one .xls file per investigator site and one for all sites combined. Each `EXCEL` file contains separate tabs for each report	excel, create table, data governance, group by, macro variable, proc step	10
90	Multidimensional Data Visualization for Generating Patient Profile	Rajavel Ganesan, Suhas R. Sanjee, Jason Clark	Traditionally, patient profile information is displayed in multiple tables and graphs in a disconnected fashion. A methodology is presented here to enable multidimensional data visualization of a patient's profile. Data from multiple SDTM domains can be presented graphically without compromi sing any details by adding text annotations wherever needed. These profiles can be very usef ul in evaluating a patient's safety a nd disposition. It can also help with medical monitoring and serve as reference for medical writing. Medical Monitoring is an on-going consolidated review conducted by clinical in life by reviewing the safety, efficacy, and other relevant data in an effort to assess for specific trends, outliers, and to ensure protocol compliance. We hav e effectively used this approach in oncology therapeutic area	study day, proc gplot, ctcae grade, cdisc	4
91	Need reporting deliverables the painless and easy way? A batch submit macro call, of course!	Jim Cawley, Jillian Prescod	Submitting numerous SAS reporting programs from PC SAS does not have to be a cumbersome task when utilizing a batch mode macro. Whether time and/or system resource constraints are an issue, a properly crafted SAS batch macro is an invaluable tool to generate and send reporting deliverables quickly and easily; and in doing so without a drain on resources. This paper will guide you through submitting an unlimited number of programs in a macro call and emailing the final deliverables to the intended customer(s). This can be accomplished through two methods that will ensure efficiency in managing time and system resources	in=, macro call, sample	8
92	Non Printable & Special Characters: Problems and how to overcome them	Sridhar R. Dodlapati, Praveen Lakkaraju, Naresh Tulluru, Zemin Zeng	Non printable & special characters in clinical trial data create potential problems in producing quality deliverables. There could be major issues such as incorrect statistics / counts in the deliverables, or minor ones such as incorrect line breaks, page brakes or appearance of strange symbols in the reports. Identifying and deleting these issues could pose challenges. When faced with this issue in Pharmaceutical & Biotech industry, it is imperative to clean them up. We need to understand the underlying cause and use various techniques to identify and handle them	compress function, clinical data, clinical trial, data governance, character variable, options statement, macro definition	13
93	Online Query Management System: An Innovative SAS/IntrNet Solution for Clinical Data Management	Romain Miralles	Electronic Data Capture (EDC) solutions for clinica l trials provide efficient and sophisticated capabi lity to easily validate and clean data using a built-in query-mana gement system. However, only half of all trials are run using EDC because of its inherent implementation complexity a nd high cost. Many organizations are still performing paper-based trials. As in any other trials, the data from pape r-based studies have to be validated and cleaned. To clean the data base, data managers create discrepancies, which are sent to the investigator sites. Tracking and managing these dis crepancies for large studies can be a daunting task without the right tool. This paper will introduce a SAS-based innovative q uery-management solution called the Online Query Management System (OQMS).This tool has been used by one of the top five medical device companies to so lve 8,000 queries in less than a month	data validation rules, dataset, audit trail, clinical trial, edc, excel, macro definition, unique key, sas programmer, data cleaning	12
94	Operational Uses of Patient Profiles ... Even in an eCTD and SDTM World	Terek J. Peterson	For years, patient profiles were instrumental in conveying information and data about a single patient in a concise output within a company's submission to FDA. In the clinical trial world of eCTD and SDTM, the need for these outputs has decreased because when data submitted is SDTM compliant the FDA has a powerful graphical patient profile tool to review the data. What is now a benefit of FDA receiving SDTM domains leaves other stakeholders within the sponsor company creating the submission without outputs that many groups find essential to do their work. Through experience, there is still a need for the creation of simple patient profiles to meet operational objectives when conducting clinical trials, creating the submission documents, and making patient safety decisions. The techniques the authors of this paper are proposing are for operational uses of patient profiles as in narrative writing, Data Safety Monitoring Board (DSMB), and internal data review meetings	proc step, proc report, proc print, macro variable, clinical trial, macro definition, cdisc	8
95	Optimally Scheduling Resource Constraint Project Using SAS/OR	Jeff Cai	This paper shares with SAS users an approach to effectively distribute programming resources in a clinical study project or among several projects by using procedures and macros in `SAS/OR`. Considering some activities in a project may have precedence and time constraints, and each programmer may also have different time constraints, such as different start date and quit date on a project, personal vacation, etc., one effective method is to use constraint programming `PROCEDURE CLP` to optimally schedul e these activities for project management. By incorporating a specific calendar, user can estimate the project target date w ith the constraint resource, or forecast the required programming resource with the constraint target date. Plot procedures NETDRAW and GANTT for visualizing activity data are described in this paper as well. Keywords: `SAS/OR`, CLP, NETDRAW, GANTT, CSP, Project Management	proc step, constraint programming, clinical programming	10
96	Order, order please: sorting data using PROC REPORT	Lisa Fine	`PROC REPORT` is one of the more useful tools available in SAS for generating formatted reports. One of the key expectations for customers of our reports is providing output in a sorted order. Accomplishing that sorted order is not always obvious or easy. This paper provides clarification and demonstrates various sorting options and techniques for achieving a desired sort order when using the `REPORT` procedure. The paper presents the difference between 'ORDER' and 'ORDER=' options and details how the two options should be used together. The available types of sorts, how to sort multiple columns when display order differs from sort order, and techniques for creating a sort variable, one which incorporates the use of an 'alias' are also discussed	proc report, proc step, sample, proc format	9
97	Physician Practice Pattern Variation: Using Data Mining and Predictive Modeling to Identify and Control Costly Treatment Patterns	David C. Ogden	The Challenge: A major provider of health insurance wanted to identify which physicians (in their network) were providing excessively costly services and treatments for a given health condition (upper respiratory infections) and develop a business process to influence practice patterns in such a way as to control costs. The Solution: Using an innovative fusion of analysis-of-variance and predictive modeling, "expected costs" were calculated for each physician, based on member health, condition(s) being treated, and physician demographics. This allowed for an automated process to compare actual costs to expected costs, and to "red flag" physicians. The Outcome: $10 million in annual cost-control opportunity identified for a single condition (upper respiratory infections). Some Details: The big breakthrough in this area was to devise an approach to model the expected costs associated with treating a given condition (upper respiratory infections)	sample, outlier, regression analysis, insurance claims	12
98	Practically Perfect Presentations	Cynthia L. Zender	`PROC REPORT` is a powerful reporting procedure, whose output can be "practically perfect" when you add `ODS` STYLE= overrides to your `PROC REPORT` code. This hands-on workshop will feature several `PROC REPORT` programs that produce default output for `ODS HTML`, RTF and PDF destinations. Workshop attendees will learn how to modify the defaults to change elements of `PROC REPORT` output, such as HEADER cells, DATA cells, SUMMARY cells and LINE output using `ODS` STYLE= overrides. In addition, attendees will learn how to apply conditional formatting at the column or cell level and at the row level using `PROC FORMAT` techniques and CALL DEFINE techniques. Other topics include: table attributes that control interior table lines and table borders, use of logos in output and producing "Page x of y" page numbering. The rest of this document is divided into two parts: the slides used in the Hands-On Workshop presentation and the attendee worksheet	proc report, proc step	40
99	Pressures on SAS Programming Roles and their Evolution in the Competitive Global Environment	Vijay Moolaveesala, Mark Matthews	Pharmaceutical and clinical research organizations (CRO) are facing tough economic conditions and pressures on their ability to reduce the costs to conduct and report clinical data. This industry is exploring ways to reduce the layers of operational groups to streamline the development process as well as reduce the time and cost to bring out a viable compound. These efforts need introspection of operation group's structure and resource allocation. When faced with similar dilemma during IT industry bubble, software industry reacted quickly to reduce their costs by concentrating on their core competency and outsourcing the standard coding & maintenance work to low cost regions in North America as well as in emerging markets. This opened up doors for other industries also including the Pharmaceutical industry. Most organizations feel that clinical programming roles can be effectively outsourced without compromising the quality of the output	sas programming, sas programmer, pharmaceutical industry, service provider	3
100	PROC CPM for Project Management 'Activity-On-Arc (AOA) or Activity-On-Edge (AOE)' Method	Venkateshwaran Lajapathirajan	A project management tool helps manage time and resource for the duration of a project. This paper explains how to use `PROC CPM` to manage studies	proc step, adam dataset	8
101	PROC REPORT - Compute Block and Conditional Footnote	Yumi Sembongi	`PROC REPORT` is one of the most effective and flexible reporting procedures in SAS language. It is a `STANDARD` procedure to derive tables/listings in the pharmaceutical industry. A unique feature which allows customizing the report dynamically is COMPUTE block. In this paper, four types of conditional footnotes are presented. Conditional footnote is defined as a footnote which appears or disappears in a page or report with a given condition, or a footnote that can vary per page. We use the unique position of COMPUTE AFTER _PAGE_, which can substitute for a footnote. We also utilize the summarizing/calculating ability of COMPUTE BEFORE\|AFTER and COMPUTE BEFORE\|AFTER <variable>, and a temporary variable which retains a value across observations. In our examples, we try to minimize pre-processing prior to `PROC REPORT` in order to utilize the flexibility of `PROC REPORT`. We generate the listings with conditional footnotes using SASHELP.CLASS dataset or the modified dataset in this paper	out=, proc report, proc step	20
102	PROC TABULATE: Getting Started and Doing More	Arthur L. Carpenter	Although `PROC TABULATE` has been a part of Base SAS since early version 6, this powerful analytical and reporting procedure is very under utilized. TABULATE is different; it's step statement structure is unlike any other procedure. Because the programmer who wishes to learn the procedure must essentially learn a new programming language, one with radically different statement structure than elsewhere within SAS, many do not make the effort. The basic statements will be introduced, and more importantly the introduction will provide a strategy for learning the statement structure. The statement structure relies on building blocks that can be identified and learned individually and in concert with others. Learn how these building blocks form the structure of the statement, how they fit together, and how they are used to design and create the final report will be presented	proc step, proc tabulate, missing value, proc format, format statement	18
103	PROC TABULATE: Getting Started and Doing More	Arthur L. Carpenter	Although `PROC TABULATE` has been a part of Base SAS since early version 6, this powerful analytical and reporting procedure is very under utilized. TABULATE is different; it's step statement structure is unlike any other procedure. Because the programmer who wishes to learn the procedure must essentially learn a new programming language, one with radically different statement structure than elsewhere within SAS, many do not make the effort. The basic statements will be introduced, and more importantly the introduction will provide a strategy for learning the statement structure. The statement structure relies on building blocks that can be identified and learned individually and in concert with others. Learn how these building blocks form the structure of the statement, how they fit together, and how they are used to design and create the final report will be presented	proc step, proc tabulate, missing value, proc format, format statement	18
104	Programming Validation: Perspectives and Strategies	Parag Shiralkar	There have been several attempts to define and collect programming error rate and related quality metrics in order to improve the programming process efficiency. One of the important steps in programming process cycle is quality check and validation of programming deliverables. Although quality check or QC and validation are interrelated, they serve distinct purpose and can have different interpretations based on nature of programming work. Validation and QC process is critical to the success of programming work. Many times this important process gets influenced and impacted by pressing clinical study timelines, established processes, and quality assurance requirements of sponsor. In certain cases, such influences often conflict with each other and can deviate the process from its true purpose of ensuring error free deliverables	data cleaning, quality assurance, data wrangling, independent qc	4
105	Quality in a World of "Standards" with Differences!	Sandra VanPelt Nguyen	Even with the use of data standards such as CDISC, obtaining quality data and analyses can still be quite challenging, particularly when there is great variance in the interpretation and usage of standards. How many varieties of SDTM have you seen? Have you heard the descriptions "SDTM-like", "SDTM-friendly", "SDTM-plus", or "SDTM-minus"? How many varieties of data specifications have you seen? Does anyone use controlled terminology? How many different file formats have you received from vendors? Do you ever struggle with how to map data to SDTM? So do others - and they may end up with different results! This presentation will take a look at how standards are implemented, where there are opportunities for improving quality when using standards, and the challenges that still exist with the use of standards, with focus on the CDISC SDTM standard. Additionally, it will cover how process and tools can be used to enhance the use of standards and assist towards improving overall quality	data cleaning, data standard, sdtm domain, raw data, clinical trial, controlled terminology, data validation rules, sdtm programming, crf, database lock, cdisc	9
106	Report Management Using the ODS Document Destination and Report Metadata	Brit Harvey	Analysis projects may require writing many reports in several formats, such as PDF and RTF. Report management can become a substantial task when the number of reports is large. This paper describes a report management system developed to support clinical trials analysis in a small organization with limited resources. The system uses the `ODS` DOCUMENT destination, `PROC DOCUMENT`, and report metadata to decouple report content generation and rendering reports. The system supports fully programmatic combining and bookmarking of PDF output files, resulting in increased efficiency and quality. The principles are applicable to any analysis requiring large report sets and to other report output formats	proc step, sample, proc report, output delivery system, ods pdf, ods rtf	6
107	RTF_READ: Decoding RTF Files	Sandeep Juneja	RTF has quickly become the standard medium for the production of tables and listings. RTF, while very useful to medical writers and other down stream processes, has hindered SAS programmers in that automating QC of tables and listings seemed to become unattainable. Previously, w hen outputs were produced in ASCII format (.lst) the outputs could simply be read in to create a SAS dataset whic h could then be compared to the output dataset of the QC program. This paper presents a detailed proce ss of decoding RTF files using SAS and generating SAS dataset(s), thus enabling us to automate QC. In this pape r we explain how to identify important tokens in RTF files to extract data, convert special symbols to SAS tokens, handle complex column head er spanning and distinguish between the Title and Footnote data from the normal docum ent data	dataset, out=, data cleaning	6
108	SAS Programmer's check list-Quick checks to be done before the statistical reports go off the SAS Programmer's table	Thomas T. Joseph	"The statistical output looks absurd" is not the coo lest thing to hear. If the data is clean and if the output looks absurd, then it is a programming issue because `SAS/STAT` procedures can do no wrong. Our experiences as programmers have encouraged us to write down this check list while doing statistical programming. The essence of this paper is to do primary checks to avoid programming errors while statistical reporting in clinical trials before QC and review by the statistical team. The few procedures and checks which we discuss below do not require a very sound mathematical or statistical knowledge, but instead can be done on your finger tips or using a calculator. The later part of the paper discusses the pitfalls that could occur during pooling of data for safety and efficacy reporting	least squares mean, confidence interval, odds ratio, ls mean difference, proc step, degrees of freedom, proc glm, proc genmod, sample, pharmaceutical industry, treatment arm	7
109	SAS Programming Techniques for Manipulating Metadata on the Database Level	Chris Speck	One great leap that beginning and intermediate SAS programmers must take in order to achieve a more expert level of programming is to think in terms of libraries, or entire databases, rather than only datasets. This has recently become much more crucial now that programmers are under greater pressure to apply regulatory standards to clinical research data. In the United States, the FDA more and more requires that data meet the strict standards set by the Clinical Data Interchange Standards Consortium (CDISC). This has caused many SAS programmers to rethink their purposes and practices while preparing study data for FDA submission. It forces them to deal with metadata, not just on the dataset level, but on the level of entire libraries as well. This paper will provide methods by which SAS programmers working in Base SAS can quickly manipulate the data and metadata of entire libraries	macro variable, data step, proc sql, %if-%then-%else, proc step, sas programmer, dictionary tables, macro quoting function, macro loop, macro facility, cdisc	7
110	SAS to Excel with ExcelXP Tagset	Mahipal R. Vanam, Kiran Karidi, Sridhar R. Dodlapati	Creating XML based `EXCEL` files is a very convenient and powerful feature of SAS 9 or later version. Th is paper provides a brief overview of the `ODS` `TAGSETS`.ExcelX P destination to generate XML output that conform t o the Microsoft XML Spreadsheet Specifications and can be opened with Microsoft `EXCEL` 2002 (also known as Ex cel XP) or later version. And also, by using ExcelXP tagset s we can create multi-sheet Microsoft `EXCEL` workboo ks very quickly and easily	excel, xls, ods xml, proc template	7
111	SAS/Graph Annotate Coordinate Systems by Example	Max Cherny	The Annotate facility is a very powerful tool within `SAS/GRAPH`. It is used to create or enhance SAS graphics by placing graphical elements such as lines or symbol s anywhere on a screen. However with this power comes a certain degree of complexity which makes learning the Annotate facility a difficult task. One of the most challenging aspects of the Annotate facility is its use of graphical coordinates. There are 12 different systems which control the placement of a coordinate. A SAS user needs to understand how each system identifies the exact location of a coordinate in order to judiciously use the facility. Some of these systems are complicated, for example the ones which use relative position of a coordinate. This paper explains the Annotate coordinate systems and provides an easy and reusable example for each of them. These examples can function either as the stand-alone programs or as the starting point for further experimentation with the Annotate coordinate systems	bar chart, proc step	18
112	sasCommunity.org - Your SAS Site: What it is and How to Get Started	Arthur L. Carpenter	From around the world, the community of SAS users, programmers, developers, as well as others interested in the broader application of SAS software, are increasingly making use of the sasCommunity.org wiki site. Fast becoming the clearing house for all information that is related to SAS, sasCommunity.org is operated and run by SAS users. It is free, and it is open to contributions from everyone. Learn how you can make use of this site. Find out how you can contribute. Discover how you too can quickly make a difference in the world wide community of SAS users. Join the thousands of other SAS users that are a part of the creation of a site that is greater than the sum of its parts	excel, proc sql	12
113	SDD Unleashed - Extending the Capabilities of SAS Drug Development	Stephen Baker	SAS Drug Development is well known as a compliant, hosted repository for storing SAS data and running programs in a secure and audited environment. What is not well known is that SAS Drug Development provides an Application Programming Interface (API) for extending this environment to be more relevant to the way people do business. Where are the features for automating the upload of transactional data, or for defining a new study? Well, while those features do not exist out of the box with SAS Drug Development they can be obtained through a variety of methods to extend the capabilities of SAS Drug Development. By taking advantage of the remote API and understanding the inner workings of the SAS Drug Development product, custom plug-ins can be developed to improve the business process. Workflow can be introduced, automating manual, time intensive tasks in a way that translates to more time spent on valuable science rather than administering a tool	clinical research, macro definition, web service	10
114	SDTM Attribute Checking Tool	Ellen Xiao	Converting clinical data into CDISC SDTM format is a high priority of many pharmaceutical/biotech companies. Most of these companies are ready to invest in obtaining a SDTM conversion t ool that can do the job specifically designed for their company or to hire a CRO to convert the data for them. CDISC STDM conversion may sound like an easy job given the various available t ools. What is important not to overlook is the va lidation of the resulting CDISC SDTM data. It can be quite challenging and time consuming validate SDTM submissions manually. Three targets or perspectives of validation can be categorized as structur e, content and attributes. Here, I'd like to focus specifically on a SDTM Attribute Checking Tool used to check between SDTM data and the define.xml	macro parameter, macro variable, excel, data cleaning, cdisc	6
115	Searching for a Diamond in the Rough: Finding and Integrating SAS Programming Talent	John Hotaling, Lynn M. DiFinizio	Although the current economy has seen a lot of companies cutting back on hiring programmers, there is still a high demand for talented SAS programmers. It can be a challenge to find highly qualified candidates and then determine which would be the best fit for your team. This paper will detail some of the things a manager looking to hire SAS (programmers should be keeping in mind	sample, pharmaceutical industry	5
116	Smart Programming and SAE Reconciliation	Haibin Shu, Ronen Mansuri, Maggie Ci Jiang	Reconciling a large number of SAEs between Safety (Pha rmacovigilance) database and Clinical database within a short period of time can be a daunti ng task because of the following reas ons: 1) Changing source information frequently in either Safety or Clinical database or both; 2) Manual process and potential for human errors; 3) Repetitive lots of reconciliations. Smart programming processes and routines were designed to address these three major difficulties in SAE reconciliations. Our approach has the following unique characte ristics: 1) Reconciliation only applies to necessary cases; 2) Double check manual and human processes for accuracy; 3) Integrate with manual review process. As a result of implementing this smart programming process, several hundred SAEs were reconciled within a couple of weeks	clinical database, raw data	3
117	So, You've Got Data Enterprise Wide (SAS, ACCESS, EXCEL, MySQL, Oracle, and Others); Well, Let SAS Enterprise Guide Software Point-n-Click Your Way to Using It	William E. Benjamin Jr.	This paper will walk a SAS user through the process of opening and using files from different (non-SAS) generated sources and processing the data with SAS Enterprise Guide (EG). The focus of this paper will be to get access to the data, since most functions available to EG are drop down menus and wizards which help teach a new user how to process data. For completeness a simple chart will be shown with instructions about how to access each type of data shown. This paper will not show how to get the data into non-SAS files; it will be assumed that the user's enterprise will guide them through that process	sas enterprise guide, excel	10
118	Sort Your Problems: Using SORTEDBY to Compare Datasets	Sanket Kale	Analysis datasets have become a permanent fixture in study submissions. The complexities of these datasets need higher validation levels. In an increasingly competitive drug innovation market with global outreach and added scrutiny from the regulatory agencies, most companies rely on validation method of independent recreation of datasets. Comparison of th is set of independently c reated datasets with each other, on the correct ID variables, is an important part of the validation effort. This paper presents a simple and efficient technique to compare entire dataset libraries, created by original and independent programmer, with each other, using the sort key information contained in the SORTEDBY variable from the metadata. This technique utilizes the functionality of Dictionary Tables to access all necessary information stored in metadata for comparin g each dataset with its counterpa rt	adam dataset, dictionary tables, macro variable, data cleaning, macro call, macro definition, proc step	5
119	Surviving Survival Analysis: Basic Knowledge from a Programmer's Viewpoint	Deb Cassidy	While many statistical analyses are concerned with what happened at a point in time, survival analysis looks at the span of time until an event occurs. In the pharmaceutical industry, you might be looking at how long before a person recovered, died, got a disease or had a recurrence. Although some programmers do have some statistical training, it usually doesn't include survival analysis. `SAS/STAT` includes three procs for survival analysis. This presentation will give you some basic information on using `PROC LIFETEST` for survival analysis to enable you to ask reasonably intelligent questions of your statistician and to reduce the need to ask some questions. The presentation will be given as a walk-through of the code and sample tables that you might be reporting. This presentation is based on my experiences as a programmer and will not be an explanation of the statistical calculations behind the `PROC`	survival analysis, proc step, time variable, confidence interval, sample, predictor variable	9
120	Thanks CDISC - Free Utilities	Robert Agostinelli	With the emergence of CDISC data standards (thanks CDISC! ), there are opportunities to produce tools that check for the conformance to the standards, and more importantly , tools for data presentation/r eporting. These tools could be developed in an open way, such that the entire industry can create and take advantage of work done by individuals or groups. Regarding tools to check for data conformance to the standards, OpenCDISC (www.opencdisc.org) has a free tool that will check for conformance to the SDTM standard. This paper will focus on data presentation/reporting	subject visits, data standard, histogram, cdisc	5
121	The 5 Biggest Challenges of ADaM	Terek J. Peterson	Electronic submission of clinical trial documents and data continues to be a challenge for many companies. Legacy systems and processes, which work well and are efficient, have to be retooled to meet the needs of FDA and ICH requirements. While the implementation of the eCTD is maturing and the implementation of CDISC SDTM is now beginning to mature, the CDISC ADaM standard is still in the adoption stage for many companies. With many pharmaceutical and biotechnology companies making the business decision to wait until ADaM is more widely adopted or have analysis data in company specific standardized legacy formats, service providers and CROs are fully embracing the CDISC ADaM standard as the de facto analysis data standard in lieu of another analysis data standard and because of the seamless use of SDTM as the data source	adam dataset, sdtm domain, raw data, metadata management, service provider, clinical trial, cdisc	11
122	The CDISC/FDA Integrated Data Pilot: A Final Summary of Findings, Reviewer Feedback, and Recommendations Implementing CDISC Standards Within and Across Studies	Chris Decker	The CDISC/FDA Integrated Data Pilot was initiated in 2008 with the mission of demonstrating that a data submission created using the CDISC standards will meet the needs and expectations of FDA reviewers in conducting an integrated review of data from multiple studies and compounds. The Pilot was completed recently and the results are currently being compiled. This paper will provide an overview of the original goals of the Pilot, the major findings across the CDISC models, the feedback from the FDA participants, and the timeline for publishing the final report and submission package. The session will present a review of experiences implementing the CDISC models, FDA reviewer feedback, and provide the industry with the Pilot team's experiences with the new CDISC models and their use for data integration	csr, clinical trial, sdtm domain, change from baseline, dose escalation, adverse events, vital signs, adamig, controlled terminology, exposure summary, open label, data science, class variable, cdisc	20
123	The Compliance and Implementation of Clinical Trial Outcome Disclosure on ClinicalTrials.gov	Eric T. Sun, Craig Anderson	Section 801 of the Food and Drug Administration Amendments Act (FDAAA) mandates the following information be loaded into ClinicalTrials.gov effective from 27SEP2009 for all trials for Drugs, Biologics and Devices: The basic results information described in the law. Information about serious and frequent adverse events (AE) observed. To comply with the Law, the trial sponsor must register Phase I trials in patients and Phase II to IV trials as early as first regulatory approval and at the latest 21 days after first enrollment. To accommodate these additional requirements SOPs were established and cross functional teams were involved in the implementation collaboratively. In this paper, the following points are discussed. the brief background and the drive of FDAAA Section 801 the reaction and formation of a team at sanofi-aventis for analysis and solutions * the experience learned in stages (e.g	adverse events, clinical trial, serious adverse event, secondary endpoint, macro variable, macro definition, proc freq	13
124	The Graph Template Language and the Statistical Graphics Procedures: An Example-Driven Introduction	Warren F	This paper provides a gentle, parallel, and example-driven introduction to the graph template language (GTL) and the statistical graphics (SG) procedures. With the GTL and the SG procedures, you can easily create professional-looking statistical graphics and modify the graphs that the SAS System automatically produces. Most graphs in this paper are produced in two ways: one graph is produced with the GTL, `PROC TEMPLATE`, and `PROC SGRENDER`; the other graph is produced more directly with `PROC SGPLOT` or one of the other SG procedures. Each example provides you with prototype programs for getting started with the GTL and the SG procedures. Although you do not need to know the GTL to make many useful graphs, understanding the GTL enables you to create custom graphs that cannot be produced by the SG procedures	proc step, histogram, box plot, scatter plot, proc sgplot, predictor variable	20
125	The Healthcare Performance Dashboard: Linking Strategy to Metrics	Gregory S. Barnes Nelson	There is little doubt that the change in our global business environment will continue to outpace complacency and "tried and true" approaches for managing costs and increasing revenues. At the epicenter of this change we have a tsunami of data generated at the point of daily activities - data about patients, costs, operations and outcomes - but are we connecting that data to our strategy? Are we putting data to work for us or are we merely responding to that data? Ultimately there are a panoply of techniques, methods and frameworks that can be put to work to help us manage more effectively. And while, we don't take a position with respect to one tool being better than another, we do think that those that put data to work for us and keep our goals in front relative to our actual performance will help us deliver strategy more effectively		10
126	The Ins and Outs and Ups and Downs of Sorted Data	Andrew T. Kuligowski	Many things that appear to be simple turn out to be a mask for various complexities. For example, as we all learned early in school, a simple drop of pond water reveals a complete and complex ecosystem when viewed under a microscope. A single snowflake contains a delicate crystalline pattern. Similarly, the decision to use data in a sorted order can conceal an unexpectedly involved series of processing and decisions. This presentation will examine multiple facets of the process of sorting data, starting with the most basic use of `PROC SORT` and progressing into options that can be used to extend its flexibility. It will progress to look at some potential uses of sorted data, and contrast them with alternatives that do not require sorted data. For example, we will compare the use of the BY statement vs. the CLASS statement in certain PROCs, as well as investigate alternatives to the `MERGE` statement to combine multiple datasets together	proc sort, proc step, missing value, by group processing	9
127	The Interaction Between the SYNTAXCHECK and OBS= System Options During Batch Unix Sessions	Robert Adams	A SAS program submitted to Unix in batch mode doesn't behave the same way as a program submitted to Unix interactively. One of the reasons is because of the SYNTAXCHECK and OBS= System Options. This paper will examine the interaction between these options and will show how they need to be set in order for programs to create the output intended. As a result, programmers new to batch processing on Unix can spend more time getting output and less time investigating the inner workings of the SAS system	options statement, create table, proc step	4
128	The Little Engine That Could: Using EXCEL LIBNAME Engine Options to Enhance Data Transfers between SAS and Microsoft Excel Files	William E. Benjamin Jr.	Many people are not aware that the SAS Access for PC Files product will allow SAS Programmers to access an `EXCEL` spreadsheet in much the same way as any other SAS file. There are of course some restrictions, but there are also a lot of options that help remove some of the bumps in the road. The `LIBNAME` statement allows the user to define an `EXCEL` file in SAS terms and gives the programmer access to `LIBNAME` and data set options to control how the `EXCEL` file is defined, accessed, and yes even how the data will be formatted. This paper will describe some of those options	excel, variable, libname statement, sas library, proc sql, dataset, proc step, character variable, libname engine, numeric variable, sysdate9, sas programmer, missing value	10
129	The Manager's Skill Set: How to Move from Programmer to Manager	Paul A. LaBrec	Programmers desiring to make the career move from the role of Programmer to that of Manager should be aware of the skills that comprise a manager's skill set, and define a plan of how to acquire those skills. Gaining that initial management title is a challenge, as those recruiting are usually seeking experienced managers. The more elements of the manager's skill set a candidate can acquire prior to seeking a management position the greater the chance of earning that first position. The "Manager's Skill Set" can be defined by using the followin g groups of skills: Project Management, Personnel Management (incl. recruiting and hiring candidates, as well as evaluating and developing employee s), Communication, Negotiation, Team Motivation, Conflict Resolution, and Financial Management	adam dataset, clinical trial	7
130	The Most Popularly Applied Vehicles in Health Industry for Data Transfer from SAS to Microsoft Excel	Guiping Yang	SAS is the most accommodating vehicle in statistical world for transferring data between SAS and other systems. There are many different tunnels available to achieve this function; however, this paper puts its focus on the data flow from SAS to `EXCEL` or its close relatives because of their well-known popularity in pharmaceutical and health realm. We briefed totally fourteen tools in this review, covering from the most basic to the relative advanced, from the most popular to the less heard, from the very traditional to the high-level in modern world. These include, but not limited to, `EXCEL` Functions, Macro, Dynamic Data Exchange (DDE), Output Delivery System (`ODS`), eXtensible Markup Language (XML) and `EXCEL` `LIBNAME` Engine. Both advantages and disadvantages of individual techniques were captured and evaluated for the users' convenience	excel, dataset, proc step, sas output, xls, libname engine, sample, ods csv, ods html, format statement, libname statement, missing value, proc export, proc report, sas enterprise guide, sas log, proc template, txt, sas base, proc sql	23
131	The new SAS 9.2 FCMP Procedure, what functions are in your future?	John H. Adams, Boehringer Ingelheim	Our company recently decided to upgrade from SAS 8.2 to SAS 9.2. In doing so, we had to roll up the cumulative changes over 5 intermediate SAS versions (9.0 Æ 9.1Æ 9.1.2 Æ 9.1.3 Æ ◊ 9.2 ), no easy task. We then tested most of the changes and new features and presented the results of our testing and observations to our global SAS user community. We discovered that there are so many new features in SAS 9.2 to get exited about. So many new options to control the environment, over a hundred new functions, dozens of new formats, exiting new features in Procs and `ODS` and so much more. This paper explores one area of the enhancements - functions. To be sure, SAS 9.2 has a very rich repertoire of functions. There are handy functions for almost everything, e.g. character , math, statistics and even perl functions. But SAS did us one better! They have now given us the capability to define our own functions with `PROC FCMP`	proc fcmp, proc step, data step, imputation, sas base, sysdate9, date variable	11
132	The SAS Hash Object: It's Time To .find() Your Way Around - Hands-On Workshop	Peter W. Eberhardt	"This is the way I have always done it and it works fine for me." Have you heard yourself or others say this when someone suggests a new technique to help solve a problem? Most of us have a set of tricks and techniques from which we draw when starting a new project. Over time we might overlook newer techniques because our old toolkit works just fine. Sometimes we actively avoid new techniques because our initial foray leaves us daunted by the steep learning curve to mastery. For me, the PRX functions and the SAS hash object fell into this category. In this workshop, we address possible objections to learning to use the SAS hash object. We start with the fundamentals of the setting up the hash object and work through a variety of practical examples to help you master this powerful technique. Note: this paper is the same as paper TU02 in the Tutorials section. Although the papers are the same, the presentations will be different	hash table, dataset, proc sql, lookup, hash iterator, out=, sysrc, sas programmer, sample, data merging, proc step	17
133	The SAS Hash Object: It's Time To .find() Your Way Around - Tutorial	Peter W. Eberhardt	"This is the way I have always done it and it works fine for me." Have you heard yourself or others say this when someone suggests a new technique to help solve a problem? Most of us have a set of tricks and techniques from which we draw when starting a new project. Over time we might overlook newer techniques because our old toolkit works just fine. Sometimes we actively avoid new techniques because our initial foray leaves us daunted by the steep learning curve to mastery. For me, the PRX functions and the SAS hash object fell into this category. In this workshop, we address possible objections to learning to use the SAS hash object. We start with the fundamentals of the setting up the hash object and work through a variety of practical examples to help you master this powerful technique. Note: this paper is the same as paper HW01 in the Hands-On Workshop section. Although the papers are the same, the presentations will be different	hash table, dataset, proc sql, lookup, hash iterator, out=, sysrc, sas programmer, sample, data merging, proc step	17
134	The SE Domain as Treatment Data Store: Using the SDTM Subject Elements (SE) domain to drive analyzing treatment	Jeremy Gratt	One of the advantages of the adoption of CDISC SDTM and ADaM data standards is being able to leverage the domain structures to improve consistency and quality of clinical trial analysis. Treatment can present a barrier to reporting standardization and automation due to the wide range of treatment designs. This paper demonstrates how the SDTM SE trial design domain can be used as a central treatment data store and used to build treatment into the ADaM datasets and clinical trial reports. The SE domain is further leveraged with a sponsor-specific SUPPTE domain that is used to "tag" SE with additional treatment meta-data used to define the treatment analysis. The approach presented in this paper is based on an implementation with a biometrics programming group focused on Phase 1 studies with complex trial designs	proc step, proc report, adam dataset, sample, create table, proc sort, proc sql, cdisc	10
135	The Super Genius Guide to Generating Dummy Data	Brian K. Varney	A common necessity in clinical development and prog ramming is having representative data to program an d develop against. Situations that can hamper this could be t hat the data has not been collected yet, the data i s too sensitive to share or just lack of resources to prepare and prov ide the data from the source data tables. This pape r is intended to provide methods to generate representative data whe ther one has the project data or only metadata in s uch a way that sensitive data is not revealed	proc sql, proc step, data step, macro loop, create table, dictionary tables, excel, sample, macro definition, %if-%then-%else	10
136	The Technical Phone Screen Hiring with One Hand Tied Behind Your Back	David J. Polus	In an era dominated by ever tightening budgets, how does a comp any ensure they're effectively hiring the right statistical programming candidate without the expense of a face -to-face interview? The Technical Phone Screen (TPS) is one tool used by most companies to gauge the skills of an individual applying for a posit ion. This paper explores best practices in the use of the TPS in today's challenging environment	sas base, clinical programming, excel	6
137	The Technical Programmer Manager The Best of Both Worlds	David J. Polus	A Technical Programmer Manager (TPM) is a position that falls between Project Management and Senior Statistical Programming. TPMs spend the majority of their time on production programming, but have a portion of their time allocated to provide line management for a number of team members. The typical TPM is patient, and enjoys mentoring newer team members and directing highly functional teams	sas programmer, production run	3
138	The Ubiquitous Clinical Trials Data Summary Table "Summary Statistics in Rows"	John Henry King	Many if not most summary tables creat ed from clinical trials data use basic ally the same format, they display a mixture of discrete (categorical) and/or continuous variables in a style that could be referred to as "summary statistics in rows". Each variable whether, discrete or continuous forms a group of rows in the table, the descriptive labels for these variables form the first left most column of the table. Continuous variable rows are made up of descriptive statistic descriptions e.g. N, Mean, Standard Deviat ion, Median, Minimum and Maximum. It is common for two or more of these statistics to be displa yed on the same row; "Mean (SD)" and "Min, Max". Discrete variable rows are formed from the descriptive values of t he levels of each discrete va riable, e.g. levels of Gender (Male, Female). Usually the treatment variable fr om the clinical trial forms groupings in colu mns to the right of the row descriptions	macro variable, proc means, proc step, interval variable, clinical trial, proc report	15
139	Tips for Creating Oncologic Efficacy Summary Tables using PROC LIFETEST and PROC PHREG	Scott Michael Ward	Survival statistics are used frequently within Oncologic Efficacy Summary tables. They are used to measure several types of outputs including Progression Free Survival, Time to Treatment Failure, Time to Disease Progression, Duration of Survival, and Duration of Response. This paper will show how to achieve the following types of outputs with `PROC LIFETEST` and `PROC PHREG` by offering some tips and techniques for fast and efficient results and outputs	proc step, proc phreg, hazard ratio, efficacy summary	7
140	Tiptoe through the Templates	Cynthia L. Zender	Are you confused about the difference between style templates, table templates, tagset templates, and graph templates? Do you wonder how they're all used with `ODS`? This paper provides an overview of all the different template types and how they're used with the Output Delivery System. From style and table templates, that first appeared with SAS 7 to the newest graph templates that appeared with SAS 9.2, this paper will provide an overview and several concrete examples for each template type. Along the way, we'll also discuss the template garden (SASHELP.TMPLMST) where all the templates live, how to transplant your templates to a different garden, how to come up with your own new variety of templates (`PROC TEMPLATE`), and how to find your way to the new template garden (`ODS` PATH). New features of `PROC TEMPLATE` syntax (such as the IMPORT statement) will be highlighted	proc step, proc freq, proc template, proc means, ods html, proc print, proc report, sas log, ascii, ods pdf, sas output, txt, output delivery system, sas enterprise guide	27
141	Traffic Lighting Your Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS	Vincent DelGobbo	"Traffic lighting" is the process of applying visual formatting to data. This paper explains how to use Base SAS 9 software to create multi-sheet Microsoft `EXCEL` workbooks (for `EXCEL` versions 2002 and later), and then traffic light the values that lie outside a range. You will learn step-by-step techniques for quickly and easily creating attractive, multi-sheet `EXCEL` workbooks that contain your SAS output using the ExcelXP `ODS` tagset. The techniques that are presented in this paper can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real time using SAS server technology is also discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented	excel, xls, date variable, proc report, output delivery system, sas output, proc template, proc step, sas base, sysdate9, proc print, dataset, by group processing, format statement, ods xml, sample, clinical trial	28
142	Two-piecewise Random Coefficient Model Using Proc Mixed	Shengjie Wu	In clinical research, the longitudinal data is gaining popularity. A pre-test is used as the baseline to minimize variation cross subjects. The example in this paper will show that repeated measures are taken not only after treatment but also before treatment with uneven time spaces. Two-piecewise random coefficient model is a powerful tool to model trends corresponding to time before and after treatment. The effect of treatment can be investigated by comparing two trends. In `PROC MIXED`, two-piecewise random coefficient model can be easily accomplished	proc mixed, proc step	6
143	Universal History: Who, What, When, and More	James R. Johnson	As an application programmer, have any of these ever happened to you? 1. Someone came to you and told you that your progr am did not work and when you asked what parameters they sent to the system they respond "I don't remember". 2. Your manager wants to know which features of your system are being us ed, but more importantly, which are not being used and can be de-supported. 3. Someone wants to know how long your program runs? W hat kind of load does that run time entail? That is, how many records were processed, or how many subjects, or how many datasets? Does the time of day matter? Most history files are designed to maintain an account of what program was run, who ran it, and when it was run. Some also store information about when the program stopped and how long it ran	macro variable, proc step, proc sql, macro definition	9
144	Updating SDTM Metadata Excel File (Define.xls) with SAS	Lin Yan	When preparing FDA submissions, one usually needs to create metadata Define.xls as working files in order to ensure that the structures and attributes of the submitted SAS da tasets are in accordance w ith FDA endorsed SDTM. To do that, continuous updating the Define files may be required.. When updating a Define file, one can choose to modify the `EXCEL` file directly. However, this common practice can be cumbersome when some changes need to be applied to multiple `EXCEL` spreadsheets. For example, it may be necessary to add some identical comments to the baseline definition of the Lab and many other fi nding domains with subsequent updates to the comments. In this case, working on the Define file from SAS, most of tim e, can be more efficient. This paper illustrates a method of modifying Define `EXCEL` files using SAS programs step by step, which includes some technical tricks to ensure correct data transfer between `EXCEL` files and SAS data sets	excel, dataset, proc step, data step, cdisc	6
145	Use Base SAS FILENAME URL to Build Surveillance and Monitoring System for New Clinical Trial Registration	Xin Wei, James Cai, Jim Rosinski	Clinical trial is an irreplaceable approach to test the efficacy and safety of new drugs in human subj ects. In recent years, the number of clinical trials sponsored by p harmaceutical industry, federal government or non-p rofit organizations has been rapidly increasing to develo p new therapeutics for the unmet medical needs. Cur rently, Clinicaltrial.gov, the central repository for clini cal trial registration developed by the national in stitute of health (NIH) and Food and Drug Administration (FDA), holds 80, 2 68 trials conducted in many nations and this databa se is expanding on the daily basis. To obtain the latest information on trials of interest require constantl y checking the website, which may be time and labor consuming. It would be beneficial to patients, regulators and tri al sponsors to have an automated data pipeline that extracts the l atest data from registered trials by dynamically fe tching the contents of on-line information in a pre-scheduled time frame	clinical trial, macro definition, data step, filename statement, dataset, data warehouse, sas base, macro variable, macro quoting function	7
146	Using Advanced RTF language to Enhance ODS RTF Output	Zhouzhou Xi	`ODS` has given SAS great power to control its output appearance. By using `ODS` to create outputs in Rich Text Files (RTF) format, SAS is able to produce almost any appearance for tables or listings that the clients want. There are so many skills or techniques out there to achieve this goal that even one book can not hold all. This article gives an example for improving the appearance of AE or Concomitant Medication tables by combining the Advanced SAS programming skills and usage of RTF language	proc report, proc step, concomitant medications	4
147	Using PROC SURVEYMEANS to determine the dispensing time of prescriptions in pharmacies in South Africa: A user's perspective	Martie Lubbe, Faans Steyn	This paper demonstrates the use of `PROC SURVEYMEANS` to determine the dispensing time of pharmacists in different pharmacy sectors nationally in South Africa (SA). In order to ensure that the results were representative of all pharmacy sectors in SA, a complex sampling design with stratification, clustering and unequal weighting was used. In order to make statistically valid inference from the sample to the study population, it was necessary to analyse the data taking into account the sample design	sample, phase ii, phase i, sample size, random sampling, phase iii, population	12
148	Using SAS Proc Power to Perform Model-based Power Analysis for Clinical Pharmacology Studies	Peng Sun	In this paper, we demonstrate that m any hypothesis testing problems in c linical pharmacology studies can be reformulated as a one- or two-sample t-test or equival ence test. Thereby one can use SAS `PROC POWER` to perform the power analysis. Sample programs for different hypothes is testing problems are pres ented in a variety of study design settings. Compared to other commercial software such as NQuery and PASS, using `PROC POWER` offers several advantages. First, one often obtains variance estimates by analyz ing relevant clinical data in SAS, thus it is more streamlined and convenient to conduct pow er analysis in SAS as well. Second, the documentation in SAS is more comprehensive than those in NQuery and PASS; this helps the users better understand the procedure and hence the users are less likely to make mistakes	sample, power analysis, equivalence testing, proc power, geomean, proc step, hypothesis testing, sample size, proc ttest, parallel group, degrees of freedom, alternative hypothesis, crossover study, pk parameter, sas output, anova	11
149	Using SAS to Compute Partial Correlation	Jianxin Lin, Aiming Yang, Arvind Shah	Partial correlation is used in many epidemiological studies and clinical trials when a researcher investigates a n association involving some potential confounding factors. Partial correlation is a method used to describe the relationship between two variables when controlling for effects of one or more variables in this relationship. In SAS, several procedures such as `PROC CORR`, `PROC REG`, and `PROC GLM` , can be used to obtain partial correlation coefficient. This paper will illustrate how to use these different procedures to get partial correlation, and explain the difference among these procedures. KEYWORDS: Partial Correlation, `PROC CORR`, `PROC REG`, `PROC GLM`	correlation, partial correlation, proc step, proc corr, proc glm, proc reg, proc genmod, sas output, percent change from baseline, multiple regression, change from baseline, regression analysis	5
150	Using the SAS Clinical Standards Toolkit to Validate CDISC SDTM Data	Gene Lightfoot, Bill J. Gibson	Presentation	data cleaning, sample, create table, proc sql, dataset, macro variable, macro call, lookup, proc step, adverse events	33
151	Utilizing SAS to Dynamically Generate Clinical Trial Data Transformation Programs	Clark Peirce, Johnny Johnson	We illustrate a technique, using SAS, to convert programming specification text into a set of uniformly formatted SAS programs used to perform a clinical database transformation. The method begins by reading an `EXCEL` worksheet that contains instructions on how to modify clinical database data. Next, we demonstrate how to parse those instructions and convert the text into formal SAS syntax. This syntax is then used to build a series of SAS programs that, when finalized and executed, perform the desired database transformation	clinical database, data transformation, sas syntax, attrib statement	7
152	Validate it Again?! Get SAS to do it!	Carol Matthews, Ginger Lewis, Olga Belotserkovsky	In today's pharmaceutical research environment, clinical project teams often want to see statistical output well before database lock and also want to see it frequently throughout the course of the study. Changing data values can affect the results of validated programs in subtle, yet significant ways. As a result, it is critical to revalidate programs after each data update to prevent errors in output. With this in mind, it is more important than ever for programmers to make their SAS code do as much of the validation work as possible. This paper will discuss several simple techniques that will put Base SAS to work looking for data issues that could adversely affect your program's result	data cleaning, proc step, proc compare, proc means, clinical trial, error message, macro loop, sample, abort, missing value, proc format, proc print, validation set, proc freq, proc sql, %if-%then-%else	13
153	Validation: Quick and Easy	Wancheng Luo	%diff_TL is developed to compare text files, especially, tables and listings for FDA submission. This macro can compare two files record by record, while most other programming tools only compare two files line by line. In addition, %diff_TL can be used to compare of titles, footnotes and headers. The output of the %diff_TL displays the discrepancies with source file names, page numbers and the positions, therefore the differences in original files can be easily traced. Using %diff_TL can effectively reduce comparison time and prevent errors.	key, data cleaning	14
154	Watermarking SAS Outputs Without Using Proc Template and ODS	Sam Mao	Hastily using confidential or draft listings and tables produced in SAS for business needs increases the risk associated with misusing or misinterpreting the results. A viable solution to reduce that risk is to use a visible watermark embedded in the SAS outputs such as a pale image or text displayed and/or printed behind the text. This paper will introduce a method of watermarking the output directly from SAS through an interface with Microsoft Office Word (Word) without using `PROC TEMPLATE` and `ODS`	sas output, data step, proc template, proc step, macro variable	6
155	When Best to Use the %LET Statement, the SYMPUT Routine, or the INTO Clause to Create Macro Variables	Arthur X. Li	Macro variables are the most essential part of the SAS macro facility. One can create macro variables by using the `%LET` statement, the SYMPUT routine, or the INTO clause in the SELECT statement from the `SQL` procedure. Sometimes a SAS programmer is often unsure when best to use which method due to a lack in understanding each step of macro language processing. This lack in understanding includes how SAS statements are transferred from the input stack to the macro processor and `DATA STEP` compiler, what role the macro processor plays during this process, and when best to utilize the interface to interact with the macro facility during the DATA or SQL execution. Once one grasps the intricacies of macro language processing, one will best know how to accurately create a macro variable	macro variable, macro put, call symput, symbol table, proc sql, sas log, macro trigger, macro facility, proc step, call symputx, &variable, macro definition, global symbol table, put statement, run, select	18
156	Which Sub-macros are Called by that macro?	Ginger Redner	Macro libraries often contain macros that call sub-macros, which may call additional sub-macros. This macro interlinking poses a problem when there is a need to know all the sub-macros that are used. This paper gives an overview of a program which identifies nested macros. Examples of ways in which this information can be productively used will also be discussed. This paper is intended for programmers of all levels who are interested in tracking the level of macro nesting in a macro library folder, as well as anyone interested in examples of useful ways to use this information	macro call, sample	7
157	Wonderful Unix SAS Scripts: SAS Quickies for Dummies	Raoul A. Bernal	Repeated simple SAS jobs can be executed online and in real-time to avoid having to directly execute SAS command files, producing immediate results directly into any Unix display window. The trick is to simulate SAS using Unix scripts, which can be executed just like any Unix command with minimum of one or two parameters. One advantage of making such SAS Unix scripts available is the quick access to SAS without having to learn actual SAS syntax. This reduces the number of exploratory data requests from non-regular SAS users, thereby empowering them to get "quickie" exploratory results without having to ask a SAS practitioner. Just like any Unix command, the results can be directed to an output file (other than the default output screen) for reporting	proc step, proc univariate, proc freq, proc print, proc contents, proc printto	9
158	Zoom, Zoom: Get Your Document to Scale on All Paper Sizes	Daniel O'Connor	This paper will help you learn how to scale your large reports so they will always fit the paper size. The paper includes tips and tricks of the Output Delivery System (`ODS`) style that will make things fit without paneling your tables or clipping your graphs. Scaling to fit is a very difficult problem; learn how to tell SAS to "just make it fit."	output delivery system, file statement, data visualization, sample	19

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