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BUGSPharmaSUG 2011 Papers - Index
152 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | 2 PROC TRANSPOSEs = 1 DATA Step DOW-Loop | Nancy Brucken | A limitation of PROC TRANSPOSE is that multiple variables in the VAR statements lead to one record per variable in the output dataset. Sometimes, this behavior is desired, but often we need a single record per BY statement as output. This paper demonstrates the use of the DOW-Loop to transpose the data properly in a single pass through the input dataset |
proc transpose, in=, proc step | source | 4 |
| 2 | A Cost-Effective SDTM Conversion for NDA Electronic Submission | Xiangchen (Bob) Cui, Scott E. Moseley, Min Chen |
When submitting clinical study data in electronic format to the FDA, sponsors are required to submit data definition tables (define.xml) and a reviewer guide (define.pdf), in addition to SDTM and ADaM datasets. The standardized, well-defined, and detailed define files minimize the time needed for FDA reviewers to familiarize the data, which can speed up the overall review process. The programming specification documentation serves as a part of a reviewer guide, as well as the documentation for programming validation. It is very crucial to ensure the consistency of the attribute of each variable among datasets, define files, and programming specification. It is highly desirable to automate this process to ensure technical accuracy and operational efficiency | sdtm domain, dataset, excel, sample, data cleaning, macro variable, adverse events, controlled terminology, order by, sdtmig, macro put, clinical trial, reviewer guide, sdtm programming, proc sql, proc step, macro definition, cdisc | source | 12 |
| 3 | A Cup of Coffee and Proc FCMP: I Cannot Function Without Them | Peter W. Eberhardt | How many times have you uttered "If only I could call this DATA STEP as a function"? If this statement describes you, then PROC FCMP are for you. This tutorial will get you started with everything you need to write, test, and distribute your own "DATA STEP" functions with PROC FCMP |
proc fcmp, proc step, sas log, put statement, return, sas function, sas programmer | source | 16 |
| 4 | A Many to Many Merge, Without SQL? | David W. Franklin | As a SAS Programmer, one of our common tasks is to merge data from two or more datasets. Most merges are 1-to-1 or 1- to-many, i.e. there is at least one dataset with a sequence of variables that create a unique record identifier. But what about the case where there is not a unique record in both cases, known as a many-to-many merge? Typically the advice is to use PROC SQL, however this paper looks at a way (and the code is presented) that a SAS datastep can be used to achieve the same result, with the advantage that you as a programmer have more control over the way SAS processes it. The technique introduced here will probably not change your reliance on PROC SQL to do a many-to-many merge, but it will add a valuable "utility" to your toolkit |
proc sql, proc step | source | 4 |
| 5 | A Mass Symphony: Directing the Program Logs, Lists, and Outputs | Tom Santopoli | When executing programs in SAS, it is efficient to direct the development versions of log, list, and output files to a different location than the production versions. Development versions are typically executed in interactive mode. However, to ensure quality, production versions are most effectively executed in batch mode. There are innovative SAS programming techniques that can automatically direct log, list, and output files to various destinations based on whether the program is executing in interactive mode (development) or in batch mode (production). These destinations can be set dynamically, according to the relative location of the executable program in the directory structure. Additionally, it is often useful for logs and lists to be displayed in the interactive windows as copies are simultaneously directed to external files. This presentation will explain an integrated approach to applying these techniques | macro variable, macro definition | source | 7 |
| 6 | A Modular Approach of Reporting Meta Analysis Statistics Using Forest Plots | Vikash Jain | Forest plots are the favorites for reporting frequently with the popularity of Meta Analysis Statistics associated with systematic review in the clinical research. They are used to report array of statistics like estimates of treatment effects between multiple studies or ratios between subgroups for multiple studies in consideration or also occasionally by other comparative features based on the objective of hypothesis analysis under consideration | forest plot, graph template language, proc step, odds ratio, proc template, confidence interval, box plot, in= | source | 8 |
| 7 | A Non-Invasive Macro to Track Submission Metadata in SAS Drug Development | Katrina E. Canonizado, Bradford J. Danner, Matthew J. Wiedel |
To address the concerns of program quality and transparency in the contract research organization (CRO) industry, program validation plays a central role in data analysis. As an aid to speed up this process, we provide fellow programmers and reviewers with what we refer as an audit program list. This list contains the current settings of the SAS session like the program name, data sets used, etc. In SAS Drug Development, this information is stored in SDDparms for individual programs within submission. This paper demonstrates briefly the macro %metadata we have written. The macro primarily utilizes SDDparms in tracking the information to build the audit program list | proc sql, macro variable, proc step, dataset, clinical research, permanent dataset, csr, clinical trial, order by, %if-%then-%else | source | 8 |
| 8 | A Practical Approach to Process Improvement Using Parallel Processing | Viraj R Kumbhakarna | In applications which process huge volumes of data for analysis purposes, it is often essential to minimize processing time to increase efficiency. Large data volumes (in the author's experience, row counts of 1.5 million and up to 2500 columns) result in very long processing times of about 4 to 5 days. It is often required to reduce the execution time for such processes. This paper discusses the following generic steps for improving any process. (1) Identify areas for improvement (understand the process thoroughly; analyze logs to identify steps which take the longest amount of time). (2) Look for certain processes that can be executed in parallel (actual parallelization of independent processes executed one after another; automated virtual multithreading; executing multiple in- stances of jobs in UNIX) | cpu time, transaction data, proc step, dataset, proc sort, where statement, if-then-else, proc format, flat file, key=, parallel processing | source | 16 |
| 9 | A Pragmatic Programmers Introduction to Data Integration Studio: Hands on Workshop (updated for DI Studio 4.2) | Gregory S. Barnes Nelson | sas data integration studio, dimension table | source | 35 | |
| 10 | A Programmer's Introduction to Survival Analysis Using Kaplan Meier Methods | John Ventre, Lisa Fine |
Survival analysis methods are common in clinical trials and other types of investigation. Programmers are often called upon to program these analyses and produce tables and figures that are often referred to as simply Kaplan Meier Curves. A basic understanding of these methods, their derivation and interpretation of their resulting statistics is provided for a programmer. In addition a presentation for extracting these statistics from PROC LIFETEST is given so the programmer can move from concept to usage. INTRODUCTION: WHAT ARE SURVIVAL ANALYSIS AND THE KAPLAN-MEIER ANALYSIS? Survival Analysis represents a set of statistical methods used to estimate lifetime or length of time between two clearly defined events and is sometimes referred to as time to response or time to failure analysis. Survival data is often analyzed in terms of time to an event. For example, in pharmaceutical research, it might be used to analyze the time to responding to a treatment, relapse or death |
proc step, confidence interval, survival analysis, km analysis, sample, proc contents, proc print, time variable, median, proc calis, censoring, survival function, sas output | source | 8 |
| 11 | A Recursive SAS Macro to Automate Importing Multiple Excel Worksheets into SAS Data Sets | Wenyu Hu, Liping Zhang |
In order to import data from Microsoft EXCEL into SAS, there are various methods such as PROC IMPORT, IMPORT WIZARD, ODBC and EXCEL LIBNAME engine. The optimal way often depends on the project needs and programmer's preference. The need for importing multiple worksheets adds more complexity to an already challenging job. This paper introduces a SAS macro that can automatically handle all EXCEL files with various numbers of worksheets in different subfolders and generate separate SAS datasets for each EXCEL file. As EXCEL files often contain multiple worksheets, we believe this macro is a flexible and user-friendly tool that has wide applicability for importing EXCEL files to SAS. Keywords: PROC IMPORT, EXCEL, recursive |
excel, libname engine, name range list, proc import, proc step, dataset, macro variable, macro definition, macro loop | source | 6 |
| 12 | A Regular Language: The Annotated Case Report Form | Ryan Wilkins, Joel Campbell |
As per CDISC and FDA regulatory requirements, including a link to the Annotated CRF (aCRF) from the metadata definitions facilitates a clear and transparent review of the clinical data supporting a regulatory submission [1]. In practice, providing this link can lead to a costly development of the electronic Case Report Tabulations (eCRT) or define.xml. This paper describes an automated approach rooted on the basis of Backus-Naur Form (BNF) to define the standard CDISC annotation construct. Then, using an intuitive design and a combination of tools - Adobe Acrobat, SAS XML Mapper, SAS Perl Regular Expression functions, and DATA STEP logic - programmatically applies and validates the links found in the define.xml at a drastically lower cost |
annotated crf, proc step, data cleaning, cdisc | source | 9 |
| 13 | A SAS Macro to Indent the un-indented SAS programs | Sreekanth Reddy Middela, Jyothsna Samala, Anirudh Bhetala |
The ease of understanding any SAS program written by a programmer depends on parameters like Indentation of the SAS code, amount of commenting used and so on. Though, usage of proper comments in the program is highly essential, proper indentation also plays an equally important role to make the code look comfortable and unambiguous to understand in less time by the reviewer. This paper will provide an open code for any SAS programmer across domains, to use it as a tool to indent the un -indented program, thus making it reviewer-friendly | do loop, end, macro call, sample, macro definition | source | 8 |
| 14 | A SAS Macro Tool for Selecting Differentially Expressed Genes from Microarray Data | Huanying Qin, Laia Alsina, Hui Xu, Elisa L. Priest |
DNA Microarrays measure the expression of thousands of genes simultaneously. Commercial software such as JMP/Genomics and GeneSpring use T-tests, ANOVA, or mixed models for statistical analysis to identify differentially expressed genes. These methods are valid for larger sample sizes. We work with an immunology laboratory that often needs to analyze data from experiments with less than 10 samples. The researchers developed an EXCEL-based process to select differentially expressed genes from microarray experiments with a small sample size. This process required complex, manual manipulation of data and could take weeks to complete. We created a SAS MACRO to automate the whole process. The program reads microarray data from and provides a summary report in EXCEL. The researchers can easily modify the parameters and repeat the analysis. The program made it possible to reduce data processing time from weeks to minutes with no mistakes related to manual manipulation |
excel, sample, macro parameter, macro definition, macro variable, sample size, variable list, do loop, macro loop, proc sql | source | 7 |
| 15 | A Six-Sigma/DMAIC Approach to Defining a SAS Macro Repository Strategy | Sandy Paternotte, Tom Fritchey |
Most programming organizations eventually gravitate to developing a strategy to store in some repository reusable SAS macro code for common use by their staff of SAS programmers. Such strategies should be developed to meet their organization's goals and needs. What is the right strategy for your organization? How do you begin to develop the most effective strategy? This paper will offer a sound Six-Sigma quality-based approach to a common-use macro repository strategy using the DMAIC (Define > Measure > Analyze > Improve > Control) process that will yield a guide to assessing what macros should best be placed into your firm's global macro repository | macro definition, macro parameter | source | 6 |
| 16 | A Special SDTM Domain RELREC and its Application | Changhong Shi, Beilei Xu |
The Related Records (RELREC) domain is a Special-Purpose Relationship domain in the Study Data Tabulation Model (SDTM). It is used to identify relationships between records in two (or more) domains. The relationship is sometimes important and unique for analysis. This paper discusses the basic attributes and meaning of the RELREC domain, as well as how to use different types of relationships in RELREC to facilitate analysis. Keywords: SDTM, Related Records, RELREC | concomitant medications, adverse events, cdisc | source | 8 |
| 17 | ADaM Standard Naming Conventions are Good to Have | Christine S Teng | The Clinical Data Interchange Standards Consortium (CDISC) Version 1.0 Analysis Data Model Implementation Guide (ADaMIG) was released in 2009. The ADaMIG specifies ADaM standard dataset structures and variables, including naming conventions. It also specifies standard solutions to implementation issues. This paper focuses on the naming conventions (GRy, FL, etc.) which enable the use of wildcards in programming code when displaying the data content for quality control (QC) purposes. This paper presents code examples using SAS data dictionary tables and Perl regular expressions to output the values of key variables of interest based on naming conventions. The code examples can also be applied to the standard Basic Data Structure (BDS) required variables such as PARAM and PARAMCD. An EXCEL workbook (using the SAS9 ExcelXP tagset) that contains metadata for all ADaM datasets of a mock study will be produced |
adam dataset, proc sql, dictionary tables, proc step, data cleaning, cdisc | source | 8 |
| 18 | Adjusting Analyses of Survey Results using a Predicted Probability of Response | Rob Gately | When conducting surveys, an issue that is typically encou ntered is a differential response rate for different categories of respondents. For example, women may re spond at a higher rate than men or vice-versa. The same could occur across different age groups, individuals in different geographic regions, or any of many possible characteristics describing the population. In this paper I use PROC LOGISTIC in SAS , along with a number of other data steps, to demonstrate the creation and use of a predicted probability of response (PPR). The PPR is used to generate an inverse probability of response (IPR) which becomes the weight in the adjusted analyses. The study for which this analysis takes place is a compone nt of a larger Risk Evaluation and Mitigation Strategies (REMS) examining patients' understanding of the risks associated with the us e of a particular medication |
logistic regression, logistic distribution, regression analysis, sample, binary variable, response variable | source | 12 |
| 19 | Alternative Approaches to Creating Disposition Flow Diagrams | Brian P. Fairfield-Carter, Suzanne M. Humphreys |
The description of subject screening, enrollment, randomization, completion and discontinuation is generally referred to as 'subject disposition'. Subject disposition tends to be reported in tabular format, providing frequency counts in various disposition categories, occasionally supplemented by 'time-to-event' graphs for subject discontinuation. However, since disposition basically describes how subjects 'flow' though the life of the project, the clearest and most intuitive means of presenting is with a flow diagram. The problem is that there is no SAS procedure especially well- suited for creating flow diagrams, probably because the layout and content of flow diagrams is highly variable and unpredictable | excel, txt | source | 10 |
| 20 | An Excel Framework to Convert Clinical Data to CDISC SDTM Leveraging SAS Technology | Sophie McCallum, Stephen Chan |
CDISC SDTM data is the standard format requested by th e FDA for clinical trial data submission. SAS is often used as an Extract, Transform, Load (ETL) tool to manually convert SAS extracts from a clinical database to SDTM format. While this is a reasonable approach, it can quickly become tedious, error -prone, and time -consuming. In addition, the code is difficult to maintain. As an alternative, Clinovo has developed an EXCEL framework that maps any clinical database to CDISC SDTM. All mapping definitions and rules written in EXCEL are then dynamically conve rted into a SAS program that can perform the SDTM transformation & validation with minimum programming through a series of SAS macros. Our framework is highly extensible and we can rely on a function library of SAS macros for mapping standard data elements. Additionally, this framework supports natively CDISC controlled terminology |
clinical data, excel, clinical database, macro definition, in= option, cdisc | source | 12 |
| 21 | An Introduction to SAS Hash Programming Techniques | Kirk Paul Lafler | SAS users are always interested in learning techniques that will help them improve the performance of table lookup, search, and sort operations. SAS software supports a DATA STEP programming technique known as a hash object to associate a key with one or more values. This presentation introduces what a hash object is, how it works, the syntax required , and simple applications of it use. Essential programming techniques will be illustrated to sort data and search memory-resident data using a simple key to find a single value |
hash table, lookup, sas log, composite key, proc sort | source | 8 |
| 22 | An Introduction to SAS/GRAPH or Quick Tricks with the GPLOT and GCHART Procedures And the Annotate Facility | Ben Cochran | The power and flexibility of SAS/GRAPH software enab les the user to produce high quality graphs, charts, and maps. With all this strength and flexibility, the new user is sometimes overwhelmed and discouraged from using these powerful tools. However, learning how to use the tools of SAS/GRAPH can be a pleasant experience. This paper introduces several ways to generate graphica l representations of your data. Starting with the importance of understanding their dat a, users are lead in a step by step process that ends with the generation of high quality plots and graphs. Finally, the paper ends with a short explanation on how to use the Annotate facility of SAS/GRAPH. Let us begin. Step 1: Understanding Your Data. Is some data more appropriate for generating graphical reports than other data? What data is most appropriate for plots and graphs? T he first data set to be examined is the SASUSER.RETAIL table |
proc gchart, proc step, bar chart, proc gplot | source | 12 |
| 23 | Analytical Methods for Post-Marketing Safety Surveillance | Annette Stemhagen, Juliane K. Mills |
Safety surveillance can be part of a comprehensive post-marketing program to satisfy a regulatory requirement or to collect additional data for use in drug development. The objectives of a safety surveillance program are to assess and quantify known or suspected drug safety issues, identify and characterize potential new risks following product marketing, and to monitor product-use patterns. The common types of post-marketing surveillance are passive and active surveillance, stimulated reporting, comparative observational studies, targeted clinical investigations, and other descriptive studies | adverse events, medical history, sample | source | 3 |
| 24 | Array of hope: Using SAS Arrays to produce data-driven analysis and reports | Aida Likaj | Programmers sometimes need to program reports when the complete range of data values is unknown at the time of the request. For example, the number of treatment cohorts may increase throughout the life of the study. This paper will show how to use arrays to produce results dynamically, minimizing the need for updates in statistical calculation and reporting regardless of the number of unique cohorts | macro variable, group by, create table | source | 4 |
| 25 | Assumptions and Consequences of Comparative Effectiveness Analysis Using Data Mining | Patricia B. Cerrito | Comparative effectiveness analysis often results in the rationing of treatment due to cost considerations; the definition of futility care is similar. One of the unintended consequences of these concepts is to diminish successful treatment of patients on the margins. For example, the gestational age of neonates is decreasing as successful treatments are discovered in the course of practice. Similarly, treatments for HIV and cancer improved outcomes as newer medications were combined with older treatments. We want to examine trends in the use of treatments from their initial approval to see how the treatments spread through patient conditions and severities. We also want to examine whether a focus on medications results in higher treatment costs elsewhere | association rules, adverse events, market basket analysis, sas enterprise guide | source | 21 |
| 26 | Automated Generation of Clinical Study Reports | Suhas R. Sanjee, Rajavel Ganesan, Jason Clark |
The results of clinical / pre -clinical trial s are presented to regulatory agencies in a Clinical Study Report (CSR). A CSR typically consists of a number of tables, listings and graphs each of which are stored as individual files. The process of incorporating these tables, listings and graphs in a CSR is laborious and potentially error prone. An 'automated' technique is presented in this paper which uses SAS and Windows Scripting Host (WSH) to generate a CSR document populated with specified tables, listings and graphs. This way, parts of CSR can be generated by a simple call to a SAS macro. SAS and WSH can be used to automate routine tasks performed in MS Office. WSH is a powerful and comprehensive utility which facilitates the controlling of Microsoft applications such as MS Word programmatically. The WSH utility allows VBScript programs to run on the Windows operating system as stand -alone applications | excel, macro definition, adverse events, clinical trial | source | 4 |
| 27 | Automated or Manual Validation: Which One is for You? | Richann J. Watson, Patty Johnson |
Validation is an essential part of the derived data set, summary table and listing development process. Validation can require that an independent program be created that confirms the results of the original program are accurate. It is common practice that derived data sets are validated using a more automated method, but it is not as common that table and listing outputs are validated using an automated method. A validation programmer often manually compares the results of her independent programming to the original production output or results. This entails checking not only the titles and footnotes and other cosmetic aspects of the output but also the informational aspect of the output. The manual method of checking the informational aspects of the output is a time consuming process and is subject to human error. This paper presents an automated approach for performing a 100% validation of the informational portion of summary table and listing outputs | data cleaning, validation set, dataset, proc step, proc report, proc compare, sas output, sample, macro definition | source | 5 |
| 28 | Automating the Installation and Operational Qualifications of Your SAS Applications with the SAS System | Joe Perry | Many industries require that new software installations go through checks that they have both been installed correctly (Installation Qualification or IQ) as well as operate as intended (Operational Qualification or OQ). Modest software development projects can use one or more brute force methods of performing or ensuring the critical software qualifications of the software placed in the hands of the end-user, the application, even for modest software applications, can tax the patience of any person or company. As anyone who plans, executes or supports software validation activities knows: preparing, executing and documenting the validation processes can be time consuming and costly. Recognizing this, the SAS Institute has pre-packaged IQ and OQ routines that ease the task of performing installing SAS in controlled environments, saving users considerable time and money | data cleaning, macro variable | source | 7 |
| 29 | Automating the Process of Preparing Data Definition Document for NDA Electronic Submission from Programming Specification in Word Format | Min Chen, Xiangchen (Bob) Cui, Scott E. Moseley |
When submitting clinical study data in electronic format to the FDA, Case Report Tabulations Data Definitions in an XML format (define.xml) are required for both CDISC SDTM and ADaM to provide the domain and variable metadata in order to improve the efficiency of the Regulatory Review process. The programming specification documentation is the key to prepare define.xml, for it provides most of, if not all, the information in define.xml. In pharmaceuticals industry, the programming specification is often created in Microsoft Word format due to its popularity. Automating the process of preparing define.xml from Word specification is of great help to reduce the tedious manual work and achieve the high quality of the submission. This paper introduces an effective way of automating define.xml preparation process from Word document. The key part is to read Word document to SAS, and then filter the useful information from the programming specification | excel, adam dataset, dataset, cdisc | source | 12 |
| 30 | Automatization of Patient Characteristics Report | Mirjana Stojanovic | This paper describes a standardized and presentable patient characteristics report. In an effort to standardize the appearance of the patient characteristics' report our team decided to creat e one macro that will produce patient characteristics tables for all studies. This macro prov ides an easy method for producing standardized and nicely formatted tables. It uses the same lo gic and structure across all studies. In addition the macro automatically computes all stratification parameters based on number of strata and number of levels within each strata. This paper will outline the features of the macro including examples of code and output. This paper is intended for programmers with a sound foundation in SAS macro programming | macro variable, macro loop, %if-%then-%else, macro facility, macro definition | source | 8 |
| 31 | Basic Understanding on SE Domain for Beginners | Gayatri Karkera | The Subject Element (SE) domain contains subject level data about the timings of each subject transitioned from one element (such as screening period, treatment period, follow-up period, etc.) to the other. These element s are presented in the Trial Element (TE) Domain by providing the rules for starting and stopping the element. To achieve this, other relevant domains are referenced. This paper is intended for the beginner in the clinical programming field to understand what this domain consists of, its significance, how do we create/ define this domain, its interdependence with other domains and finally how do we check that the domain so defined/ created is SDTM compliant. SE DOMAIN: The preliminaries It is very important for clinical programmer to clearly un derstand the life cycle of a subjec t in a given trial. The SE domain precisely achieves this objectiv e. Let us take a canonical example in a non-clin ical set-up to understand this at a conceptual level | proc calis, trial elements, cdisc | source | 4 |
| 32 | BAT Files: Run all Your Programs with One Click in PC SAS | William Conover | This is a simple, fast and easy way to automatically create a BAT file that runs all of the SAS programs in one directory | macro variable, txt, batch processing | source | 5 |
| 33 | Better Ways to Speak to Your System Using SAS: Automate Routine Tasks by using X, SYSTASK & FILENAME | Ranganath Bandi, Harini Kunduru |
The main objective of this paper is to discuss how interactions with your operating system can be done using SAS by triggering the operating system's commands to fetch required information to make your programming easier and more robust in nature. This paper mainly deals with usage of the X, SYSTASK and SAS FILENAME statements in combination with the PIPE device type to interact with the host operating system and how these statements can be used to attain the same results. Programmers will be guided through some of the system commands in both the UNIX and Windows operating systems environment which will help them to get a better understanding of how to make optimal use of the commands to automate certain routine tasks without having to re-do them manual ly each time and re-documenting. This paper gives examples related to creating directories; copying existing files to new directories, fetching attributes of external files, and opening EXCEL spreadsheets, CSV files, etc |
excel, filename statement | source | 10 |
| 34 | Beyond Double Programming - SAS Programming By Design (PBD) with Soop | Laiju Zhang | data step, independent qc, macro call, excel | source | 4 | |
| 35 | Beyond the Basics: Advanced REPORT Procedure Tips and Tricks Updated for SAS 9.2 | Allison McMahill Booth | This paper is an update to the 2007 SAS Global Forum Paper "Beyond the Basics: Advanced PROC REPORT Tips and Tricks"-a compilation of tips for producing desired PROC REPORT output with SAS 9.1.3. This paper offers tips and tricks for getting desired PROC REPORT output with the new functionality and enhanced features that are specific to using SAS 9.2, such as: exploring the new SPANROWS option for repeating group and order variables on a continuing page using the ODS TAGSETS.RTF statement with PROC REPORT using the new border control for creating gridlines in all destinations changing nodes in an Output Delivery System (ODS) PDF bookmark by using the DOCUMENT procedure Other new topics include: conditionally executing a LINE statement changing the style attributes of spanned headers adding text at the end of a report, even with a PAGE option taking control of across variables Sample code and output are included with each topic |
proc report, proc step, ods pdf, sas output, ods html, ods rtf, output delivery system | source | 18 |
| 36 | Beyond the Comma Delimited File for Bringing Data into a SAS Dataset | David W. Franklin | "Help! I got this AE data in a Word document and I need it in a SAS dataset within the hour!" Getting data from outside SAS is an important task for many who deal with data from different sources. There are a number of procedures that SAS have developed over the years that can get data from sources like Microsoft EXCEL and Access with PROC IMPORT, but still there is that time when we get data in a flat text file that is not suitable for this approach. Based on a real-world example of an Adverse Event listing in a text file, this paper introduces several ways the INFILE and INPUT statements inside a datastep, in which SAS can still bring in your data from outside source to a dataset, even though it is not in one of the more commonly recognized electronic formats |
input statement, dataset, txt, infile statement, adverse events, excel | source | 5 |
| 37 | Brave New World: How to Adapt to the CDISC Statistical Computing Environment | Jeffrey M. Abolafia, Frank C. DiIorio |
clinical data, data cleaning, adam dataset, sdtm domain, data governance, statistical programmer, clinical trial, controlled terminology, data science, excel, cdisc | source | 11 | |
| 38 | Building a Hosted Statistical Computing Environment: Is it Possible? | John Leveille | Small pharmaceutical companies face a large challenge of implementing a compliant and validated environment with limited resources, no QA, and an IT staff who thinks SAS is a European airline. They go searching for new fangled technology to solve their problems, only to find high priced commercial solutions that are more of a sledgehammer on a pin. So small companies are stuck: they can't afford commercial solutions and don't always have the knowledge to implement their own. This paper will present a case study of how a small pharmaceutical company implemented a hosted computing environment including development and production workflow, version control, syncing of clinical data and a set of robust processes. And the best part is that they did at a price tag that was right for their budget allowing them to grow in the future | data cleaning, clinical programming | source | 6 |
| 39 | Caution: Hazards Crossing! Using the Renyi Test Statistic in Survival Analysis | Matthew Davis, Sharon X. Xie |
One of the main assumptions regarding a number of survival analysis techniques is that of proportional hazards. If the hazard rates are found to cross, analyses such as Cox proportional hazards regression and log-rank test will either lose power or be rendered inadequate. One way to adequately analyze survival data with crossing hazard rates is to obtain inference using the Renyi test statistic. Both an example and a macro will be provided to calculate the Renyi test statistic with various weights | survival analysis, hazard function, rank test, log-rank test, wilcoxon test, sample, degrees of freedom, cumulative hazard, life table, markov chain, proc step, proc phreg, normal distribution, regression analysis | source | 9 |
| 40 | CDISC Variable Mapping and Control Terminology Implementation Made Easy | Balaji Ayyappan, Manohar Sure |
CDISC SDTM (Study Data Tabulation Model) standards have become a widely accepted form of representation of the primary component of CRT - data tabulation datasets. The core challenge in converting non-CDISC-compliant clinical data from ongoing studies and legacy studies is mapping of data to CDISC compliant domains and variables. It can be time consuming as the information lies in multiple places - clinical datasets, metadata, CRFs, protocol, SAP, SDTM implementation guide, etc. If only there was a tool bringing most of the required components on one screen, enabling you to dynamically map raw data components to SDTM compliant components using these pieces of information. In quest to achieve this we have created a utility that makes this process less frustrating and almost enjoyable | excel, controlled terminology, format statement, dataset, proc step, cdisc | source | 5 |
| 41 | Challenges in Implementing ADaM datasets: Balancing the Analysis-Ready and Traceability Concepts | Pushpa Saranadasa | According to the ADaM Implementation Guide Version 1.0, analysis datasets ought to adhere to certain fundamental principles. One such principle is "Analysis datasets should have a structure and content that allows statistical analyses to be performed with minimal programming. Such datasets are described as 'analysis-ready'." Another fundamental principle of the ADaM standards is "Traceability ". This standard is of great importance in ADaM since traceability facilitates transparency, which is an essential component in building confidence in a result or conclusion. A CDISC-compliant submission includes both SDTM and ADaM datasets; therefore, it follows that the relationship between SDTM and ADaM must be clear. This highlights the importance of traceability between the input data (SDTM) and the analyzed data (ADaM). This paper explains the challenges encountered when applying these two principles, e.g., analysis-ready data and traceability, in real life clinical data | adam dataset, clinical data, adverse events, proc step, data science, cdisc | source | 7 |
| 42 | Choosing the Best Method to Create an Excel Report | Romain Miralles | PROC EXPORT, LIBNAME, DDE or excelXP tagset ? Many techniques exist to create an EXCEL file using SAS . Which one is the best one? Well, it depends; each of them offers some unique advantages. LIBNAME statement is a new and useful option availa ble with SAS 9 but old methods like DDE are still v ery powerful and offer some unique capabilities. Knowing and und erstanding the different techniques is essential fo r SAS programmers to quickly and effectively produce a report that will meet the requirement provided by the customer. This paper will briefly describe the main technique s to generate an EXCEL file from SAS, and provide recommendations on the appropriate method to use when an EXCEL output must be created |
excel, proc export, proc step, name range list, libname engine, sas programmer, dataset, label statement, ods excel | source | 12 |
| 43 | Choosing the Road Less Traveled: Performing Similar Tasks with Either SAS DATA Step Processing or with Base SAS Procedures | Kathryn McLawhorn | When you are seeking a solution that requires SAS code, do you choose the path that is marked with DATA STEP programming? Or, do you prefer the path that applies Base SAS procedures? Programmers often tend to use just one path, based on their programming skills and personal preferences. In many situations, you can use either DATA STEP logic or procedure code to arrive at the same solution. To help broaden your horizons, this paper provides examples of common situations in which using either method leads to the same result. While this paper compares coding methods, it does not promote one particular method nor examine differences in efficiency. The discussion is aimed at intermediate to high-level users. Whether you are a loyal DATA STEP coder or a devoted proponent of procedure syntax, this paper offers some alternative techniques to help diversify your programming knowledge and skills |
proc step, by group processing, proc report, proc tabulate, proc print, sas base, proc freq, numeric variable, proc transpose, class variable, proc means | source | 12 |
| 44 | Classification of frequent snoring from routine medical examinations using the NHANES Database | Barry W. Row | Increased upper airway resistance during sleep, or snoring, is a risk factor for health problems affecting individuals of all ages, such as increased obesity, hypertension, alterations in glucose metabolism, dyslipidemia, and inflammation. A logistic regression model was employed to investigate how well routine physiological and laboratory measures predict frequent snoring using survey and clinical data derived from 2005-2008 National Health and Nutrition Examination Survey (NHANES). Gender, age, BMI, diastolic blood pressure, triglycerides, total cholesterol, and alanine aminotransferase level were associated with frequent snoring. Our findings suggest that frequent snorers may be at increased risk for the development of cardiovascular disease, metabolic dysfunction, and liver damage | logistic regression, logistic distribution, regression analysis, sas enterprise guide, odds ratio, feature selection, data mining | source | 11 |
| 45 | Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterprise Miner | Patricia B. Cerrito | Clinical trials typically involve a random selection of subjects to receive an experimental treatment or a control. They often use a minimal sample size, a short term rather than a long term period of study, and clinical trials often use surrogate endpoints. Inclusion/exclusion criteria are used to create a fairly homogeneous set of subjects so that the assumption of normality is relatively valid. Because of the nature of clinical trials, the database is designed to optimize the statistical analysis; most of the techniques used are available in SAS/STAT and there is relatively little preprocessing required prior to the statistical analysis. In contrast, health outcomes typically use data collected in the course of patient care with databases that are not designed for statistical analyses. The data are extremely messy and require considerable preprocessing; They are also observational and require consideration of potential and/or actual confounding factors |
sample, regression analysis, sample size, effect size, proc hpsplit, clinical trial, type ii error, type i error, central limit theorem, logistic distribution, surrogate endpoint, data mining, outlier, confidence interval, logistic regression, survival analysis, feature selection, gamma distribution, prior probability, data science | source | 15 |
| 46 | Combining RTF Graphs | Lucius Reinbolt | ODS RTF easily enables the placement of SAS graphical output from a single program into a document. While a multitude of graphical files can be produced, the real challenge is getting all those files into one integrated file for review or reporting use. Many existing concatenation approaches rely on manual labor or external programs. The process described in this paper uses a single SAS program to concatenate multiple individual RTF graphics files by adapting the literal RTF code. Combining all of these files within an efficient and versatile SAS environment provides an added layer of traceability when working in an industry compliant environment such as SAS Drug Development. Each individual rich text formatted file has a similar structure consisting of a header and document group area. The header group controls the display characteristics and the document group area defines the appearance of the display images |
sample, macro call | source | 4 |
| 47 | Computing Percentage Using PROC TABULATE - from Simple to More Complex | Wende (Ted) Tian, Hong (Lily) Zhang |
The TABULATE procedure can calculate the percentages of the total count or sum of analysis variables for individual cells. This paper will first review how to calculate basic percentage using PCTN, PCTSUM, ROWPCTN, ROWPCTSUM, COLPCTN and COLPCTSUM, and then focus on creating more complex percentages on concatenated tables and computing percentages of subtotals, etc |
treatment arm, proc tabulate, class variable, proc step | source | 11 |
| 48 | Confessions of a Clinical Programmer: Dragging and Dropping Means Never Having to Say You're Sorry When Creating SDTM Domains | Janet E. Stuelpner, Jack Shostak |
We have been clinical programmers for many years. Our first instinct has always been to write the SAS code by hand because that is all we had in the past . That meant knowing a great deal of syntax and always having the manuals handy. It also meant pages and pages of code that were difficult to correct, difficult to maintain and hard to reuse for different compounds or devices . The first level of growth came when SAS introduced various windows and wizards (e.g., Import/Export Wizard, Report Window, Graph -n-Go) that gave us the ability to start using the wizard and then grab the SAS code and change it as necessary. The next innovations from SAS were tools like Enterprise Guide and Clinical Data Integration with their Graphical User Interface (GUI) that made programming a great deal easier, faster and much more efficient. You can still access all of the different data sources that you need from SAS data sets, spreadsheets and/or relational databases, but in a much easier way | sas base, sas enterprise guide, sdtm domain, proc sql, sas programmer, proc step, proc sort, clinical trial, data governance, data validation rules, raw data, study day, dataset, excel, data cleaning, cdisc | source | 12 |
| 49 | CONTRAST and ESTIMATE Statements Made Easy: The LSMESTIMATE Statement | Kathleen Kiernan, Randy Tobias, Phil Gibbs, Jill Tao |
In many SAS/STAT modeling procedures, the CONTRAST and ESTIMATE statements enable a variety of custom hypothesis tests, but using these statements correctly is often challenging. The new LSMESTIMATE statement, available in ten procedures in SAS/STAT 9.22 software, greatly simplifies the use of these statements. The LSMESTIMATE statement enables you to side-step parameterization issues and to specify custom tests in terms of population quantities of direct interest (the LS-means). The LSMESTIMATE statement also implements a new nonpositional syntax for specifying contrasts. This paper discusses these new features and demonstrates them with examples from actual user questions to the Statistical Procedures group in SAS Technical Support |
proc step, proc genmod, proc glm, main effect, logistic distribution, proc glimmix, proc logistic, proc mixed, multiplicity, proc plm, anova, class variable, f-test, fixed effects, proc phreg, odds ratio, two-way anova, interaction effect, proc surveylogistic | source | 19 |
| 50 | Cost-Effectiveness Of Primary PCI At Hospitals Without Onsite CaABG | Pedro Ramos | sample, sensitivity analysis, clinical endpoint, sample size, clinical trial | source | 21 | |
| 51 | Create a Format from a SAS Data Set | Ruth Marisol Rivera | Many times we have to apply formats and it could be hard to create them specially if there are a lot of values to consider. But if you have all the values on a SAS data set you can pull them from there instead of creating the format manually (typing the data). This paper shows how to do that just by saying which variable you are going to use for the start of the range values (or a single value) and which value you want to receive back (a variable value or a pre-established value) | merge statement, dataset | source | 7 |
| 52 | Creating a Customized Graph for Adverse Event Incidence and Duration | Sanjiv Ramalingam | Adverse event (AE) analysis is a critical part of clinical trial data analysis, and is commonly handled through conventional summary tables and listings. However, pres enting adverse event information graphica lly at the individual subject level may help the clinician visualize the assessment of safety, emphas ize the adverse events of special interest, and compare the adverse events between treatments and subjects. A very robust technique to create such a graph using the annotate feature is discussed. The technique also involves dynamically creating SAS statements using PROC SQL and SAS Macro |
macro variable, adverse events, proc step, proc sql, proc format, trial elements | source | 7 |
| 53 | Creating a define.xml file for ADaM and SDTM | John H. Adams | The use of Define.xml files is currently required for most FDA submissions. While the define.xml file process for SDTM only submissions is pretty stable now, many users in the pharmaceutical industry are still struggling with define.xml files that also cover ADaM submissions. A define.xml is central to any electronic FDA submission. It is what a reviewer sees first and guides the reviewer through the objectives, analyses and data for the submission. You can think of the define file as a container of metadata (and table of contents) that describes all of the data and analysis that a submission contains. While it is a machine readable file, an accompanying style sheet allows the reviewer to display and read the file in any browser. Since the define.xml file has imbedded active links, the reviewer can easily drill down into the data and or supporting documents. Define.xml files are dependant on two other issues, a schema and a style sheet | sample, crf, cdisc | source | 18 |
| 54 | Creating Customized Patient Profiles using SAS ODS RTF and PROC TEMPLATE | Andrea Ritter | Patient profiles (a.k.a. case report form tabulations) involve many disparate pieces of information about a single subject, displayed in various formats and all contained in a single word document. This article presents an example for producing patient profiles using SAS PROC TEMPLATE table definitions and the Output Delivery System (ODS) with the DATA STEP |
proc template, proc step, demographics table, vital signs | source | 10 |
| 55 | Creating Forest Plots Using SAS/GRAPH and the Annotate Facility | Amanda Tweed | Forest plots have become common in the pharmaceutical industry as a way of comparing the relative strength of treatment effect by providing a visual representation of the amount of variation between different groups. At present, SAS/GRAPH does not have a stand alone procedure that can be called to generate these plots. Creation in SAS requires additional processing of the data and use of the annotate facility. This paper demonstrates the development and application of a simple SAS program for generating forest plots as part of a Phase III clinical trial. It describes the annotate facility and how it was applied to achieve the desired result. It also highlights some of the interesting features that were added in response to requests from study clinicians and statisticians |
forest plot, confidence interval, proc gplot, phase iii, treatment arm, treatment effect, sample, proc step, clinical trial | source | 7 |
| 56 | Creating Hyperlinked PDF Graphical Patient Profiles with PROC REPORT | William Conover | Using SAS 9.1.3, SAS/GRAPH and the ODS PDF destination, the methods described here create graphical patient profiles with hyperlinks to data listings |
proc step, proc report, ods pdf, proc print, adverse events, vital signs, clinical trial, sample | source | 11 |
| 57 | Creating reference amplicons and genotyping using the SAS System | Kevin R. Viel | The sequence of DNA nucleotides flanked by the primers is known as an amplicon and is frequently used in alignment-based variation scans. The goal of this paper is to describe two BASE SAS macros that generate the flanks for each base in the reference amplicon that can be used to make a genotype call and record the result and its associated data in a database. The principle underlying this approach is to indentify loci using the flanks by incorporating them in regular expressions (regexes) used in the PRX functions in BASE SAS. We can genotype single nucleotide substitutions (single nucleotide polymorphisms or SNPs), copy number variants, insertions, deletions, and inversions. By querying the database we can report whether a base is a variant or whether we did not cover it -an essential quality control process | sas base, prxmatch | source | 15 |
| 58 | Creating SDTM Datasets from Legacy Data | Fred Wood | Many companies have been converting legacy data (data that are in a non-standardized, proprietary format) to the CDISC SDTM (Study Data Tabulation Model) format. Reasons may include the following: 1) anticipating an FDA need, 2) a sponsor need, such as when non-standard data are being prepared for the ISS/ISE, and 3) getting a specific FDA request. This paper will provide some background on the SDTM and SDTM Implementation Guide and discuss some of the issues facing companies who are performing such conversions. While attendees are expected to be familiar with the SDTM and SDTMIG, this workshop will provide a brief overview of both. Attendees will be given examples of legacy data and be asked to represent it in an SDTM-compliant format. Legacy data will be provided in EXCEL as well as in SAS datasets, so knowledge of SAS programming is not required. A sharing and |
adverse events, label statement, sdtm domain, concomitant medications, cdisc | source | 6 |
| 59 | Creating Stylish Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS | Vincent DelGobbo | This paper explains how to use Base SAS 9 software to create multi-sheet EXCEL workbooks (for EXCEL versions 2002 and later). You 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 and ODS styles. 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 discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented |
excel, xls, output delivery system, sas output, proc report, proc template, sas base, proc step, by group processing, ods xml, sample, proc format, dataset | source | 22 |
| 60 | Cumulative Incidence Ratio Plots | Sai Ma | Cumulative incidence ratio (CIR) is a measure of association that is the ratio of two cumulative incidences, or the proportion of one group (e.g. a treated group) who experiences an event relative to the proportion of another group (e.g. a control group) who experiences the event. This paper shows how to derive CIR and a point-wise 95% confidence interval with the aid of SAS procedure PROC LIFETEST, and how to use the Miettinen-Nurminen solution to handle some special cases. A graphic representation that aims to better detect the group difference is introduced |
confidence interval, proc step, kaplan-meier plot, proc greplay | source | 5 |
| 61 | Data Edit-checks Integration using ODS Tagset | Niraj J. Pandya, Vinodh Paida |
In the Clinical trials data analysis and reporting work, though the Data Management works hard to ensure high quality data as per the Good Clinical Practices, data issues still can exist. The study programmers extract data for the study and checks for potential data issues to ensure the data integrity by executing edit check programs. This paper demonstrates how to effectively integrate all different edit checks into one output file. Also, this article talks about some important and very common edit checks. This paper incorporates the use of ODS tagset feature that is available in SAS 9.1.3 and later versions |
adverse events, data validation rules, excel, date variable, xls, macro definition, consistency, clinical trial | source | 6 |
| 62 | Data Quality Review for Missing Values and Outliers | Ying Guo, Bradford J. Danner |
Before performing any analysis on a dataset, it is often useful to perform a review of the dataset in order to detect missing values and outliers. The macro provided in this paper is a SAS macro program designed to check the data in a time-efficient and user-friendly way, which can generate the following content: First, the macro can generate an EXCEL report to determine whether the variables have missing values or outliers, and report the percentage of missing values. Second, the macro can display all records which contain missing values and/or outliers, grouped by variables in an EXCEL file. Each tab will represent the exception records for one variable. This macro can automatically check all variables regardless of the dataset structure and variable names. Also, an extended version of this macro can perform an automatic check of a whole SAS data library at one time |
missing value, outlier, excel, character variable, proc freq, proc step, macro definition | source | 8 |
| 63 | Developing Resourcefulness in SAS Programmers | Brian K. Varney | As a SAS programmer or developer, you are often ask ed to obtain results by using SAS and related techn ologies in new and innovative ways. As a manager of these reso urces, you often ask yourself, "Why is it Larry alw ays gets the job done with minimal instruction, Moe very quietly never gets the job done and Curly gets the job don e after seemingly hundreds of questions and interactions. O ne difference in these resources is most likely the ir level of resourcefulness | sample, sas programmer | source | 9 |
| 64 | Don't Gamble with Your Output: How to Use Microsoft Formats with ODS | Cynthia L. Zender | Are you frustrated when EXCEL does not use your SAS formats for number cells? Do you lose leading zeros on ZIP codes or ID numbers? Does your character variable turn into a number in EXCEL? Don't gamble with your output! Learn how to use the HTMLSTYLE and TAGATTR style attributes to send Microsoft formats from SAS to EXCEL. This paper provides an overview of how you can use the HTMLSTYLE attribute with HTML-based destinations and the TAGATTR attribute with the TAGSETS.EXCELXP destination to send Microsoft formats from SAS to EXCEL using the Output Delivery System (ODS) STYLE= overrides. Learn how to figure out what Microsoft format to use and how to apply the format appropriately with ODS. A job aid is included in the paper; it lists some of the most common Microsoft formats used for numeric data. The examples in this paper demonstrate PROC PRINT, PROC REPORT, and PROC TABULATE coding techniques |
excel, proc step, ods xml, proc report, ods html, proc print, date variable, format statement, character variable, sysdate9, user-defined format, numeric variable, proc tabulate, xls, sas output, output delivery system, dataset | source | 30 |
| 65 | Effectiveness Of Disease Management Care In The Case Of Heart Failure | Beatrice Ugiliweneza | Objective: The main objective of this study was to evaluate the cost effectiveness of the managed care in the case of heart failure. Also, the proportion of patients with a hospital free year was compared using managed care and non-managed care the non-managed care. Method: A meta-analysis was used to estimate the effectiveness (hospital free years). Cost effectiveness was analyzed in two perspectives: the program's and the payer's. In the program's perspective, the literature was used to estimate the cost. In the payer's perspective, Thomson Reuter's MarketScan data were used to estimate the cost. Results: It was found that, in the program's perspective, a hospital free year's cost was $8,872.60 while in the payer's perspective by using managed care; it saved an average of $53,109.22. The proportion of individuals with a hospital free year in managed care and in the usual care were not found to be statistically significant | proc hpsplit, proc univariate | source | 11 |
| 66 | Efficiencies Realized in Building and Utilizing ADaM from SDTM | Shirish Nalavade, Parag Shiralkar |
Although Study Data Tabulation Model (SDTM) serves the purpose of tabulation and submission, and Analysis Data Model (ADaM) serves the purpose of analysis and review of data, understanding interrelation of these models and utilizing efficiencies by their simultaneous implementation can be extremely beneficial to sponsor. Many initiatives have been taken by industry to implement ADaM when there is already in-built structure of SDTM based data models. SDTM/ADaM pilot project, which was conduc ted by collaborative efforts from industry, CDISC, and FDA, provided lot of guidelines and lessons learnt. In this paper, authors would like to share their experiences of simultaneous implementation of SDTM and ADaM for tabulation, analysis, and reporting of three clinical trials associated with same compound | adam dataset, sdtm domain, clinical trial, cdisc | source | 5 |
| 67 | Ensuring Consistent Data Mapping Across SDTM-based Studies - a Data Warehouse Approach | Annie S. Guo | SDTM is about standardization of clinical trials data. This paper presents a tool that helps ensure consistent data mapping across SDTM-based studies. The tool is comprised of a series of SAS programs. The input to the SAS programs consists of three sources: annotated CRF, SDTM data set specifications, and SDTM SAS data sets. The SAS programs run across each study, and summarize the information from the input files. The output is a set of standardized SAS data sets per study that serve as a data warehouse storing the metadata and data contained in the SDTM data sets. This data warehouse approach allows for direct access and comparison among existing studies, bypassing the original sources, as well as providing a reference database useful for facilitating the programming of new studies | dataset, sample, controlled terminology, label statement, sdtm domain, excel, proc freq, clinical significance, raw data, response variable, data mapping, proc step, cdisc | source | 12 |
| 68 | Epidemiology in Drug Development | Catherine Sigler, Annette Stemhagen |
adverse events, clinical trial, clinical research, phase iv, sample | source | 3 | |
| 69 | Estimating Sample Size through Simulations | Wuchen Zhao, Arthur X. Li |
Determining sample size is one critical and important procedure for designing an experiment. The sample size for most statistical models can be easily calculated by using the POWER procedure. However, the PROC POWER cannot be used for a complicated statistical model. This paper reviews a more generalized method to estimate the sample size through a simulation approach by using SAS software. The simulation approach not only applies to the simple but also to a more complex statistical design |
sample size, sample, null hypothesis, numeric functions, proc power, proc ttest, standard deviation, type i error, type ii error, sas output, interaction effect | source | 6 |
| 70 | Evaluating Safety Signals in Clinical Trials: the Dirichlet-NBD Model Implementation with SAS | Yuqin Li, Xiaohai Wan |
In clinical research, it is often necessary to analyze a group of drug adverse events jointly as well as to analyze them individually. The Dirichlet-NBD model, a statistical model with the combination of both the Negative Binomial Distribution (NBD) and the Dirichlet Multinomial Distribution (DMD), can be applied to analyze adverse events jointly and individually. The model simultaneously fits incidence counts of each adverse event recorded over a period of time. In this paper, we describe an implementation of the Dirichlet-NBD model to analyze drug adverse events data with SAS. First we give a brief introduction to the statistical theories behind the model. Then we describe the SAS macro developed using maximum likelihood method to estimate the parameters for both DMD and NBD. Based on the estimated distributional parameters, the SAS macro computes derived statistics which are useful to study either groups of adverse events or individual adverse event | adverse events, likelihood function, dataset, maximum likelihood estimation, macro definition, proc freq, binomial distribution, clinical trial | source | 7 |
| 71 | Evolution of SDTMIG 3.1.1 to 3.1.2: A mapping specialist must reference on these changes | Rachit Desai, Anirudh Gautam, Vikash Jain |
With the evolution of changes to the SDTM data standards model which incorporated in minor and major changes to the Classes, Domains, Variables, Controlled Terminology and its respective assumptions in SDTMIG 3.1.2 version has played a great deal of efforts for a mapping specialist to understand these evolving changes and their respective principals been implementation in compliance to these data standards and keep themselves updated on this knowledgebase with their ongoing work with in the industry to keep them in par with the competitive global market. This paper will further dive into the details of the new domains incorporated in the current version and also give an overview and details on their structure and its respective implementation with real case scenarios | adverse events, label statement, protocol deviations, controlled terminology, natural key, cdisc | source | 8 |
| 72 | Excel Traffic Lighting and Street Paving Simplified | Steven C. Black | Thanks to several recent additions to the EXCEL xp tagset it is now easy to not only traffic light cells, but also ensure the colors, borders, and dimensions of your EXCEL document are just as you want them, without any post processing. In this paper I will show a simple example of three different methods of traffic lighting, a short template for EXCEL, and a simple method of creating a great looking EXCEL document using the REPORT procedure |
excel, proc step, proc report, proc template, data step, output delivery system, ods excel | source | 8 |
| 73 | ExcelXP on Steroids: Adding Custom Options To The ExcelXP Tagset | Mike Molter | The multitude of options available with ODS's ExcelXP tagset has allowed users access to dozens of EXCEL features when creating spreadsheets from SAS, but not all of them. ExcelXP is a SAS-made tool, but because it is a tagset, users have the ability to modify it. In this paper we'll discuss strategies for adding simple functionality to ExcelXP. Users of all levels will not only see the brief, intuitive tagset code used to produce the required XML for these specific examples, but will also realize the power over their output that this and other TAGSETS give them. Those with more experience with XML and tagset coding will learn a little more about the inner workings of ExcelXP as well as general strategies for adding any functionality to any tagset |
excel, put statement, proc step, xls, set statement, macro parameter, proc template, macro loop | source | 14 |
| 74 | Exploring DATA Step Merges and PROC SQL Joins | Kirk Paul Lafler | Explore the various DATA STEP merge and PROC SQL join processes. This presentation examines the similarities and differences between each, and provides examples of effective coding techniques. Attendees examine the objectives and principles behind merging and joining, as well as the coding constructs associated with inner and outer merges and joins, hash techniques, and selected Set operators |
proc sql, inner join, if-then-else, proc step | source | 11 |
| 75 | FAQ: Issues with Efficacy Analysis of Clinical Trial Data Using SAS | Sandeep Sawant, Nehan Mohan |
In pharmaceutical/CRO industry, role of statistical programmers is to support the stat istical activities of the clinical trial by means of performing safety and/or e fficacy analysis. It is recommended that the statistical programmers should have the knowledge about the statistical methods used especially while working on the efficacy analysis. This however, is not mandatory because detailed specificati ons are provided by the Biostatisticians. Despite of well written specifications, there is a variety of questions from the programmers regarding the analysis or the outputs produced by SAS procedures. There are many situations where SAS doesn't provide the output directly and hence some kind of manipulations is required. This paper is designed to impart a better understanding of efficacy analysis using SAS, as well to communicate the related complexities in the execution and application | proc step, proc freq, odds ratio, efficacy summary, correlation, statistical programmer, normal distribution, proc logistic, proc univariate, box plot, interaction effect, logistic distribution, missing value, binomial distribution, data cleaning, proc genmod | source | 5 |
| 76 | Find / Track / Check and Close, Using SAS to Streamline SDTM Validation Including the Hyperlinks | David Tillery, Qiang Zhai, Lily Peng |
A number of vendor developed systems for SDTM compliance have been available for some time. While coding to identify SDTM validation issues is a straightforward process, methods of reporting, tracking, querying, resolving and closing items remains both challenging and time consuming, especially for certain manual processes. Even by electronically sourcing every CDISC standard element including SDTM domains, controlled terminology, annotations and hyperlink destinations, without a dynamic tracking interface, resolution remains tedious. Using SAS with a little help from some drop-down Acrobat features, this paper will illustrate a three-module approach including basic compliance data issues as well as the prevalent define.xml linking issue. A well designed system eliminates queries to the data, as well as the PDF and XML "click and hope" validation | data cleaning, controlled terminology, cdisc | source | 5 |
| 77 | Formulas Calculating Risk Estimates and Testing for Effect Modification and Confounding | Manojkumar B Agravat | The author is demonstrating formulas for point estimates, effect modification algorithms, and confounding inferences that may have bias and inferences of causality with new formulas, that involves PROC LOGISTIC, PROC IML, and PROC MIXED (SAS TM) algorithms. The death penalty and a sample randomized clinical trial is given for bivariate and multivariate applications of the formulas. The Cochran Mantel Haenszel Test is compared with new Agravat's formulas using novel codes of "R" from the author for strength of association. Logistic regression and Cochran Mantel Haenszel test are compared for confounding. In addition, an algorithm for hyper-geometric distribution is shown for myocardial infarction that did not exist before for effect modification. A new probability formula, hazard ratio, and logits are shown to explain head neck cancer (INHANCE) for one exposure no drinking, along with Agravat series which is hyper-geometric |
proc step, odds ratio, confidence interval, proc iml, logistic distribution, proc logistic, predictor variable, lognormal distribution, weibull distribution, regression analysis, likelihood function, proc freq, proc mixed | source | 44 |
| 78 | From SAP to BDS: The Nuts and Bolts | Nancy Brucken, Paul K. Slagle |
You've just read through the protocol, the analysis plan, and the table shells for the study you were recently assigned to, and have noticed that the primary efficacy analysis is an ANCOVA on change from baseline for one of the vital signs. Furthermore, there are quite a few tables summarizing change from baseline over multiple visits, as well as subjects with abnormal or clinically significant responses. Your manager has informed you that your analysis datasets will need to meet the Analysis Data Model (ADaM) specifications, version 2.1. You know what analysis datasets are, but how do you make them ADaM compliant? This workshop will walk participants to process in creating the Basic Data Structure (BDS) analysis dataset defined in the ADaM v 2.1 model and implementation guide. Participants will create the ADaM based specifications and metadata, based on the tables being created | adam dataset, change from baseline, least squares mean, vital signs, bds, baseline, sdtm domain, modified intent-to-treat, adverse events, concomitant medications, medical history, cdisc | source | 9 |
| 79 | Generate Informative Clinical Laboratory Results Listing | Sai Ma | Clinical laboratory results review is an indispensable, or even crucial, part of clinical trial studies. An informative, neat- looking laboratory results listing can provide insights on important clinical findings or trend identification. The nature of laboratory data (multi-visit, massive size, and often messy) sometimes poses a challenge to obtain such a listing. A method utilizing SAS procedure PROC REPORT is presented in this paper to achieve this |
proc report, proc step, macro variable, sample, %if-%then-%else | source | 5 |
| 80 | Get Dynamic 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 | source | 11 |
| 81 | Good Programming Practices in Healthcare Creating Robust Programs | Gregory S. Barnes Nelson, Jay Zhou |
proc step, error message, sas programming, sas log, missing value, if-then-else, merge statement, proc sql, user-defined format, work library | source | 17 | |
| 82 | Good Versus Better SDTM - Date and Time Variables | Mario Widel, Henry B. Winsor |
The SDTM rules are pretty clear about many things, but there are some areas where guidance is not provided and it is left to the user to implement how they see fit. While there are many implementations that do not violate the rules; there are also implementations that not only meet the rules but also offer extra utility to the end user. In this paper the authors will cover some examples involving the Date and Time variables; showing how making some effort to think about the large picture and design the unspecific parts of your SDTM data and metadata can make your data friendlier and more useful to end users | elapsed time, time variable, cdisc | source | 3 |
| 83 | Helping Students Become Effective Industry Statisticians: Supplementing Science with Data Savvy | Aleksandra Stein, Steven R. Kirby |
In the strictly regulated environment of the pharmaceutical industry, a Contract Research Organization (CRO) provides a wide range of services including, but not limited to, "design of a protocol, selection or monitoring of investigations, evaluation of reports, and preparation of materials to be submitted to the Food and Drug Administration." [FDA 21 CFR 312.3(b)] With regulatory agencies' recent endorsement of CDISC data submission standards, statistical programmers and statistical scientists now need a solid, functional understanding of data to be effective. Academia, however, has not yet embraced a data-focused training of statisticians. This paper examines the emerging discrepancy between the scientific knowledge students are equipped with and the focus on data required to work in the industry. We also share our methods for enhancing communication and easing the transition between academia and industry | pharmaceutical industry, data science, statistical programmer, proc step, clinical data | source | 5 |
| 84 | Implementation of Pairwise Fitting Technique for Analyzing Multivariate Longitudinal Data in SAS | Madan G. Kundu | Mixed models are widely used in the literature for the analys is of single outcome variable measured repeatedly over time. Multivariate longitudinal data arise when a set of different re sponses on the same unit are measured repeatedly over time. A joint modeling of such kind of data is necessary to quantify, fi rstly, the relationship between evolutions of different respons es and, secondly, the evolution of relationsh ip between different response variables over time. An associated problem with joint modeling is as the number of response variable goes up, the convergence issue becomes more and more severe. To resolve this computational complexity, a pairwise fitting approach has been proposed in the literature (Fieuws and Verbeke, 2006, 2007). Purpose of this article is to describe implementation of pairwise fitting approach and estimation of correlations among response variables in SAS | response variable, correlation, covariance, proc mixed, longitudinal data, random intercept, random slope, likelihood function, proc step | source | 12 |
| 85 | Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures | Bohdana Ratitch, Michael O'Kelly |
Methods for dealing with missing data in clinical trials have been receiving increasing attention from the regulators, practitioners and academicians in the pharmaceutical industry over the past years. New guidelines and recommendations place a great emphasis not only on the importance of carefully selecting primary analysis methods based on clearly formulated assumptions regarding the missingness mechanism, but also on the necessity to perform a range of sensitivity analyses that stress-test the results of the primary analysis under different sets of assumptions. There are many methods that could be employed for sensitivity analyses, but some of them have not yet gained a wide-spread usage, partly because of the complex underlying theory, and partly because of lack of relatively easy approaches to their implementation | imputation, proc mi, missing value, proc step, treatment arm, multiple imputation, sensitivity analysis, clinical trial, in=, change from baseline, out=, treatment effect, regression analysis, visit, normal distribution, probability distribution, proc mianalyze, proc mixed, nominal variable, response variable, estimate, pharmaceutical industry, data science, clinical programming, by group processing | source | 10 |
| 86 | Importing and Parsing Comments From a PDF Document With Help From Perl Regular Expressions | Joel Campbell, Ryan Wilkins |
This paper presents a case study of importing and parsing annotations contained in a blank aCRF PDF document for use in creation of the CDISC required define document. The first section details how the PRXMATCH function was used to overcome a critical loss of data that occurred during the import phase which required the use of the SAS XML Mapper and XML LIBNAME Engine. The second section presents an introduction to the Perl regular expression functions that can be used to parse a standardized set of annotations that are ultimately used to provide the CDISC required hyperlinks from metadata definitions to the CRF page where the data originate. This paper assumes that the reader has basic knowledge of the Perl regular expression syntax used in SAS and does not cover the technical aspects of using the SAS XML Mapper or XML LIBNAME Engine |
libname engine, prxmatch, data step, cdisc | source | 5 |
| 87 | Importing Excel 'Data' Into SAS Datasets Without Involving SAS | Stephen Hunt | Everyone's favorite supplimentary data-storage utility isn't actually a database: EXCEL, despite its quirks and eccentricies, has had no serious competition for ease of use and ubiquity across a host of industries. Therefore, the neccessity of importing information from EXCEL into SAS has spawned literally hundreds of papers on the subject. So why do we need yet another? In a nutshell, virtually all solutions for moving EXCEL 'data' into SAS fall along a continuum: The 'minimalist' approach assumes it's best to pull data as pure text and then post-process one's way out of any problems that arise. In contrast, the 'technologist' approach assumes that either the SAS Import facility is available and able to import any EXCEL data via a few mouseclicks, or a SAS map file will be sufficient to import any workbook saved as an XML Worksheet. However, most approaches seem to have assorted drawbacks |
excel, length statement, data cleaning | source | 7 |
| 88 | Incorporating Graphics into Summary Report Tables using ODS and GTL | Qinghua (Kathy) Chen | Graphical representation of treatment effect is often easier to understand and therefore frequently used in conjunction with summary reports in Clinical Study Reports. The roll out of SAS Output Delivery System (ODS) has facilitated the automatic creation of statistical graphics and ODS Graphics Template Language (GTL) when used with a DATA STEP or PROC SGRENDER procedure allows you to design your own layout so you can easily incorporate the statistical graphics into summary reports. The paper will describe how to use ODS Graphs and GTL to incorporate dynamic visualization of data within the body of a summary report table. Topics include: Creating statistical graphs from SAS procedure with ODS Customizing summary reports by incorporating graphics using GTL |
proc template, proc step, macro variable, proc sgplot, box plot | source | 10 |
| 89 | Keeping Patients on Schedule, The Art of Visit Windows and Cycle Slotting | Paul K. Slagle | It would be so much easier if the patients enrolled in a clinical trial would simply follow the visit schedule. The scheduled visits reported at the top of most case report forms would align to the data collected and programming would be so much simpler. Alas, this is reality and nothing could be further from the truth. Generally all clinical trials have to deal with taking "unscheduled" or "off cycle" visits and aligning them with the planned visits in order to make sense of the data collected. This paper covers a few of the different methodologies used to align the data collected with the visit in which it was intended to occur. Typical approaches will include the visit window calculations and cycle slotting. We will also cover how to handle different epochs of treatment. All solutions will be presented using SDTM domains as source and ADaM implementations for the derivations | study day, clinical trial, laboratory data, proc freq, if-then-else, data science, cdisc | source | 4 |
| 90 | Leave Your Bad Code Behind: 50 Ways to Make Your SAS Code Execute More Efficiently | William E. Benjamin Jr. | This laundry list of tips, gathered from over 25 years of SAS programming experience, will show 50 ways to help SAS programmers make their code run faster and more efficiently. General topics will include doing more than one thing in each DATA STEP, combining steps to make simple tasks take less code, using macro variables to simplify maintenance, using built in features rather than writing your own code, ways to save disk space, using sorts for more than just sorting data, and ways to make the program code just read better (code that is easier to read is easier to maintain). The list is broken into categories to allow readers to find something they can use faster. This list can work as a primer for making code changes that will improve the systems and processes to which it is applied. Keywords: Base SAS, Code tips, Macro variables, procedures, conditional code In over 25 years of SAS programming this author has encountered code of all shapes and sizes, both good and bad |
macro variable, data step, txt, out=, proc step, character variable, input statement, dataset, proc sort, %if-%then-%else | source | 20 |
| 91 | Lessons Learned From Managing a New Statistical Programming Group | Yong Zhao | This presentation focuses on lessons learned by a new statistical programming manager, managing a new group in a very challenging environment. Real life examples will be shared and examined to make the presentation more relevant and valuable to the audience. My true personal feeling of being a new manager of less than one year will be scrutinized in order to promote exchanges with experienced and potential managers in the audience. The whole presentation aims at generating discussions on creating positive working environment for programmers as well as the managers. Ultimately, our patients will benefit from our timely delivery of quality products created by statistical programmers | clinical programming, statistical programmer | source | 3 |
| 92 | Let the System Do the Work! Automate Your SAS Code Execution on UNIX and Windows Platforms | Niraj J. Pandya, Vinodh Paida |
Often, same SAS macros are run to produce specific SAS outputs on a regular basis and many times on a strict scheduled time in a production environment. Automating this process can save lots of time and resources. The whole process can be streamlined. This paper explains how to automate the SAS jobs which will execute the required SAS macros on a given specific time point, will also check the log for errors and warnings. If there are any errors, it will send an e-mail with log file; else if the job is successful then it will send the SAS output to the e-mail to appropriate personnel. This article explains about the automation on Microsoft Windows platform using the task scheduler as well as CRONTAB facility on UNIX platform | macro variable, macro definition | source | 7 |
| 93 | Listening to the Voice of the Customer when Deploying Your Application: Using SAS and Design Methodologies to Create a Pleasing User Installation Experience | Joe Perry | We have all had to install software from a simple package like WinZip or something more complex like SAS and these installations create a lasting first impression about the quality of the software vendor's product and the competency of their coding. Work for a large company planning an internal deployment or a small company deploying your software at multiple client sites you have limited opportunities to create a good impression and continue the business relationships you have made. The fact is that whether you like it or not, just like meeting other people first impressions count and the installation and first use of your software is your opportunity to make that impression a good one. This paper explores how to think like a naïve SAS user and not like a programmer while planning your next deployment of your SAS-based software applications. This paper is intended for users of moderate to advanced SAS skills | macro catalog, txt | source | 5 |
| 94 | Macro to Generate Summary and Descriptive Statistics Tables | Yogesh Pande | The most popular type of tables in the phar maceutical industry are thos e that have descriptive stat istics (n mean std median maximum) or tables having frequency (%) count and descriptive statistics of categorical and continuous variables. Often requested by statisticians, medical writers, and clinical research scientists, these tables have to be generated in lesser time spans and with stringent quality concerns. Using SAS macro language and SAS base, this paper will introduce a macro that generates a table having categorical variables (for frequency count) and continuous variables (f or descriptive statistics). For laboratory, vital signs, and ECG data, this macro can generate tables having descriptive statistics for "Change from Baseline." Keywords: Summary, Descriptive, Macro, continuous, categorical | proc means, change from baseline, laboratory data, macro parameter, proc freq, interval variable, vital signs | source | 6 |
| 95 | Macros to Help You Clean Up! | Kavitha Madduri | All programs leave their footprint when executed in SAS. These programs modify the default SAS environment settings - options, formats, library references, titles, footnotes, global macro variables, and temporary datasets. This paper introduces two macros, USTART and UEND, which provide an easy and efficient solution to handle the housecleaning chores for a program and help prevent unexpected results for programs that follow | macro variable, temporary dataset, libname statement, dictionary tables, proc freq | source | 7 |
| 96 | Making a List, Checking it Twice (Part 1): Techniques for Specifying and Validating Analysis Datasets | Elizabeth Li, Linda Collins |
In the CDISC era, biotechnology and pharmaceutical companies are paying increasing attention to how analysis dataset specifications are documented and accuracy of datasets that are generated. It has always been a desirable practice to record the details about analysis datasets, including the structure of the dataset, the source of data variables, the logic of derivations, and methods of special data handling. For FDA submissions that include analysis data model (ADaM) datasets, the analysis data specifications must be included in submission documentation. The use of independent programming is increasingly a gold standard validation method. In this paper, we describe techniques for leveraging analysis data specifications to automate processes in producin g analysis datasets, quality control of the data by independent programming validation, and generating Data Definitions (define.xml) content | adam dataset, data cleaning, independent qc, raw data, attrib statement, excel, macro variable, macro definition, data science, cdisc | source | 8 |
| 97 | Managing the Validation and Migration from SAS 9.13 to 9.2 on a New Server | Carey G. Smoak, Sy J. Truong |
Managing the validation and migration from SAS version 9.1.3 to 9.2 on a new server presents many challenges. In May, 2010, such a project was completed. The manager of such a project should take the following into account: (1) purchasing hardware and software, (2) following company validation standards, (3) regular meetings with IT, Quality and other personnel, (4) the time required to write and execute validation documents, (5) file migration from the old to the new server, (6) start-up of the new SAS system, and (7) hiring personnel with validation experience, if needed. The successful manager should not underestimate the amount of time and resources to accomplish this type of project | data cleaning, pharmaceutical industry | source | 4 |
| 98 | Method to Derive Reproducible SDTM Relationship Datasets | Suwen Li, Sai Ma, Regan Li, Bob Lan |
SDTM has relationship datasets SUPPQUAL, CO, and RELREC that are related to parent domains and records. When those relationship datasets are derived from raw data, attention should be used to ensure that the records in those datasets can be traced back to split parent domains correctly. This paper describes how to derive the relationship datasets SUPP--, CO, and RELREC, and employs examples to illustrate the process | dataset, raw data, sdtm domain, cdisc | source | 9 |
| 99 | Methods of Building Traceability for ADaM Data | Songhui Zhu, Lin Yan |
Traceability is the property that enables the understanding where the analysis data come from. It facilitates transparency and is an essential component for building confidence in analysis results. It is built by recording the path from a record to its predecessor(s) [1]. For simple cases, one may use variables SRCDOM, SRCVAR, and SRCSEQ to build traceability [1]. However, establishing clear traceability for ADaM datasets is not always as simple as this. In fact, sometime, it is challenging. This paper presents methods of building traceability in ADaM datasets by examples of building traceability in ADQS by adding variables RLCRIT and RLFACT when the relation between records in ADaM datasets and their predecessors are complex such as the cases where there are multiple source records, the cases where the multiple source records are not kept in ADaM dataset, and the cases where the sources are nested conditional ones | adam dataset, sdtm domain, cdisc | source | 9 |
| 100 | New Tips and Tricks for Creating a Harmonized, Report-Friendly SDTM and ADaM Lab Data for a Clinical Study Report | Xiangchen (Bob) Cui, Min Chen, Scott E. Moseley |
A clinical study report typically calls for programming efforts to manipulate laboratory data in order to create LB SDTM and ADLB ADaM. There are many challenges that SAS programmers face when working through these tasks. Lab data usually is the largest data set among clinical data sets, in addition to the number of analytes. It could be from different vendors, local and/or central labs. Its values could be numbers or characters. It could have different SI units for the same lab test. Developing a CDISC compliant LB SDTM and ADLB ADaM to facilitate a Clinical Study Report adds more work and more difficulties to the process. This paper shows a new implementation strategy that integrates the requirements of the SDTM LB and ADLB ADaM to facilitate generating tables, graphs, and listings (TFL) by creating a harmonized, report-friendly data flow. It was developed when we were preparing NDA electronic submission | laboratory data, csr, clinical trial, laboratory shift table, sample, laboratory abnormality, shift table, controlled terminology, cdisc | source | 12 |
| 101 | Oncology Trials 101 - The Basics and Then Some | David J. Polus | What makes oncology trials unique? What characteristics do they share with other clinical trials? What does the future hold for oncology research? This paper looks at the oncology therapeutic area under a microscope to determine the answers to these questions and more | clinical trial, adverse events | source | 4 |
| 102 | Perl Regular Expressions: Out of the Oyster, Into Your Code | Eric Larson | Perl Regular Expressions (PRX) are powerful, but frequently unused, functions that can be employed in-line with your code. These functions are typically used to parse data for patterns, but they can also replace multiple uses of other functions [e.g. FIND()]. The purpose of this paper is to demonstrate how Perl Regular Expressions are constructed and deployed within SAS programs, including explanations of the "pattern-matching" code and general examples of "when" and "where" to use PRX functions | character variable, index | source | 7 |
| 103 | Permutation via Recursive SAS Macro | Jian Dai | In this paper, we will demonstrate how to implement a recursive algorithm with SAS macro that generates the permutations of a list of distinguished elements. We will also present two recursive utility macros used to manipulate lists. One of them implements For-Each loop; the other returns the complement of an element in a given list | macro definition, macro variable, %if-%then-%else | source | 5 |
| 104 | Plotting an Error/Line Plot and Bar Graph in a Single Plot with Dual Y-Axis Scales | Sanjiv Ramalingam | In analyzing clinical trial data, it is sometimes ideal to vi sualize the changes in the effica cy parameter with respect to change in dosing over time. This facilitates to observe the relationship between the two parameters and to also provide dosing recommendations, i.e. dose selection and c hoice of dose interval. A bar graph representing the dose and an error bar to represent the change in the efficacy pa rameter would clearly illustrate the relationship between the two parameters over time. The paper combines PROC GPLOT and the ANNOTATE feature to implement the above graph. A very simple and robust method is described to draw the bars and the pattern within every bar in a few steps |
bar chart, proc gplot, proc step | source | 6 |
| 105 | Powerful and "Sometimes" Hard-to-find PROC SQL Features | Kirk Paul Lafler | The SQL Procedure contains many powerful and elegant language features for intermediate and advanced SQL users. This hands-on workshop presents topics that will help SAS users unlock the many hidden features, options, and other hard -to-find gems found in the SQL universe. Topics include CASE logic; the COALESCE function; a sampli ng of summary (statistical) functions; dictionary tables; PROC SQL and macro interface; PROC SQL statement options _METHOD and _TREE; and "key" performance issues |
proc sql, proc step, dictionary tables, where statement, select, macro variable, inner join, sas library, sas log, macro facility, view, create table, group by, macro call, order by, right join | source | 17 |
| 106 | Processing the RefSeq and CCDS Annotation Datasets Using the SAS System: Creation of Gene Reference | Kevin R. Viel, Shannon Grabich |
A source for the human genome data is the UCSC Genome Browser, in particular the RefSeq and CCDS annotation databases, which are indispensible to biomedical researchers. The goal of this paper is to assist SAS programmers in their efforts to obtain a local copy of the reference human genome, the RefSeq file, CCDS file, and to use these to create SAS datasets of reference sequences via SAS data steps. Further, using tools available in BASE SAS, such as perl regular expressions (PRX), allows investigation of the DNA and protein sequences. The creation of gene reference sequence is an integral part of a suite of SAS programs that creates a genomics database. Finally, this paper describes a minor modification of the genome-wide peptide search initially described by Burroughs et alia | macro variable, data step, point= option, set statement, proc sql | source | 9 |
| 107 | Producing Clinical Laboratory Shift Tables From ADaM Data | Rao Bingi | Shift tables are ve ry impo rtant in clinical trial studies. A shift ta ble i s a ta ble that di splays the num ber of subjects who are low, normal or high at baseline and the shift at post-dose. Writing code for a shift table is an easier task, if ADaM datasets are u sed rather than ra w datasets. It represents cross frequencies of baseline values with p ost-dose valu es. The prod uced shift tabl e gives safety information of the su bjects duri ng the clinical study. This paper provides information about producing laboratory shift tables from ADaM datasets in clinical trial studies. The SAS product used in this paper is SAS BASE version 9.2 running on a WINDOWS environment | shift table, adam dataset, clinical trial, proc report, proc step, laboratory shift table, cdisc | source | 12 |
| 108 | Project Automation and Tracking Using SAS | Rajesh Lal | During the life cycle of a clinical trial study/project, there are various tasks that are done manually, ranging from project setup to tracking of project status, and project wind-up. Manual tasks are always time and effort intensive and prone to errors, especially when the number of programs and outputs to be created in the project is large and timelines are tight. By automation of some of these tasks, timely production and validation of standardized programs and outputs can be ensured. This paper describes the design of a utility that is implemented in SAS, automates the project startup related activities, keeps track of validation comparison status, status from validation documents, re-validation requirements, version control status, SAS log check status, program readability status, overall validation status and performs project wind-up activities | data cleaning, proc step, sas log, excel, proc compare, dataset, macro variable, sysrc, sample, clinical trial, comment, macro definition | source | 12 |
| 109 | Protecting Macros and Macro Variables: It Is All About Control | Eric T. Sun, Arthur L. Carpenter |
In a regulated environment it is crucially important that we are able to control, which version of a macro, and which version of a macro variable, is being used at any given time. As crucial as this is, in the SAS macro language this is not something that is easily accomplished. We can write an application that calls a macro that we have validated, but the user of the application can write and force the use their user written un-validated version of that same macro. Values of macro variables that we have populated can be 'accidentally' replaced by user written assignments. How can you guarantee that the end results are accurate if you cannot guarantee that the proper programs have been executed? Although our tools are limited, there are a few options available that can be used to help us control our macro execution environment. These, along with management programs, can give the application developer better control, and greater protection, during the execution of the application | macro variable, stored macro, symbol table, macro definition, options statement, autocall macro, local symbol table, proc sql, global symbol table, macro facility, call symputx, proc step, catalog, macro call, proc printto, sas programming | source | 11 |
| 110 | Quick - Ready Set Retain, and Maybe Reset! | Lisa Fine | The RETAIN statement is one method that SAS programmers commonly use for making comparisons across observations. One source of misunderstanding around the RETAIN statement centers around how long a value is retained and the ability or need to reset retained variables in many circumstances. This paper clarifies, through examples, how the RETAIN statement overrides the default behavior of a DATA STEP and maintains a variable's values until the value is reset. The scope of the paper is to demonstrate the use of the RETAIN statement in conjunction with assignment statements |
retain, dataset, variable | source | 8 |
| 111 | Reading Title and Footnote from RTF Output into SAS utilizing Microsoft Excel | Ajay Gupta | Capturing titles and footnotes from Microsoft Word readable RTF documents into SAS datasets for the quality control/validation purposes has always been a tedious task for SAS programmers. Also, manually typing titles and footnotes from RTF documents into various places in SAS programs is clearly time consuming and prone to typing errors. In addition, SAS does not provide a built-in procedure to import titles and footnotes directly from a Microsoft Word (RTF formatted) document into SAS, but SAS does have built-in procedures for reading data from Microsoft EXCEL. In this paper a SAS macro %Read_Title_Footnote is introduced to read titles and footnotes from the Microsoft word readable documents using EXCEL/Word Basic commands via DDE |
excel, dataset, macro definition, filename statement, proc import, macro call, sas programmer, proc step | source | 6 |
| 112 | Resolving OpenCDISC Error Messages Using SAS | Virginia Redner, John R. Gerlach |
The Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) defines the required standard for the submission of human clinical trial data to a regulatory agency. OpenCDISC Validator is a free, open-source tool that is embraced by many pharmaceutical and biotechnology companies and used to assist in validating clinical data in accordance with the ever-evolving CDISC standards. Although OpenCDISC does a thorough job of validating CDISC domains, the application produces error messages that can be difficult to understand, let alone resolve. Interpreting the output and determining the necessary course of action is not always straightforward. Certainly, creating and validating clinical data requires a multi-faceted approach that includes having knowledge of the CDISC standard, experience with Base SAS, as well as an understanding of clinical data. OpenCDISC introduces another factor to that endeavor | data cleaning, clinical data, adverse events, missing value, proc sql, error message, proc freq, proc step, sdtm domain, serious adverse event, create table, clinical trial, cdisc | source | 10 |
| 113 | Run your reports through that last loop to standardize the presentation attributes | Niraj J. Pandya | Post Processing of the report could be considered as the last step of generating listings and analysis reports of clinical data reporting in pharmaceutical industry and is the essential aspect of better presentable reports. In general, these macros developed in SAS software are very flexible for repeated use and can work with varying needs of report presentation. Post processor macros generally take care of different formatting, display, and other presentation attributes associated with the report. There are various methods to generate and invoke the post processor macros. This paper discusses such methods to create, and effectively use the post processor macros in variety of reporting needs. The approaches suggested here include usage of global macro variables by resolving such variables in the post processor macro and development of post processor macro based on the report presentation needs | macro variable, proc step, proc report, proc sql, clinical data, data step, macro definition, clinical trial, character variable | source | 6 |
| 114 | SAS Abbreviations are your friends, use the template method to code! | Elizabeth Ceranowski | Often, coders find themselves using the same procedures or sequence of procedures over and over again. It would be very useful to create a "shell" of a procedure or program that can be used to cut down on repetitive typing. (For example, to submit the generic code "PROC PRINT;run;"). This quick demonstration will show how to create/edit/use SAS Abbreviations to quickly interject code into the Enhanced Editor window in SAS for Windows |
proc step, proc report, tranwrd function | source | 6 |
| 115 | SAS Drug Development Program Portability | Ben E. Bocchicchio, Nancy Cole |
A Roadmap showing how SAS code developed outside of the SAS Drug Development system can be used with the Process Editor (within SAS Drug Development) and how code developed within SAS Drug Development can be utilized outside of the application. In an era where going green and recycling are part of everyday life, can SAS code also adhere to t hese principles in terms of recycling and reuse? If so, how can you take advantage of existing SAS code (recycle it) and make it run in SAS Drug Development's Process Editor? How can this be accomplished with a minimum amount of code changes? In an equal ly import design decision, if the SAS code generated from the SAS Drug Development system is a required deliverable, how can the code be designed to be able to run outside of SAS Drug Development? These are some of the many questions that face programm ers using SAS Drug Development, an integrated 21CFR Part 11 compliant system for managing, analyzing, reporting and reviewing clinical research info | clinical research, libname statement, macro variable, comment, macro definition | source | 9 |
| 116 | SAS Users Can Command Microsoft Excel to Automatically Create Graphs From SAS ExcelXP Output | William E. Benjamin Jr. | With everything becoming more "Security Aware", hearing that Microsoft EXCEL 2007 can ignore DDE is no surprise. The SAS ODS Tagset ExcelXP creates .xml output, and .xml output cannot contain graphs. Nowadays, no one wants EXCEL spreadsheets with any kind of macros in the workbook. Most default security settings will not open "Macro Infected" workbooks (without an authorized Digital Signature - just try to get one of those) |
excel, xls, proc print, proc step | source | 19 |
| 117 | SAS/GRAPH Elements You Should Know - Even If You Don't Use SAS/GRAPH | Arthur L. Carpenter | We no longer live or work in a line printer - green bar paper environment. Indeed many of today's programmers do not even know what a line printer is or what green bar paper looks like. Our work environment expects reports which utilize various fonts, with control over point size and color, and the inclusion of graphic elements. In general we are expected to produce output that not only conveys the necessary information, but also looks attractive. In the line printer days little could be done with the style and appearance of reports, and consequently little was expected. Now a great deal of control is possible, and we are expected to take advantage of the tools available to us. We can no longer sit back and present a plain vanilla report. The Output Delivery System, ODS, gives us a great deal of the kind of control that we must have in order to produce the kinds of reports and tables that are expected of us |
title statement, footnote statement, proc step, box plot, regression analysis | source | 15 |
| 118 | Scatter Charts of Serial Observations with Proc SGPLOT and Graphics Template Language | Anthony L. Feliu | The new statistical graphics procedures and underlying Graphics Template Language (GTL) in SAS 9.2 offer programmers the opportunity to create sophisticated, publication-ready charts using less code than with the traditional graphics procedures. This paper selects one chart common to clinical trials-scatter chart to trend observational data-and presents a step-by-step analysis of the code to create this output, including how sample counts can be presented below the chart image | proc step, proc sgplot, sample, output delivery system, proc template, scatter plot, graph template language, standard deviation, series plot | source | 20 |
| 119 | SDTM, ADaM and define.xml with OpenCDISC | Matthew J. Becker, Angela S. Ringelberg |
Standards are an ongoing focus of the health care and life science industry. Common terms you will see and hear during industry conferences include: "SDTM," "ADaM," "ODM," "LAB," "SEND," and "define(.xml/.pdf)." What do these terms mean? How do we create and validate these standards before submission to a client OR the FDA? Is there an easier way to ensure compliance? As a user, many of us have spent hours reading the SDTM/ADaM standards and implementation guides to generate "compliant" SAS data sets for our clinical studies. We have spent countless hours having another user QC our data structures...but is there an easier way? Each of these terms and subsequent standards encompass a set of metadata. Metadata is "data about data." For example, a PROC CONTENTS of a data set produces a list of variables, variable type, variable length, variable format, etc. Each of these tidbits of information is metadata about that individual data set |
data cleaning, excel, length statement, ods xml, cdisc | source | 13 |
| 120 | Semi log plots - Getting the axis tick mark labels in expanded Log10 scale | Nehan Mohan | In Semi-log plots, one axis is plotted on a logarithmic scale while the other on a linear scale (here the actual data is plotted on a log scale and not the log transformed data) - the advantage being t hat it can bring out features in the data that would not easily be seen if both the axes had been pl otted linearly. When such a semi-log plot is required to be created in SAS using Log10 scale, we usually use the LOGBASE and LOGSTYLE options in the axis statement. If the data points are either too small or too large, the resultant log axis that SAS generates is sometimes presented in exponential form having tick mark labels such as '1.00E-02', '1.00E-04', et c. However, if the display is required to be in the expanded form instead i.e. tick ma rk labels such as '0.001', '0.00001', etc , the user has to then define these labels in the AXIS statement by means of ORDER and/or VALUE options | macro variable, clinical trial | source | 5 |
| 121 | Similarities and Differences in SAS Programming Among CRO and Pharmaceutical Industries | Sandra Minjoe, Mark Matthews |
SAS programming in the clinical environment has a wide range of opportunities across a breadth of exposure of the entire clinical drug development cycle. Whether you are employed by a Contract Research Organization, Pharmaceutical or Biotechnology company, or as a contractor, the programming tasks are often quite similar, and at times the work cannot be differentiated by your employer. However, the higher level strategies and the direction any organization takes as an enterprise can be an important factor in the fulfillment of a SAS programmer's career. We would like to share our experiences around the differences and similarities of interests and goals that we have experienced in our careers. The authors have a total of almost 30 years of experience within the industry with a combination of large pharmaceutical/biotech and contract research organization perspectives, from a SAS technical role and at a manager capacity | sas programmer, sas programming, clinical research, job security, adverse events | source | 6 |
| 122 | Smart Import/Append Data in Excel Sheets | Zhengping Ma, Liping Liu |
It is common that we have data stored in various forms in clinical research. One commonly used format is Microsoft EXCEL spreadsheet. However, data analysis is often conducted through SAS software. There are many SAS procedures, such as PROC IMPORT, PROC DOWNLOAD, etc, which can be used to load EXCEL files into SAS. Another option is to use DDE (Dynamic Data Exchange), especially to load EXCEL files with multiple sheets. We can also use SAS9 LIBNAME Engine to get sheet names dynamically. In several projects we worked on, periodic data transfers (e.g. weekly, monthly, etc) were scheduled so that EXCEL files accumulated over time. In other circumstances, similar data files accumulated over multiple studies. There is a need for automating the loading of source data files and combining them either into study specific datasets or into an IDB across several studies |
dataset, excel, proc step, proc append, proc import, character variable, macro definition, sample, libname engine, macro loop | source | 8 |
| 123 | Some Useful Techniques of Proc Format | Stan Li | SAS Format is a very unique and powerful function. It provides system built-in standard formats and the capability of allowing users to define their own formats. These formats are often used for data input and data output. In addition, formats can also be used for SAS dataset extraction and dataset merging. It would save significant computing resources by employing format function in very large dataset sub-setting or merging. This paper will present the techniques of creating a format, listing/viewing the contents of a format, nesting formats and multi-label formats. We will also illustrate the methods of using formats for large dataset extraction and merging. Keyword: PROC FORMAT, Very large dataset |
dataset, proc step, proc format, format statement, sample | source | 6 |
| 124 | Statistical Analysis of Adverse Events in Randomized Clinical Trials Using SAS | Dongsun Cao, Xiaomin He |
Adverse events are generally tabulated and listed in statistical reports in randomized clinical trials. This paper introduces how to apply statistical methods to assess adverse events using SAS. In addition to the crude percentage (rate), adverse events are also adjusted by exposure time or multiple repeat. The paper also introduces the commonly used chi-square test and Fisher's exact test, and other statistical methods such as Cochran-Armitage trend test, nonparametric approach and generalized linear mixed model. The application of statistical methods depends on study designs, data type and investigation purpose. Several clinical trial examples such as dose-response studies, cross-over trials, are analyzed using SAS for illustration purposes | chi-square test, clinical trial, adverse events, exact test, sample, sample size, proc freq, null hypothesis, data science, fisher exact test, proc mixed, confidence interval, normal distribution, proc glimmix, proc phreg, treatment effect | source | 12 |
| 125 | Success As a Pharmaceutical Statistical Programmer | Sandra Minjoe, Mario Widel |
Why do some people EXCEL in their careers? This is a difficult question to answer and one that is especially puzzling when you are just starting out or aren't doing as well as you expected. The authors of this paper have been in the industry for a combination of more than 30 years, and have observed many successful and not-so-successful pharmaceutical statistical programmers. This paper is a compilation of our experiences and expresses our opinions of the skills needed and best suited for the role of pharmaceutical statistical programmer. It can help you identify your skill gaps and describe what you can do about them |
statistical programmer, pharmaceutical industry, pharma programmer, clinical programming, data science, survival analysis, regression analysis | source | 7 |
| 126 | Symbol Table Generator (New and Improved) | James R. Johnson | In Seattle at the PharmaSUG 2000 meeting the Symbol Table Generator was first presented. The original program was simple, and so was the output: a line numbered listing of selected program followed by a symbol table which is a cross reference of all the variables and keywords used in the program. In the New and Improved version comment limitations have been overcome and the strengths of PROC REPORT have been employed. The line numbered listing is much more intelligent and can provide up to three different types of program nesting. Other new features include optional suppression of common keywords from the symbol table, tab processing, simple error checking, and even suppressing the symbol table altogether |
symbol table, select, macro loop, macro definition, proc step, proc sql, sas base, end, proc template, footnote statement, keyword parameter, %macro parameters | source | 14 |
| 127 | SYMply PUT: GET the most out of SYMPUTX and SYMGETN | Robert Howard | SYMPUTX and SYMGETN are simple, yet extremely powerful and useful functions. The CALL SYMPUTX routine allows you to quickly and easily store dataset values into macro variables, while the SYMGETN function retrieves these values. Using these functions saves time and reduces the chance of data calculation errors in even the most complex programs. By working through a practical example, you will see the utility and flexibility of these indispensable functions | macro variable, call symputx, character variable, call symput, numeric variable, macro put | source | 4 |
| 128 | The Illusion of Good Data, or How Not to Think of a Blue Elephant | Kim Truett | When someone says, "Do not think of a blue elephant," what is the first thing that you think of? A blue elephant. The suggestion that has been put into your mind makes it impossible not to think of blue elephants. The same collection bias will occur with improperly designed CRF questions. 'Real time' edit checks can keep sites from entering 'wrong data' during EDC data entry and therefore, reduces queries, but improper use of edit checks can bias data collection. This paper will present common ways that data collection can create bias, for both paper-based and EDC studies, and will illustrate methods to minimize and avoid data biases that can be created during data collection and will present some ideas for using SAS software post-entry to look for some of these biases | medical history, clinical trial | source | 6 |
| 129 | The Many Ways to Effectively Utilize Array Processing | Arthur X. Li | Utilizing array processing allows us to reduce the amount of coding in the DATA STEP. In addition to learning how to create one- and multi-dimensional arrays, this paper will review how to create an explicit loop in the DATA STEP - the prerequisite of constructing an array. You will also be exposed to what happens in the Program Data Vector (PDV) during array processing. A wide range of applications in using loop structures with array processing, such as recoding missing values for a list of variables, transforming datasets, etc., will be covered in this paper |
do loop, array, execution phase, output, in=, out=, missing value, dataset, compilation phase, array bounds, temporary array, automatic variable, implicit loop, set statement, class variable | source | 14 |
| 130 | The Path, The Whole Path, And Nothing But the Path, So Help Me Windows | Arthur L. Carpenter | Have you ever needed to determine a directory path dynamically? Have you needed to know the name of the currently executing program and where it resides? There are a number of tools available to the SAS programmer that can be used to determine path and location information. These tools range from common to obscure and from simple to complex and they utilize SAS Language functions, System Options, SASHELP views, DICTIONARY tables, and OS Environmental Variables. This presentation will take a survey of a number of these techniques for finding and determining path and location information. This information can be important for all levels of programmers, and you should be aware of these techniques even if you think that you will never need them | proc sql, dictionary tables, macro variable, macro put, options statement, macro definition | source | 8 |
| 131 | The Perfect Marriage: The SAS Output Delivery System (ODS) and Microsoft Office | Chevell T. Parker | A compatible relationship is one in which the associated entities complement each other's strengths and compensate for each other's weaknesses. This relationship is certainly the case when you combine the power of ODS and the flexibility of Microsoft Office. This paper describes how you can create outstanding spreadsheets via the harmonious union of ODS with Microsoft Office applications. The paper explores the relationships among ODS destinations (for example, ExcelXP and MSOffice2K) as well as custom TAGSETS (for example, the TableEditor tagset). In addition, the paper explains how these components enable you to generate highly presentational spreadsheets |
excel, output delivery system, sample, proc step, proc template, mod, ods tagsets.excelxp, proc report | source | 22 |
| 132 | The Standard for the Exchange of Nonclinical Data (SEND): History and Basics | Fred Wood, Lou Ann Kramer |
The CDISC SEND model is an implementation of the Study Data Tabulation Model (SDTM) for the submission of nonclinical studies. The SEND Model was first developed in 2002, utiliz ing domains described in the CDER 1999 Guidance and the SDTM as its basis. The first pilot with the FDA was conducted in 2003, but the real stimulus that gained the model widespread recognition was the announcement of an FDA pilot project in 200 7. Since that time, more than forty volunteers representing pharmaceutical companies, vendors, and CROs have created close to thirty domains for general toxicology and pharmacology, carcinogenicity, and reproductive toxicology studies. This paper will provide an overview of the history of SEND and its close ties with the development of the SDTM and the SDTMIG. It will also cover some of the basics of the SEND model and how the nonclinical implementation is different from the clinical implementation | clinical data, clinical trial, controlled terminology, sample, cdisc | source | 9 |
| 133 | Tips and Tricks for Clinical Graphs using ODS Graphics | Sanjay N. Matange | Did you know that you can create an adverse event graph using a vector plot? Or, that you can label dosage levels for a medications plot using a scatter plot? How do you place a reference line between two values on a category axis? S tatistical graphics (SG) procedures and the graph template language (GTL) provide you myriad ways to mix and match statements to create graphs. What you can achieve is based on creative usage of the statements. This presentation includes tips and tricks you can use in SG procedures and GTL programs to build your graphs. We use examples from clinical trials and health and life sciences domains to illustrate the techniques using real - world graphs like LFT panels, patient profiles, adverse event plots, and more. Most examples use the second maintenance release of SAS 9.2, but this presentation also includes a sneak preview of some powerful new features to be released with SAS 9.3 | scatter plot, box plot, proc sgplot, adverse events, forest plot | source | 16 |
| 134 | Tracking Metadata within SAS Drug Development Using SDDPARMS | Bradford J. Danner, Matthew J. Wiedel, Katrina E. Canonizado |
An increasingly prevalent request from our sponsors is to provide cross-referenced documentation associated with all datasets, programs, macros, and outputs contained in an electronic submission. To accommodate these requests, without modifying our standard processes too much, we found a valuable tool in the SDDPARMS dataset, an optional SAS file capable of being produced with all programs in SAS Drug Development. We developed an easy-to-use, non-invasive macro to compile and update metadata, required for the documentation package, as a SAS data table. The macro we propose requires limited updates from users and utilizes common Base SAS procedures, macro processing, and the DATA STEP with the Update statement. While permanent SAS datasets are not updated instantly when programs are submitted in batch as Jobs in SAS DD, the method we propose works similarly when submitted from both the Process and Job editors |
dataset, clinical research, sas base, adam dataset, proc sql, cdisc | source | 7 |
| 135 | Trials and Tribulations of SDTM Trial Design | Fred Wood, Mary Lenzen |
An increasing number of sponsors are submitting clinical tria ls data to the FDA in the format of the CDISC Study Data Tabulation Model (SDTM). The Trial Design Model (TDM) datasets can be especially challenging because, in most cases, they are being created retrospectively from the protocol, and cannot be created from electronic data. This presentation will discuss some of the co nsiderations and challenges in creating the TDM datasets, using case studies of both relatively simple and more-complex trials. Included w ill be practical advice on which datasets should be created first, which datasets drive the creatio n of others, and how the trial-level and subj ect-level datasets relate to each other. The presentation will conclude with a list of resources for TDM dataset creation | clinical trial, subject visits, treatment period, controlled terminology, phase i, trial summary, cdisc | source | 5 |
| 136 | Truncation, Variable Association, Controlled Terminology, and Some Other Pitfalls in the SDTM Mapping Process | Na Li, Gary de Jesus, Daniel Bonzo |
This paper discusses a class of likely pitfalls during the SDTM mapping process. Problems in truncation, variable association, controlled terminology, and mapping SUPPQUAL usually occur when SDTM mapping proceeds from raw to SDTM and then using SDTM to generate ADaM. In this pathway, understanding data standards and data capture and reporting instruments from CRF to TLFs (Tables, Listings and Figures) is critical to mitigating potential errors that are embedded in the mapping process. Some collective experience in identifying and preventing these pitfalls will be shared | raw data, sdtm domain, controlled terminology, adam dataset, protocol deviations, sdtm programming, concomitant medications, proc freq, cdisc | source | 8 |
| 137 | Type Less, Do More: Have SAS do the typing for you | Jeanina M. Worden | Typing less when you're a SAS programmer seems counterintuitive; however when repetitive tasks leave you with the realization only five words differentiate the last twenty lines of code the concept becomes clearer. There are numerous ways to accomplish these tasks, such as "hardcoding" and copy-and-paste, however they carry with them increased risk in terms of additional time required for updates, and the lack of assurance all constraints are accounted for. This paper provides three examples that use SAS tools to dynamically code repetitive tasks, with less maintenance and reduced risk of omission, and doing it all with less coding | macro variable, proc step, proc sql, call symput | source | 4 |
| 138 | Using a HASH Table to Reference Variables in an Array by Name | John Henry King | Array elements are referenced by their index value using a constant, e.g. a[1], a[2], or with a variable whose value contains the index a[i] where i=1,2. This works well enough for most applications but there are situations where you need to reference an array element but you only know the name of the variable not the array index. In a situation like that a HASH table can be used to provide a lookup table for the array index using the variable name as a key . An example will be shown where this technique is applied to an ODS OUTPUT data set created by PROC FREQ, to normalize the table variables. Another example will show that many CSV files with a varying number of fields and with the fields in any order can be read in a single DATA STEP |
proc freq, proc step, data step, hash table, infile statement, numeric variable | source | 9 |
| 139 | Using LinkedIn and sasCommunity.org as Human Resource Tools for Managing a Network of SAS Professionals | Kirk Paul Lafler, Charles E. Shipp |
Social networking and collaborative online communities are enormously popular web applications. With this popularity a growing number of SAS users are connecting and sharing content with others around the world, anytime and anywhere. Organizations, understanding the huge potential these online communities offer their human resource needs, are using LinkedIn and sasCommunity.org to identify and manage a professional network of SAS professionals and experts. This presentation explores the techniques used by organizations to leverage their human resource needs | source | 14 | |
| 140 | Using SAS for Modeling and Simulation in Drug Development - A Review and Assessment of Some Available Tools | Melvin S. Munsaka, Michael Carniello |
It is widely recognized that modeling and simulation is a valuable tool that can be used to help understand various considerations in clinical trials, such as the evaluation of merits of different designs and potential efficacy and safety outcome scenarios. Together with available scientific data, this can be used to help assess and evaluate potential risks and outcomes of trials before making huge investments. Simulation and modeling can also help provide guidance in decision making by quantifying and evaluating potential choices such as the go-no-go decision, in the face of a multitude of uncertainties. Modeling and simulation can in addition be used to help define, test, compare, and optimize study designs, including specific complex design scenarios that are useful for facilitating a better understanding of study outcomes. In this survey, we review and assess the use of SAS as a tool in clinical trial modeling and simulation in drug development | clinical trial, proc step, sample size, macro definition, sample, clinical research, monte carlo, missing value, proc iml, confidence interval, proc calis, data science, proc mi, longitudinal data, sas function, markov chain, estimate, regression analysis, group sequential design, anova, bootstrap, imputation, proc mixed, proc model, adverse events, clinical data, phase i, proc genmod | source | 10 |
| 141 | Using SAS Predictive Modeling to Investigate the Asthma's Patient Future Hospitalization Risk | Yehia H. Khalil | The focus of this study is to develop predictive models to forecast future Asthma hospitalizations for patients diagnosed with Asthma. Using SAS Software tools such as predictive models provides great benefit to insurance companies, hospitals, and pharmaceutical companies tryi ng to manage their resources and define future plans and policies. The strength areas of predictive modeling are: a) an ability to incorporate any type of variable into the analysis, b) Dynamic, as they can easily accommodate any information as they become available to adjust the model accordingly. A recent study shows that around 21 million Americans are diagnosed with asthma, which represents a large section of the insurance and pharmaceutical patient p opulation. Asthma patients are classified into two groups: Controlled vs | neural network, sample, regression analysis | source | 7 |
| 142 | Using Six Sigma Methodologies to Find a Solution for Increasing Training Completions | Eunice Ndungu, Shazia Khawaja, Janet Low, Steve Miola |
All company strategies require training at some point. Failure to ensure that training is completed may jeopardize the successful execution of those strategies. The goal of this paper is to share a practical application of six sigma principles. It will discuss how the use of six sigma methodologies resulted in a high impact low cost approach to increasing training completion rates to at least 95% | sample, problem solving | source | 22 |
| 143 | Using the ADaM Basic Data Structure for Survival Analysis | Nancy Brucken, Sandra Minjoe, Mario Widel |
The Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) team has described a Basic Data Structure (BDS) that can be used for most analyses. This tutorial will walk through how to develop a BDS dataset for survival analysis. Attendees should be familiar with survival analyses and related SAS procedures, such as PROC LIFETEST. During the tutorial, attendees will complete by hand some variable-level metadata for an example set of survival analyses. Attendees may find it useful to bring to the tutorial session a copy of the ADaM Implementation Guide (IG), available free from the CDISC website1 for download |
survival analysis, adam dataset, sdtm domain, adamig, randomization, adverse events, cdisc | source | 5 |
| 144 | Using the SAS Clinical Standards Toolkit for define.xml creation | 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 (CRT -DDS, also known as define.xml) format as prepared by the CDISC define.xml team. This workshop will provide an introduction to the structure and content of the define.xml file. The SAS Clinical Standards Toolkit will then be used to create the define.xml file | data cleaning, annotated crf, dataset, macro variable, cdisc | source | 20 |
| 145 | Using Two SET Statements in One DATA Step | Ben Cochran | It has been said that the DATA STEP in Base SAS Software is the most powerful data manipulator in the business. The power and flexibility of the DATA STEP can be enhanced by using two SET statements instead of one MERGE, or UPDATE statement. This Coder's Corner presentation shows a number of examples of when and why you may want to consider doing this. Let us begin. Example 1: Illustrate how two SET statements work in one DATA STEP by using a simple example. Each SET statement executes once for eac h iteration of the DA TA step. Every time each SET statements executes, values are read into the Program Data Vect or (PDV). But, the resulting dataset ( _2SETS ) only has two observations. Why? What stops the exec ution of a DATA STEP? The answer is 'when a reads operation fails'. In this case, when the second SET st atement hits the end-of-file (eof) marker, the DATA STEP stops immediately. This happen s in the third iteration of the DATA STEP |
set statement, conditional do loop, proc print, proc step | source | 5 |
| 146 | Using Visual Basic for Application to Produce Table of Contents from SAS Output List Files | Zemin Zeng, Mei Li, Meng Pan |
The paper is a continuation and extens ion of our earlier work on applying Visual Basic for Application (VBA) procedures in the Microsoft Word to manipulate SAS out put Table/Listing (TL) files [Zeng & Li, SAS Global Forum 2010]. In this paper, we shall explore further to show how to assemble SAS list files into a single file by the order of their table/listing numbers, and produce Table of Contents (TOC) from SAS output Table/Listing (TL) files. All these manipulation tasks on SAS list files shall be accomplished by c licking buttons to execute VBA macros. The testing of the VBA macros was verified in a Windows Operating System PC with Microsoft Office 2003 or 2007 | sas output, sample, clinical trial, dataset | source | 5 |
| 147 | Validating Controlled Terminology in SDTM Domains | John R. Gerlach | Creating and validating SDTM domain data sets have become rather perfunctory tasks involving regulatory submissions. Unfortunately, both tasks (creating and validating), especially the latter, often lack a comprehensive method concerning Controlled Terminology, which is an important extension of the SDTM standard. The lack of using Controlled Terminology or the absence of validating appropriate values undermines the content and compliance of SDTM domains, and thereby jeopardizes the submission. Thus, a validation tool should include a mechanism that ensures the proper use of Controlled Terminology, which affects almost all SDTM domains, including the SUPPQUAL and RELREC domains. This paper discusses an efficient and dynamic SAS solution that validates Controlled Terminology in SDTM domains | data cleaning, controlled terminology, sdtm domain, proc sql, proc freq, adverse events, macro variable, missing value, vital signs, serious adverse event, proc step, trial summary, macro loop, cdisc | source | 12 |
| 148 | Validating define.xml: Tools, trials, and tribulations | Sandra VanPelt Nguyen | The define.xml file should serve as a guide to the data which has been submitted for a clinical trial. For those who are unfamiliar with the trial and the data that was collected, documentation is critical to understanding, reviewing, and using that data. Without a proper roadmap, it is easy to get lost. Multiple types of quality control and validation steps are needed to ensure that the define.xml file is complete, accurate, functional, and consistent with the data it is supposed to represent and describe while additionally meeting the requirements and guidelines established in the Case Report Tabulation Data Definition Specification1 (CRT-DDS, also known as define.xml). Last year, CDISC released a white paper on XML schema validation for define.xml 2. i3 Statprobe used this paper as a starting point to develop a validation process for define.xml | data cleaning, quality control, cdisc | source | 5 |
| 149 | Visualizing Healthcare Provider Network using SAS Tools | John Zheng | Healthcare provider network or patient-provider network is one kind of affiliation networks. Analyzing such networks allows us to gain additional insights on healthcare provider groups that share patients and patients that belong to the same group. This paper develops methods to visualize provider network data in healthcare using the Annotate facility. Link analysis is used to explore the connections between healthcare providers and patients, and hence connections between peer providers. In addition, this paper also takes advantage of the spatial information in the patient-provider data to gain additional insights. In particular, the connections between providers are projected on a Google map using Google Map Generator | proc step, network data, proc mds, proc sql | source | 9 |
| 150 | You Want that Program to Run on the PC in New Jersey and the Unix in Basel, without Modification?? | David W. Franklin | With offices in many parts of the world, SAS programmers are being asked more and more to write SAS programs in one environment to run on many others, including different versions of SAS and different operating systems. The underlying architecture of SAS allows SAS programmers to do this easily, with a little bit of planning. This paper introduces a few techniques and uses a number of SAS environment variables, including SYSSCPL, SYSSITE and SYSVER, to make that program run in multiple environments with no modification | macro variable, libname statement, proc sort | source | 6 |
| 151 | Your Age In People Years: Not All Formulas Are the Same | Arthur L. Carpenter | The calculation of an individual's age can be problematic. SAS dates are generally measured in terms of days (or seconds if a DATETIME value is used), so we have to convert the value to years. To some extent, how we calculate age will depend on how we intend to use the value. The society's concept of 'age' is different than the mathematical concept. Age in years is further complicated by the very definition of a year as one rotation of the earth around the sun. This period does not convert to an integer number of days, and it is therefore further complicated by leap years. Since SAS does not contain an AGE function, we need a formula for the conversion. And there are several to choose from. Which should we use? |
interval variable, datepart | source | 5 |
| 152 | Your Dataset Looks Fine - But Does It Comply with '99? | Meenal (Mona) Sinha | This paper highlights the role of best practices in Clinical Programming as it relates to regulatory compliant submission of New Drug Applications to the Food and Drug Administration. FDA Form 356h lists 20 Items to be included as part of this application. A Guidance document published by the FDA in 1999 gives detailed requirements for electronic submission of Form 356h. Item 11 on this list is, "Case Report Tabulations (CRTs)". CRTs are the main work products produced by the Clinical Programming teams in the form of datasets. More recent Guidance from the FDA address eCTD formats and Electronic Signatures on electronic submissions, but the 1999 Guidance on Item 11 remain current. This paper discusses suggested best practices for Clinical Programming to produce '99-Compliant datasets | clinical programming, concomitant medications | source | 3 |
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