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BUGSPharmaSUG 2003 Papers - Index
115 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %RTFTable - A Powerful SAS Tool to Produce Rich Text Format Tables | Eric Qi, Liping Zhang |
This paper describes an approach to generating RTF tables through a SAS macro, %RTFTable. This is a powerful SAS tool to convert SAS data sets into highly customized tables in rich text (RTF) or Microsoft Word (DOC) format. The macro gives users full control over the table appearance: e.g., defining table titles, footnotes, and headers; merging cells in the table header; customizing the data format for each column; adjusting column width and alignment; controlling text appearance (bold, italic, underline or plain text) in the table body; and more. This paper provides an overview of the unique macro design, describes the macro syntax and parameters, highlights some of the nice features and gives an example of a macro call. %RTFTable can be used on any platform to produce RTF tables. The macro is easy to use and rich in functionality | macro call, dataset, macro variable, sas output | 6 | |
| 2 | 10 Things Every Programmer Should Know About Electronic Submissions | Greg Silva | Pharmaceutical programming changed with the advent of the FDA's electronic submission guidelines. No longer can a program that runs through many external macros and is post processed outside of the SAS System be acceptable for submission to the agency. This paper is intended to show, through ten simple rules based on our experience, how a programmer can decrease the turn-around time for generating SAS code for an FDA electronic submission. These rules can also be used outside the industry for building more transportable code | character variable, annotated crf, cdisc | 4 | |
| 3 | A Beginner's Guide to Incorporating SAS Output in Microsoft Office Applications | Vincent DelGobbo | This paper provides techniques for incorporating the output from SAS software, regardless of the install platform, in Microsoft EXCEL and Word (versions 2000 and later). The paper focuses on the use of the Output Delivery System (ODS) and SAS servers |
excel, proc step, ods html, proc tabulate, sample, sas output, proc print, output delivery system, macro variable | 8 | |
| 4 | A Method To Create Descriptive Statistics Using The CLASS Statement | Jihong Jin | This paper discusses a technique to use the CLASS statement in SAS procedures. It provides a useful and effective way to summarize data and to create descriptive statistics for data subsets defined by class variables. The paper also discusses how to arrange these descriptive statistics in accordance to the order for generating tabular reports. A sample application is presented to illustrate this technique. The technique is intended for intermediate to advanced SAS users | class variable, proc means, sample | 6 | |
| 5 | A Modular Approach to Develop Patient Profile Application | Fuping Peng | This paper describes a modular approach to develop patient profile application using Base SAS in the Unix Operating System. Five generic macros, %init, %allPrf , %index, %header, and %profile constitute the frame work of the application. For each CRF section (or panel, like AE, Medhis), a section SAS file (like ae.sas, medhis.sas) is created, and will be executed (%inc) in %profile to generate a SAS data set of the same name. The data set contains information to render the section in the output file as well as clinical data. Section data sets are combined to create individual patient profile data set, and individual patient profile data sets are combined to create report data set. As all specifics are coded in the section sas file, the frame work of the application, the five macros containing all the complexity and taking most of the developing time, can be re-used directly for future studies | macro variable, data step | 4 | |
| 6 | A One-Step Method for Finding the Closest Match | Erin M. Christen | Often two data sets have data from several dates for numerous subjects. A need to compare related data across the data sets arises. This can be accomplished by a simple merge if the observations in both data sets come from the same dates for each subject. This task becomes more complicated when the dates are close, but not exact. In these instances, one data set is used as the base and the closest observation is selected from the other data set. The DATA STEP method to do this requires multiple SORTs, a PROC TRANSPOSE, multiple MERGEs, and a complicated DO loop. PROC SQL greatly facilitates this task; two data sets can be merged on the closest match in a single step |
proc sql, proc step, do loop, if-then-else | 2 | |
| 7 | A Programming Development Environment for SAS Programs | Tim P. Williams | Organizing the programming effort for large projects can be a daunting task. Programming teams are under pressure to complete work accurately and rapidly, while using standard templates and macros whenever possible. Add to that several levels of documentation and a manager's need for frequent status updates and you have an administrative nightmare. A system is necessary to integrate programming documentation, validation checklists, and real-time status reports. After deciding to centraliz e our project management spreadsheet, program header documentation, and validation documents in a database, we found that we had the beginnings of a web-based programming development environment (PDE) for SAS programs | data cleaning, 21 cfr part 11, audit trail, sas programming, pharmaceutical industry | 8 | |
| 8 | A Reporting Macro to Create Descriptive Statistical Summary Tables | Ellen Forman | It is often necessary to produce summary tables showing statistical values such as mean, median, standard deviation (sd), minimum, and maximum, for numerical data such as lab parameters. These values may be ordered by other variables such as treatment group or visit. This paper describes an easy to use, flexible macro that will produce a variety of these tables. The SAS product used in this paper is SAS BASE, with no limitation of operating systems | %if-%then-%else, macro parameter, proc means, proc step, macro definition, change from baseline, group by, proc datasets, proc sql | 4 | |
| 9 | A SAS Annotate System for Figure Generation | Brian P. Fairfield-Carter | Figures can provide some of the greatest programming challenges in a clinical trial. This is owed in part to the complicated and sometimes non-intuitive syntax requirements of SAS/GRAPH procedures such as GPLOT and GCHART, and in part to the fact that many figure specifications can only be satisfied through a hybrid of procedure syntax and Annotate functions. This paper explores the possibility of, and some basic principles behind, efficiently creating complete figures by using only Annotate datasets rendered through PROC GSLIDE. Some underlying principles behind the placement and scaling of graphical features in line plots and histograms are described, and ways of implementing these principles in re-usable code are illustrated. For the sake of completeness, a simple VBScript controller for exporting figures as word-processor-ready documents is also demonstrated. This paper (in common with Annotate generally) is targeted at an intermediate to advanced audience |
histogram, sample, macro function, txt, data step, line chart, proc step, macro definition, %if-%then-%else | 6 | |
| 10 | A SAS Macro for Producing Clinical Laboratory Shift Table | Shi-Tao Yeh, Glaxo Smith Kline |
The results of all safety-related laboratory tests are collected in clinical trial studies. A shift table is a table that displays the number of subjects who are low, normal, or high at baseline and the shift or transition at selected time points or time intervals. This table provides key information about the health and welfare of the subjects during the clinical study. This paper provides a comprehensive, flexible, and modular SAS reporting approach for producing a laboratory shift table. The SAS/G226 product used in this paper is SAS BASE /g226 on UNIX platform |
sample, proc step, shift table, macro definition, clinical trial | 6 | |
| 11 | A SAS Macro for Simple Statistical Summary | Michael Gunshenan | This is a presentation of a macro that generates the statistical summary of a variety of derived data. It requires four keyword parameters: incoming data set, variable(s) upon which to calculate statistics, the numeric variable by which they will be ordered across the page, and a format to convert that variable into the character text to be displayed. It will determine for you which variables are numeric and which are character and apply the appropriate procedure (means / freq) to generate the statistics required. Optional keywords exist: /g183/g32two keyword variables, one numeric and one the format to convert it to character can be used to calculate the statistics by, e.g. visit number and format. /g183/g32a keyword variable to calculate by a subgroup that shows output in the form of a fraction with percentages, the denominators are the numbers of distinct patients within the groups displayed across the page. /g183/g32a keyword variable to total the columns across the page | macro variable, proc step, macro loop, proc sql, macro definition, macro conditional, create table, do loop, character variable, numeric variable, proc format, proc freq, macro quoting function, order by, data step | 10 | |
| 12 | A SAS Macro to Automate The Covariance Structure Determination in Repeated Measures Data Analysis with Mixed Procedure | Don (Dongguang) Li | Analysis of repeated measures data (RMD) is commonly used in the clinical trial studies. In the modeling of repeated measures data analysis, it is necessary to determine the most appropriate covariance structure to obtain the best fitness. SAS Mixed model provides the method and criteria of determining covariance structure. This article describes the approaches of doing the analysis and provides a SAS macro to automate the processes of covariance structure comparison and selection. Key Words: Covariance structure, Repeated measures Data, Mixed model, SAS Macro | covariance structure, covariance, proc mixed, macro definition, macro variable, data science, longitudinal data, repeated measures, clinical trial | 4 | |
| 13 | A Standard SAS Programming Toolbar: A Step Forward for GPP/SOP Compliant SAS Program Development | Carl Herremans | In Clinical Biostatistics Departments all SAS programs should comply with GPP [Good Programming Practices] and other SOP regarding programming and validation. This paper describes a standard SAS toolbar that facilitates program compliance to GPP/SOP. Using the toolbar buttons several functionality's are available to the user, for instance: (i) creation of a program template including a standard program header. (ii) a GUI in order to select and include macro calls from the macro library. (iii) update the program header after program development based on the program contents | data cleaning, macro call, macro variable, macro definition | 6 | |
| 14 | A Validation Reporting System with SAS AF | Karen Walker | A Validation Reporting System will add functionality to a well- defined software development process. Because just a development process cannot stand on its own, it requires planning, execution, coordination, and control to emulate organizational processes with high quality. Developing computerized systems for any business process will be easier to manage, and the processes created will run smoothly with the help of a Validation Reporting System. The Validation Reporting System will assist planning, execution, coordination, and control of the software development life cycle, with emphasis on accelerated software development. This software solution has a built in capability assigning roles, and for establishing a team to support execution of activities. Since it is most desirable for documentation to take place during testing and development, this system renders documentation for system test results to a standardized reporting function as they are captured | data cleaning, proc copy, proc step, sas log | 6 | |
| 15 | Adjusting Dates in a New Century | Malachy J. Foley | The Y2K scare is over, but the problem of manipulating dates correctly is still current. This paper presents SAS code that automatically adjusts the YEARCUTOFF option based on the current year. This "sliding" cut-off date means you will never have to worry about the YEARCUTOFF option again. It also insures that you can always store and manipulate the maximum range of birthdays and other dates | dataset, character variable | 2 | |
| 16 | An Application of Dummy Variable and Placeholder Macro Parameter in Programming for Clinical Trial Data Quality Monitoring | Sijian Zhang, Mark Carpenter |
An important responsibility for a biostatistician supporting clinical trials is to ensure the quality of data. The Central Unit of the Collaborative Antiviral Study Group, located at the University of Alabama at Birmingham, oversees many antiviral pediatric and adult multi-center clinical trials, most of which are long-term trials. The major part of our data quality monitoring is to check if every case record form (CRF) is collected, and endpoint values are entered correctly according to the protocol. Macro is an efficient way to handle this kind of tasks. However, since the number of variables for unique ID is not the same for different CRFs, and the endpoints are scattered in different CRFs, many macros or ordinary codes are needed to accommodate these situations. In this paper, we describe a simple approach to increase the efficiency of macros for this circumstance by using dummy variable and placeholder property of macro parameter | sample, clinical trial, binary variable, data cleaning, macro parameter, macro definition, proc compare, proc step | 4 | |
| 17 | An Useful Chart of the Global Assessment in Clinical Trials | Zhongwei Zhou | Global assessment is designed to measure the rating of the overall safety, the overall efficacy, and/or the effectiveness of a treatment. This assessment integrates the investigator's or patient's overall impression about the change in the state of the treatment, and is usually a scale of ordered categorical ratings. This type of assessment is widely used in the pharmaceutical industry. It is often an interesting and difficult task for a researcher to visually display global assessment without loss of important detailed information. This paper illustrates how to use | data science, pharmaceutical industry | 4 | |
| 18 | Another Way to Create Data Definition Files | Han Zou | Reviewing clinical data can be a very complex and time-consuming process for both sponsor and the FDA reviewer. According to the Guidance for Industry on Providing Regulatory Submission in Electronic Format - NDAs item 11, Data Definition Files (data dictionary) should describe each SAS data set being submitted to the FDA by providing the following: Variable Name, Label, Type, Codes/Decodes, and Comments (ie, Variable Derivation). Producing this Data Definition File often becomes a complicated, error prone process when the pressure of ensuing time lines are approaching. To avoid these pitfalls, AstraZeneca has taken the approach of developing Data Definitions as analysis data sets are being created. Our tool, %XLSDFN, is helping SAS programers at AstraZeneca create and edit Data Definition Files in a timely fashion | dataset, excel | 2 | |
| 19 | Application of Some Advanced PROC SQL Features in Clinical Trial Programming | Changhong Shi | The DATA STEP, as the foundation of SAS, provides many programming options. However, SAS programs using DATA steps sometimes get long and the logic gets complicated. PROC SQL can be used as an alternative. For example, PROC SQL can provide very elegant code when a data set needs to be appended to another data set, excluding duplicate records. In this paper, we will review some useful, usually overlooked, features of PROC SQL and their applications in clinical trials. Five examples are discussed in this paper. They include: creating macro variable(s) with compact code, building a macro variable with a list of values dynamically, appending one data set to another one and excluding duplicate records, joining tables, especially many to many joins and self join, and filling in missing data. All of the examples contrast the use of PROC SQL with other SAS techniques, especially DATA STEP coding |
proc sql, proc step, macro variable, clinical trial, missing value, create table, laboratory data, set statement | 6 | |
| 20 | Automated Page Breaking and Numbering For Output | Lara E.H. Guttadauro | Have you ever been frustrated trying to get your output to break pages correctly? If so, the macro presented in this paper will appeal to you. The PAGENUMS macro allows for multiple columns of wrapped text, keeping data grouped together on the page and forcing page breaks | adverse events, macro variable | 4 | |
| 21 | Automatically Obtaining and Using Data Creation Dates | Varsha C. Shah, Katherine J. Roggenkamp |
This paper describes two ways to take automatic advantage of data creation dates, as well as two easy ways to obtain them (PROC SQL and PROC CONTENTS). The first example illustrates making a date part of the title of a data management report. The second shows incorporation of the date into data set names |
proc step, proc contents, proc sql | 2 | |
| 22 | Automatically Output Rich Text Format Tables Using Dynamic Table Definitions in DATA _NULL_ Step | Mei Tang | This paper presents an automated approach to produce word processor ready tables using ODS option of FILE statement in DATA _NULL_ step to output tables to ODS RTF destination with customized table definitions. A macro with simple parameters is introduced to generate dynamic table definitions through PROC TEMPLATE according to different table structures. The use of such technique eliminates the need for manual word processing of SAS outputs, resulting in improving the quality and accuracy of reports, and conforming to FDA guidance. The approach is flexible, heuristic, and easy to modify with different layout standard. SAS version 8.2 for Windows environment is used for practice. Since the approach adopt pure SAS language, it is easy to learn by SAS programmers at different skill levels |
proc step, proc template, file statement, put statement, output delivery system, sas programmer, proc tabulate, macro facility, ods rtf, macro definition, macro variable | 10 | |
| 23 | Automating the Creation of a Single Bookmarked PDF Document from Multiple SAS ASCII and PostScript Output Files | Donovan Verrill, Eugene Yeh, Syamala Kasichainula |
When creating files that contain multiple SAS outputs for QA, medical writers, clients, and regulatory agencies, it is helpful and sometimes required that the outputs are placed in to a user- friendly file format. One solution is to create a bookmarked PDF document using Adobe Acrobat Software. However, manually generating a PDF document from multiple SAS output files is a time consuming task. The time needed to manually create a bookmarked PDF document substantially increases with the number of SAS output files included. This paper presents an alternative method of creating a bookmarked PDF document. We have replaced the manual process by automating the compilation of multiple SAS outputs, and the creation of bookmarks to each SAS output. We will discuss the process of ordering and appending all SAS ASCII and PostScript output files into one cumulative file. In addition, we will describe a method to automatically create bookmarks within the PDF document | sas output, proc report, put statement, excel, proc step | 8 | |
| 24 | Automating the Creation of Data Definition Tables (Define.pdf) Using SAS Version 8.2 | Eugene Yeh | One component of an electronic NDA submission to the FDA is the metadata file Define.pdf, which contains documentation of Case Report Tabulation (CRT) datasets. This file contains a list of dataset names and their descriptions and location. For each dataset, a list of variables and their associated attributes (e.g. length, label, format name, list of decoded values, etc…) are described. Prior efforts to automate the creation of Define.pdf using a SAS version 6.12 program had limitations in handling large text fields and the PDF file format. We have made enhancements to Define.pdf, including the addition of new SAS variable attributes, as well as automating the inclusion of a generic text comment field, used to display the variable source and the definition of derived variables | label statement, proc step, proc report, proc contents, variable, character variable, clinical trial, temporary dataset, ods pdf | 8 | |
| 25 | Benchmarking Forecasting Practices in the Pharmaceutical Industry | Chaman L. Jain, St. John's |
Since business forecasting as practiced these days is a very young function, businesses are looking for benchmarks, which can help them to determine where they are in relation to the industry norm, and if they are below it, what they have to do to get there. This paper shows the norms of pharmaceutical industry as well as the norms of 13 different industries combined. The benchmarks include forecasting process, support of upper management, forecasting models used, forecasting errors, forecasting software and systems used, salary of forecasting professionals and their background | pharmaceutical industry, time series analysis, residual, regression analysis | 6 | |
| 26 | Capabilities and Example Uses of a SAS Macro for Summary Reporting of Clinical Data | James T. Decker, Jun Wang |
Programs for production of summary clinical data reports are most effective when they are both easy to use and capable of generating a wide range of outputs. This paper reviews capabilities for a single report macro that is part of Procter and Gamble Pharmaceuticals STARS (Standard Template Automated Report) System. This macro can be used to create demographic data summaries and other types of summaries as SAS datasets, conventional SAS output reports, and as RTF tables for immediate inclusion in the body of text reports produced in Microsoft Word. Code capabilities are reviewed and different example invocations are shown. Finally, the way in which STARS code is documented for users, and the testing procedures applied are briefly reviewed | proc means, nominal variable, dataset, interval variable, sample, data cleaning, raw data, clinical data, ods rtf | 8 | |
| 27 | CDISC, ADaM, and TTE: What Are These Acronyms and How Can They Help Me with My Filing? | Sandra Minjoe, Judy Phelps |
If you're planning a filing and know you'll need to submit analysis data to the FDA, but you don't know where to begin, then you've come to the right place. This paper presents a set of Time-to- Event (TTE) analysis datasets developed for the Analysis Dataset Modeling Team (ADaM) working group of the Clinical Data Interchange Consortium (CDISC). It shows how datasets of this type can be developed by your site and then used by both you and FDA reviewers in running your analyses. It also provides | adam dataset, proc step, data science, time to progression, clinical trial, study day, censoring indicator, procedures, cdisc | 4 | |
| 28 | CDISC: Why SAS Programmers Need to Know | Victor Sun | As CDISC advances global clinical data standards, it has been widely and rapidly expanded in pharmaceutical industry both domestically and globally. The pilot studies with CDISC based electronic submissions to FDA have been conducted from about ten companies. CDISC attention, however, has not been addressed enough in STAT group even though its implementation is bringing up a significant impact, especially to SAS programmers in terms of work skills and job security. After briefly introducing CDISC development and outlining its implementation approaches, future expectations of challenge and opportunity facing to SAS programmers are discussed. The suggestions provided are intended as a useful | clinical data, pharmaceutical industry, cdisc | 4 | |
| 29 | Clinical Data Model and FDA/CDISC Submissions | Mark Edwards, Gajanan Bhat |
Development of data model in clinical trial management environment is an ongoing process but of utmost importance for a good clinical information management. Companies are trying to come up with efficient global standards (e.g., ICH) in data modeling to work with. Global standards coupled with CDISC guidelines are also important and imminent in view of the new electronic submission guidelines of the Food and Drug Administration (FDA) for New Drug Application. CDISC (Clinical Data Interchange Standards Consortium) partnered with the FDA to standardize on electronic submissions to improve the review/approval process of an NDA. CDISC is an open, multidisciplinary, non-profit organization committed to the development of worldwide industry standards to support the electronic acquisition, exchange, submission and archiving of clinical trials data and metadata for medical and biopharmaceutical product development | data standard, normalization, key, adverse events, clinical trial, clinical data, star schema, data science, dataset, cdisc | 10 | |
| 30 | Clinical Data Standards - An eSubmissions Perspective | Shy Kumar | Sometimes we might lose sight of the fact that the data itself is our most important product in the clinical sections of an eNDA or eBLA. A marketing licensing application approval process is highly dependent on the data submitted with the application - its content, integrity, structure and documentation. The structure of the data (submission data) is important to those reviewers who will actually use the data. The structure of the database is usually different for data collection (the operational database) and data analysis and reporting (the submission database). The operational database records information directly from the study CRFs (case report forms.) The submission database will contain derived variables and is structured for easy use with statistical software. Standards for the documentation of the data are found in FDA guidelines. The Clinical Data Interchange Standards Consortium (CDISC), an industry standards group, recommends additional standards | clinical data, annotated crf, clinical trial, proc copy, proc step, adverse events, data standard, cdisc | 6 | |
| 31 | Confidence Intervals in Analysis and Reporting of Clinical Trials | Guangbin Peng | Regulatory agencies around the world have recommended reporting confidence intervals for treatment differences along with the results of significance tests. SAS provides easy and convenient ways to produce confidence intervals using procedures such as PROC GLM and PROC UNIVARIATE in conjunction with ODS (output delivery system). In this paper, I will discuss the relationship between significance tests and confidence intervals, summarize the types of confidence intervals used in clinical study reports, and provide examples from clinical trials to illustrate the computation of distribution- dependent confidence intervals for the mean treatment difference and distribution-free confidence intervals for the median response within each treatment group using SAS |
confidence interval, clinical trial, treatment effect, sample, confidence level, clinical data, proc step, data step, sample size, proc calis, binomial distribution | 8 | |
| 32 | Coordinated Efforts for Preparing Statistical Review Aids | Coordinated Efforts | FDA electronic submissions (e-submissions) have been rapidly adopted by pharmaceutical companies in recent years. Statisticians and programmers are responsible for preparing the statistical review aids that are incorporated into these e- submissions. There are many challenges involved in this process because it is complex and time-consuming and often requires the work of many different groups within a company. It is important to develop methods for meeting these challenges in order for companies to create consistent, efficient, and high quality statistical review aids. This paper presents a number of suggestions for streamlining the preparation process, coordinating the efforts of multiple groups, keeping current on FDA guidelines, and standardizing high quality statistical review aids | data cleaning, csr | 6 | |
| 33 | Counting the Number of Episodes of a Given Disease From Diary Datasets | Jose F. Diaz | This SAS macro estimates the number of episodes of disease from diaries. A diarrheal disease dataset is used as an example to illustrate is flexibility | 2 | ||
| 34 | Creating "that other" Laboratory Listing: Let PROC TRANSPOSE, FREQ, CONTENTS and REPORT Do the Job for You | Luis F. Albarran | When programmers produce a listing of laboratory data they expect the most common request, to have the listing displayed by patient with laboratory tests as rows and their laboratory draw dates as columns. But what if just the opposite is requested? If you have never had to program this in the past and even if you have, you probably thought just as I did, I'll have to tackle this problem using the DATA NULL report writing step. Here I will present the use of another technique that makes this an easy and painless way of displaying lab data in reverse, with the laboratory draw dates as rows and laboratory tests as columns with their respective units by using PROC TRANSPOSE, PROC FREQ, PROC CONTENTS and PROC REPORT in an almost generic manner |
proc step, proc freq, proc report, proc transpose, sample, if-then-else, macro variable | 4 | |
| 35 | Creating Display Manager Abbreviations and Keyboard Macros for the Enhanced Editor | Arthur L. Carpenter | The boss wants all of your SAS programs to have a header section of comments. She wants this section to be uniform across all of your programs, she wants it to have lots of stuff in it, and you don't like to type. How can you automatically insert uniform blocks of text into your program with a minimum of typing and without using the paste buffer? The answer lies in the use of Abbreviations and Keyboard Macros. Abbreviations allow you to associate any amount of text with a designated word or set of keystrokes. Type in the designated word, press the tab key and the desired text is substituted into your program. In the example shown below, you will see how typing the word 'HEADER' inserts a series of comment statements that become the standard header block for a program | proc report, title statement | 4 | |
| 36 | Cross-referencing Data Contents and Case Report Form | Sam Mao | Annotated case report forms (CRF), data contents, and data formats are the core documents for statistical programmers to understand the data collection, the relationship between variables, and variable properties and coding. CRF and data contents are often cross-referenced. Output from PROC CONTENTS does not facilitate quick and easy cross-reference between CRF pages and data contents. This paper introduces the macro %CNTFMT to create customized data contents, which is more compacted but information richer, and allow quick cross- referencing between CRF pages and data contents. Other features of the macro, which can meet a programmer's reference needs, will also be presented. Those features include getting study data-specific format decodes from multiple levels of format libraries, contents of partial library and so on. Macro-programming tips will also be shared. The macro has been tested and used with both SAS Version 6 and 8. The paper is suitable for all levels of audience |
crf, proc format, proc step | 4 | |
| 37 | Danger: MERGE Ahead! Warning: BY Variable with Multiple Lengths! | Bob Virgile | merge statement, length statement, data step, proc sql, by group processing, error message, macro facility, macro variable, select, proc step, macro call, macro definition | 6 | ||
| 38 | Data Extraction & MedDRA: A SAS Coding Shortcut | Fang Dong, Saket Badola |
One of the principal tasks of clinical trials reporting is the summarization of adverse medical events reported during the course of each trial. To do this, we rely upon standardized dictionaries of adverse events, such as COSTART and MedDRA, which facilitate the classification of events into medically relevant groupings. Our paper first will briefly describe the structure of the MedDRA dictionary and contrast it with COSTART. Then we will describe a method of using SAS and Oracle functions to code MedDRA classification of adverse event terms for clinical trial reporting purpose. We will give several detailed examples of the use of Oracle functions to do classification lookups via SAS SQL pass-through extraction of OC views. Finally, we will briefly discuss the implication on adverse event reporting in the MedDRA world. Skill Level: Intermediate SAS, Macros, SAS ACCESS, SAS/SQL, Basic PL/SQL |
adverse events, proc sql | 4 | |
| 39 | Datamapper: A Documentation Generator for SAS Metadata | Lei Zhang | SAS metadata is the data about SAS datasets, which is critical to the effective manipulation and analysis of SAS data. The SAS metadata exploration traditionally proceeds as ad-hoc programming on SAS Dictionary tables, which is inadequate, inefficient, and sometime complicated. Based on SAS ODBC functionality and hypertext techniques, a documentation generator called Datamapper is being developed to automatically create a set of structured hyperdocuments (called datamap) for the SAS metadata. The datamap allows users to interactively access essential piece of meta-information about SAS data objects or elements and traverse between them based on the established hyperlinks while avoiding ad-hoc SAS metadata programming. In this paper I first present a conceptual model for the SAS metadata and then the hyperdocument architecture of the datamap designed with OOHDM (Object Oriented Hypermedia Design Methodology), and finally discuss the development and implementation of the Datamapper | dataset, variable, dictionary tables, sas library, sas programmer, clinical trial, missing value, sample, sas programming | 8 | |
| 40 | Defining a Validation Process for End-user (Data Manager/Statisticians) SAS Programs | Andrew Lawton | data cleaning, data science | 6 | ||
| 41 | Descriptive Statistics: Turning the very MEAN into very CUTE | Adel Fahmy | In most statistical tabulations, Descriptive Statistics are required for Numeric Variables. The most heavily used statistics include: the Number of observations on which calculations are based (N), Mean (MEAN), Standard Deviation (STD), and Extreme values of the variable (MIN | call symput, macro variable, proc step, class variable, temporary dataset | 4 | |
| 42 | Developing "EXSAS" an integrated system for statistical analysis of non-clinical experimental data using animals | Chikuma Hamada | We have been developing "EXSAS", which combines SAS with EXCEL to provide accurate and user friendly statistical analysis system for investigators in preclinical department. EXSAS was systematically constructed in order to make it easy to manipulate and to understand the result of statistical analysis even for experimenters with limited knowledge of statistics. After preparing the data in the specified form of EXCEL sheet, EXSAS directly imports EXCEL sheet to SAS dataset and experimenters choose a statistical method from the menu. The results of statistical analysis are converted to an EXCEL sheet through output delivery system (ODS). The following statistical methods are available in the most resent version of EXSAS 6.10; preparatory analysis, basic parametric methods |
data science, excel, dunnett test, raw data, sample size, scatter plot, outlier, sample, anova, regression analysis | 6 | |
| 43 | Duplicates: Any Database, Any Client, Any Time | Adel Fahmy | Do not lock the database before capturing and removing duplicate records. A Physical Duplicate occurs when two or more records are identical in the values entered in All fields. This will be covered in this paper. A Logical Duplicate occurs when two or more records appear to be different in the value(s) of some field(s), but in fact they represent the same set of information. Eliminating duplicates will ensure accurate statistical and graphical results. The program, we are presenting here, is a generic one, macro-driven that "builds" the code as it goes along. The only required information you have to provide is the location (directory/folder) where the raw data resides. Neither you have to provide dataset names nor lengthy variable lists. The program will run for any project or any client during any stage of the database development cycle. Now relax and let the program do all the work | macro variable, call symput, macro put, proc step, libname statement | 6 | |
| 44 | Electronic Submission of SAS Data and Catalogs Simplified | Adel Fahmy | Although many study sponsors make efforts toward Electronic Submissions, little has been accomplished for Standardization of Case Report Forms and Database Design, which are the foundation of any clinical study. The process we are presenting here is geared toward fine electronic submission that covers all grounds for both data and catalogs. Despite the complexity of some macros, they greatly simplif y the e-submission process. The presented code is an efficient and self-validating one that can be used for both creating e-submission or derived datasets. It has been heavily used for many projects. When some sponsors received the e-submission files and noted their consistent structure and format, they requested that all deliverable (data and catalogs) be in e-submission form. The process is split into two parts: Generic Macros (a black box) that run on any project, and Study Specific macros that the user can very easily modify in order to tailor each dataset | proc step, macro variable, proc format, set statement, data cleaning, proc copy, proc datasets, dataset, proc cport, select, temporary dataset | 6 | |
| 45 | Enhancement of Survival Graphs | Joanne C. Zhou | As use of time-to-event data in clinical trials is becoming more frequent, survival analysis and the graphical presentation of the survival analysis are more and more popular. In order to aid correct interpretation, overall summary statistics and some reflection of statistical uncertainty should be added in the survival plots. In this paper, PROC LIFETEST, PROC GPLOT and PROC GREPLAY are used to present the results of survival analysis with annotated +/-standard errors on the curve for selected time points on the Kaplan-Meier estimates; the numbers of patients at risk are displayed under the time axis |
survival analysis, proc step, proc calis, proc means, proc gplot, macro variable | 6 | |
| 46 | Error Checking and Version Control of SAS Programs | Christopher Kania | The methods used for error checking and tracking of SAS programs can be a part of a process to comply with various aspects of the FDA 21 CFR Part 11 regulation. Increasing staff counts have made a paper and pencil-based approach to comply with FDA regulations unreasonable. Additionally, the Biogen Biostatistics department wanted to collect a standard set of information for each SAS program that is checked. Using a customizable off-the-shelf configuration and change management application has made the task of validating and tracking SAS programs less prohibitive. Moreover, this electronic approach has provided the users and managers with summary reports of the status of one SAS program or the status of a project consisting of many SAS programs | sas programmer, data cleaning, quality assurance, quality control | 4 | |
| 47 | Exporting SAS/GRAPH Output: Concepts and Ideas | Dawn Schrader | This document is designed to help you understand the basic factors that affect the quality of an image exported from SAS/GRAPH. This paper focuses on exporting graphics to two very popular destinations: the web and Microsoft Office |
sample, vertical bar chart | 6 | |
| 48 | Generating Custom Excel Spreadsheets Using ODS | Chevell T. Parker | This paper will demonstrate techniques on how to effectively generate files that can be read with Microsoft EXCEL using the Output Delivery System. This paper will further discuss a variety of methods that will allow customization of the every part of the EXCEL file from ODS. Some of the tips provided will work with EXCEL 97, 2000 and 2002. However, much of what is covered especially, the advanced techniques using XML and the special Microsoft Office style properties apply to EXCEL 2000 and 2002 |
excel, ods html, output delivery system, error message, data cleaning | 10 | |
| 49 | Generating Data Definition Tables for Electronic NDA Submissions | Robert D. Abelson | In January 1999, the Food and Drug Administration (FDA) released Guidance for Industry: Providing Regulatory Submissions in Electronic Format - NDAs. In that document, the standards for SAS datasets provided to the FDA were specified, along with a requirement that all datasets within a study be documented with data definition tables. The data definition tables can be produced directly from a SAS data library, provided that all datasets have dataset labels, all variables have variable labels, and a format catalog exists. The SAS Output Delivery System makes it possible to produce the tables as PDF files, as required by the guidelines | create table, dataset, libname statement, excel, proc step, proc import, label statement, dictionary tables, left join, order by, proc sql | 4 | |
| 50 | Generating PDF Files from Output by DATA _NULL_ | Eric Zhang, Snow Fu |
Documents produced in the Portable Document Format ("PDF/g226") are becoming popular among pharmaceutical companies. In fact, the FDA requires PDF documents in the submission of New Drug Applications ("NDAs"), including PDF files of SAS/G226 output. However, the many companies using the DATA _NULL_ technique to produce tables in SAS have had a problem generating PDF files because the macros using DATA _NULL_ could not be updated to convert SAS output into PDF files. In order to resolve this problem, a SAS macro, called OUT2PDF, has been developed internally. This new macro creates PDF files for output produced by non-procedures, such as DATA_NULL_. This paper describes the capabilities, features, structure, and usage of this macro. KEYWORDS: OUT2PDF, Macro, PDF, DATA _NULL_, SAS Output Text Files, ODS, NDA Submissions |
sas output, txt, proc step | 4 | |
| 51 | How to LOCF without RETAIN and REINITIALIZING for FIRST.PATIENT | Venky Chakravarthy | Pharmaceutical companies conduct longitudinal studies on human subjects that often span several months. It is unrealistic to expect patients to keep every scheduled visit over such a long period of time. Despite every effort, patient data are unavailable for some time points. Eventually, these become missing values in a SAS data set. However, for reporting purposes, the most recent previously available value is substituted for each missing visit. This is called the Last Observation Carried Forward (LOCF) and is familiar to the Pharmaceutical audience. There are a number of ways to carry forward the last observation using SAS. This paper focuses on a technique that has gained prominence among SAS-L regulars. It is called the DO loop of Whitlock (DOW) also known as the Dorfman-Whitlock loop | data step, do loop, baseline, last observation carried forward | 4 | |
| 52 | Implementing a Quarterly Contract Payments System for Clinical Trials | Mark McLaughlin, Independent Contractor |
By using Base SAS and SAS macro language, we have created a contract payment system for the reimbursement of clinical sites based on their Case Report Form completion. With only a few SAS programs, the system automatically assigns payment values, generates summary report, and outputs letters to be mailed. This paper discusses the issues surrounding the creation of the contract payment system. SAS code associated with key functions is presented | macro variable, subject visits, clinical trial, proc step, dataset, macro facility | 6 | |
| 53 | Implementing Constrained or Balanced-Across-the-Centers Randomization with SAS v8 Procedure PLAN | Cindy Q. Song, Olga M. Kuznetsova |
When a clinical trial employs a large number of small centers and uses a permuted-blocks allocation sc hedule, one might face a situation when most of the blo cks are expected to be incomplete. In that case, additional means to promote balance in treatment assignments can be employed. Constrained randomization [Youden 1962, 1974] uses not all permuted blo cks, but only the selected ones (for example, those better balanced at the beginning of the block). Another possibility is to list all existing permuted blocks with given treatment ratio [Zelen, 1974], randomly permute them and assign sequentially to the centers. The advantage of such allocation is that a close treatment balance will be achieved across centers among subjects enrolled first, second, etc. SAS 8 PROCEDURE PLAN constructs a variety of experimental designs and provides an easy means of generating such schedules |
randomization, treatment arm, proc step | 8 | |
| 54 | Innovative Graph for Comparing Central Tendencies and Spread at a Glance | Varsha C. Shah, Ravi M. Mathew |
The methodology described in this paper includes overlay and other features of PROC GPLOT, a little bit of the ANNOTATE facility, and PROC TEMPLATE for construction of this comparative plot. The graph enables quick visual assessment of effect magnitude and direction, and the code can be easily changed to accommodate new and different numbers of subgroups. Though the mechanism of plotting the graph is more general, it is easy to illustrate with the common concepts of central tendencies and spread. Therefore, the data for this paper is based on randomly generated numbers. The appendix includes the sample figure, data used in the example, and the SAS code needed to produce the figure |
proc step, sample | 8 | |
| 55 | Integrating SAS and Microsoft Office for Analysis and Reporting of Hearing Loss in Occupational Health Management | George Bukhbinder, Michael Krumenaker |
Evaluating the effectiveness of hearing conservation programs is an important part of occupational health management (OHM). Palisades Research, Inc. recently developed a data management and reporting system that provides access to historical audiometric data, performs analysis and creates a number of important audiometric measures. The system's analytical engine, which is written in SAS code, downloads data from a Microsoft SQL Server operational OHM database, performs data manipulation and calculations, and creates EXCEL reports and pivot tables on the company's shared drive. This system was implemented at ExxonMobil Corporation. It performs analysis of audiometric data and produces hearing loss statistics required for reporting to OSHA |
excel, data cleaning, sql server, proc sql, data step, xls, proc freq, proc step | 6 | |
| 56 | Invisible Environmental Awareness Techniques | John K. Troxell | In many SAS programming situations, it pays to be aware of the current state of the SAS session environment. Features added to recent releases of the SAS/BASE product are making this increasingly possible. This paper presents programming techniques that permit programs to obtain the session information necessary to make intelligent choices on the fly, prevent problems, and avoid trampling blindly on the pre-existing state of the session. The techniques discussed here, when used in macros, are "invisible" - they operate behind the scenes and don't leave any traces in the log |
macro variable, proc sql, proc step, macro put, %if-%then-%else, dictionary tables, macro definition | 4 | |
| 57 | Java Servlets and Java Server Pages for SAS Programmers: An Introduction | Miriam G. Cisternas | AppDev Studio 2.0 provides a drag-and-drop interface to produce Java-based thin-client applications using Servlet technology, as well as support for Java Server Pages (JSPs). Those of you who have created Java Servlets using the drag and drop methods may be somewhat mystified by the Java code generated. What is an import statement, and what is the function of those curly braces? And just how do Servlets work, anyway? How do they interact with the web server, track a session and process browser cookies? Since Servlets can access any Java API, we hope to whet your appetite to learn more about Java and how to extend the Java classes that SAS provides in AppDev Studio. Finally, we will provide a brief introduction to JSP scriptlet and tag syntax | http, dataset | 10 | |
| 58 | Likelihood Approach in Estimation and Testing Problems Involving Grouped Data | Daniel Bonzo, InfoVision Consultants |
The classical framework for handling grouped data is modified to allow the use of overlapping grouped data in estimation and testing. Estimation and testing is approached using the usual likelihood mode. Testing of hypothesis is demonstrated by providing a procedure for the classical distribution fitting problem. The approach is illustrated using the normal and logistic cases. Simulation results are presented to show the soundness of the method. Finally, a discussion of the application setting in dosing studies is presented | likelihood function, sample, dose, sample size, censoring, maximum likelihood estimation, clinical trial, logistic distribution, adverse events | 8 | |
| 59 | Macro Specializing in Adverse Events Reporting | Tony T. Salehi, Doug Moore |
One of the difficult tasks w hen displaying Adverse Event summaries using the SAS procedure, PROC REPORT, or a DATA _NULL_ keeping the MedDRA coding system, System Organ Class (SOC), or HARTS coding system, Body System on a single page of the SAS output without continuing onto the subsequent page. Typically, when the maximum number of allowable data lines is reached during the execution of a PROC REPORT, the remaining portion of the data records are placed onto the next page. Often times, the statistician and or client will request that the first level term (SOC term) always appear above the lower level terms represented. The first level term is displayed on the first page it appears or retained and labeled as "continuing" from the previous page. As a result of both client demands and statistician requests, a macro has been developed to add the current SOC term from the previous page and it's contents with the text, "(cont.)" concatenated to the end of the text portion of the SOC term |
proc report, proc step, adverse events, adverse event summary | 2 | |
| 60 | Macros Used to Create Descriptive Statistic Summary Table of Neurotoxicity Scores | Jonson C. Jiang | Four body-site variables are summarized using three Macros to get descriptive statistic tables according to neurotoxicity score data of their baseline, certain cycle and change from this cycle to baseline, respectively. The cycle number in a Derived Dataset is passed to Macro_1 by Data _ Null_. Macro_1 is transferred five global variables to Macro_2 and a corresponding Variable Block is got. Four Variable Blocks are merged together with a Label Block to get a | proc means, missing value | 4 | |
| 61 | Next Generation Data _NULL_ Report Writing Using ODS OO Features | Daniel O'Connor | This paper is targeted for advanced data null report-writing programmers that are eager to take advantage of the new ODS Object Oriented (OO) features built directly into the DATA STEP environment, which will be experimentally available in Version 9.1. Features will allow you to build tables cell by cell with all of the ODS features that you have become accustom to such as proportional fonts, traffic lighting in cells, embedded images, but will also allow you to create non-tabular output, insert sub-totals, and manage your layout |
data step, output delivery system, file statement | 8 | |
| 62 | Nine Steps to Get Started Using SAS Macros | Jane Stroupe | Have you ever heard your coworkers rave about macros? If so, you've probably wondered what all the fuss was about. This introductory tutorial unravels the mystery of why and how you use the SAS macro facility. In the nine easy steps outlined in this paper, you follow the process of testing your code, substituting macro variables into the code, and turning your SAS program into a macro definition with conditional processing | macro variable, macro definition, macro facility, symbol table, macro parameter, %macro parameters, call symput, local symbol table, automatic macro variable, global symbol table, proc print, proc sql, proc step, put statement, title statement, keyword parameter, macro function, %if-%then-%else | 6 | |
| 63 | ODS and Web Enabled Device Drivers: Displaying and Controlling Large Numbers of Graphs | Arthur L. Carpenter, Richard O. Smith |
With the advent of the Output Delivery System, ODS, it has become much easier to generate graphs that can be displayed online. ODS works with web enabled device drivers such as GIF, HTML, and WEBFRAME to create graphs that can be browsed and even interactively modified by the user. Coordination becomes problematic however, when a large number of graphs need to be displayed. Tools and techniques are available that permit the programmer to create hot zones that allow the user to drill down through graphs and index tables to find the graphs of interest. Coding techniques, including the use of macros to control the process, can be used to simplify the process of managing, naming, and locating large numbers of graphs. Although primarily directed to graphical displays of the data, many of these same techniques can be applied to reports and data summaries |
proc step, macro variable, proc print, output delivery system, proc gchart, ods html, proc gplot, catalog entry, macro loop, macro facility, macro definition | 8 | |
| 64 | ODS LAYOUT: Arranging ODS Output as You See Fit | Brian T. Schellenberger | A new experimental feature for version 9 (both v9.0 and v9.1) is the ODS LAYOUT feature. Using this feature, you can easily mix graphics, text, and tables on the same page, arranging them in any position you want on the page. It's a bit like PROC GREPLAY only not just for graphics anymore. Complex reports and even forms, which were previously almost impossible to produce with ODS, are now quite straightforward to create. If you are required to present your data in a very specific format, with different parts of the report at different positions on the page, we finally have a solution for you |
output delivery system, sample | 6 | |
| 65 | Overview of a Browser-Based Clinical Report Generation Tool | Paul M. Gilbert, Gregory T. Weber, Teofil Boata |
In an effort to increase reporting quality and lead to decrease time-to-market, drug development organizations are focusing on automating the clinical reporting generation process. SAS-based data and tools have been key to generating most of the analysis and reports that are included in the clinical sections of regulatory submissions. Many companies are developing applications to automate this process. DataCeutics has developed the DataCeutics Report Portal (DRP) to address this need. The DRP is a browser-based thin-client application for producing SAS Reporting. The DRP allows users to login, select a project, and select a study within the project. Study selection activates the study functions. Study functions include: Building, testing and saving a SAS report generation program. Re-executing multiple SAS programs. View the SAS source program; SAS log or SAS output files (ASCII, RTF, HTML, PDF or XML) | sas log, sas output, 21 cfr part 11 | 6 | |
| 66 | Parameter estimation for incomplete bivariate longitudinal data in clinical trials | Naum M. Khutoryansky | Bivariate models are useful when analyzing longitudinal data of two correlated endpoints. Longitudinal data in clinical trials are often incomplete which can create bias in estimation of their means and variances. The incompleteness is partially caused by withdrawal from trials. In some clinical trials with two endpoints, only one of them is used as a marker for withdrawal. The drop-out mechanism plays an important role in correct estimation of statistical parameters of bivariate longitudinal data. In this paper, we compare several methods of parameter estimation for bivariate data including linear mixed models (PROC MIXED), multiple imputation method (PROC MI) and incremental |
longitudinal data, imputation, standard deviation, proc step, correlation, markov chain, multiple imputation, clinical trial, response variable, proc mi, proc mixed, sample, missing value, covariance structure, macro variable, data step, covariance | 6 | |
| 67 | Performance Tuning of Java-based Application Development for Clinical Pharmacogenomics Associations | Zongwei Shen, Richard Judson, Robert Tracy, Theodore Kalbfleisch |
The DecoGen informatics system, a sophisticated Java-based analysis tool, allows users to browse various aspects of genomic data and conduct pharmacogenomics association studies. The system can be seamlessly integrated with powerful SAS software by utilizing the interfaces available from AppDev Studio . Tuning the performance of such an application system is essential when the analyses combine large genomic and clinical data sets with complex statistical manipulations. This paper will discuss how to substantially enhance the overall system performance by introducing some useful Java and SAS programming techniques | proc step, proc glm, dataset, macro definition, proc genmod, data step, sas output, logistic distribution, regression analysis | 4 | |
| 68 | Practical Tips to Customize a SAS Session | Xiaohui Wang | SAS Programmers often find that a nicely customized SAS session can greatly facilitate their routine jobs and save time spent on repetitive tedious tasks. This paper discusses some easy ways to customize a SAS session, including toolbar customization, shortcut keys, the use of the SAS configuration file and the autoexec.sas file. All the examples work on SAS V8 Windows platforms | options statement, excel | 4 | |
| 69 | Proc Compare to Validate Datasets | Angelina Cecilia Casas | Comparison of two datasets is a technique used to know that two datasets are equal or if they have discrepancies. This method can be used to validate that a dataset has been created correctly or that the changes in a dataset are only those that are expected. I.e. observations were added or removed or corrections were done | proc step, proc compare, proc sql | 4 | |
| 70 | Producing Structured Reports in a Regulated Environment Using SAS - Meeting 21 CFR Part 11 | Andrew Lawton, Michael Pearce |
catalog entry, 21 cfr part 11, where statement, clinical trial, sas output, proc step, dataset | 6 | ||
| 71 | Progress Toward Standardization of Submissions with the Electronic Common Technical Document and the Evolving Standardization of Clinical Data | Susan Bairnsfather | The FDA has posted the electronic Common Technical Document (eCTD) in parallel to their 1999 NDA electronic guidance on their website. The FDA indicated that the eCTD is a 'strongly recommended' guidance for submission by a proposed date of July, 2003. A common format will significantly reduce the time and resources needed to compile applications for registration of human pharmaceuticals and will ease the preparation of electronic submissions. Regulatory reviews and communication with the applicant will be facilitated by a standard document of common elements. In addition, exchange of regulatory information between Regulatory Authorities will be simplified. In parallel with the advent of the eCTD, evolving standards are reaching down to the specifics of datasets and variables, and linking up with submissions to the Agency | clinical data, clinical research, adverse events | 8 | |
| 72 | Queries by ODS Beginners | Varsha C. Shah | This paper presents a list of questions often asked by those initially experimenting with ODS output. Why might questions arise? One could be converting from Version 6 or simply trying to improve the appearance of output. Also, often the same results can be achieved in several different ways, and a novice might wonder about this. Here, an attempt is made to answer some common questions in as simple a way as possible, one thing at a time. It is assumed that after getting to know the basics, the reader will experiment with combinations of ODS features to achieve the desired output appearance. To keep things simple, neither templates nor ODS styles are introduced. Also, it is worth mentioning that future versions of SAS will make some of the features described here available more conveniently. This work was done with Version 8.2 |
proc step, proc report, proc tabulate, ods rtf | 8 | |
| 73 | Reading Data from A Large Number of External Files: How to Do It Efficiently? | Chang-Min Chen | Under many circumstances, SAS programmers are asked to read data from multiple external files of the same nature or structure. The number of external files that the data come from is potentially quite large, and there may be hundreds or thousands data lines in each external file. However, the programmer could be only interested in those data with some specified characters. How to complete this tedious job accurately in the shortest time becomes the critical issue to resolve. This paper discusses two approaches to complete the task: using functions and using modified INFILE statements. Both approaches can convert external data files to a SAS dataset in one DATA STEP. The major benefit of using them is the gain in speed. The more external files that the program needs to read data from, the more efficient the approaches will be |
data step, infile statement, dataset, macro variable, stop, macro definition | 4 | |
| 74 | SAS and MedDRA - A Winning Combination A "Home Grown" application for the Medical Coding of Adverse Events with MedDRA | Elaine Dempsey | The introduction of the MedDRA dictionary as an authoritative source for the coding of adverse events has presented a challenge to many pharmaceutical companies and CROs responsible for doing this coding. As a small but growing CRO, we had to make the decision whether to spend our money on a vendor application or to invest our money and time in the resources within the company to develop a MedDRA coding tool of our own. The latter option was chosen and SAS would be the tool. Our coding system was built using SAS version 8.2 and leveraging the power of the object-oriented AF/Frame technology. Base SAS, SAS/SQL and SAS/SHARE were also used from within OO environment. The intent of this paper is to demonstrate the winning partnership between SAS and MedDRA |
adverse events, medical coding, lowest level term, dataset, concomitant medications | 4 | |
| 75 | SAS Macro to Create ADDL Records To Estimate Dosing Not Represented in the Collected Data, In a NONMEM Population Pharmacokinetic Analysis Dataset | Richard Schneck, Mike Heathman, Lynn Reynolds |
Datasets created to be input to the NONMEM software for Population Pharmacokinetic (PK) and Pharmacodynamic (PD) analyses are often built from data which captures only the last few doses prior to a blood draw for PK or PD sampling, when in fact dosing occurs at regular intervals leading up to the visit at which the sample is drawn. This appears in the data as a gap in the dosing, which upon analysis would give the false impression that these last few doses alone contributed to the concentration measured in the sample. This situation typically occurs in studies in which a subject or patient is taking oral medication at home between visits. The remedy is to create supplemental dose records, hereinafter referred to as "ADDL records", to estimate the dosing which has occurred between visits but is not in the collected data | sample, in=, time variable, date variable, out= | 6 | |
| 76 | SAS Validation in the Pharmaceutical Industry | Edward D. Helton | pharmaceutical industry, data cleaning | 2 | ||
| 77 | SAS/STAT 9: Progressing into the Future | Robert N. Rodriguez, Maura E. Stokes |
SAS/ST A T 9 delivers a wealth of tools and function- ality for statistical modeling and data analysis. Areas covered by new or enhanced software include mul- tiple imputation, conditional logistic regression, ro- bust regression, general linear models for propor- tional hazards, regression diagnostics, survey data analysis, power and sample size analysis, statistical distance computations, statistical graphics, and par- allelization |
6 | ||
| 78 | Selecting Balanced Cohorts for Comparison in Observational Research | Craig Roberts | Assume you have a database that includes a population of patients who received one of two mutually exclusive treatment options. You desire to make comparisons between health outcomes of patients who received one treatment, the treatment group, with patients who received the other treatment, the control group. Also, you have a substantial pool of patients in the control group. You would like to select an adequately powered sample of patients from the control group who are similar with regard to demographic and clinical characteristics to patients in the treatment group. You begin with a SAS data set that contains clinical and demographic data for patients in the two cohorts. This program will create a dataset containing equal numbers of patients in the treatment and control groups who are similar with regard to clinical and demographic features prior to treatment | propensity score, proc step, set statement, logistic distribution, proc sort, sample, regression analysis, proc sql | 4 | |
| 79 | Sensitivity of DISCRIM PROC for Different List of Variables to Separate a Study Population by Treatment Subgroups in Clinical Trial with a New Antidepressant | Treatment Subgroups | Data were evaluated from a double blind, placebo-controlled, pilot clinical trial (N=52) with a new antidepressant. Discriminant analysis (PROC DISCRIM) was used to separate the drug- treated from placebo populations by treatment subgroups. As suggested by clinical psychiatrists, two different lists of variables were tested to check the sensitivity of discriminant analysis to the clinical assessments. The first list of variables in PROC DISCRIM included 7 primary and secondary clinical assessments for two time points: end of treatment and peak effect. The second list included just one primary clinical assessment for 7 time points from the first day of treatment to the end of observation. Separation with PROC DISCRIM was effective for both lists of variables with the overall correct classification by treatment subgroups from 80% to 83%. All of the methodological aspects and the SAS code can be used in some pivotal trials in the CNS therapeutic area |
discriminant analysis, proc discrim, proc step, clinical trial, sample | 6 | |
| 80 | Single Dose Bioassay - A SAS Approach | John Sparkes, Susan Motran |
In bioassays where one-sided limits of acceptance are used, a single-dose assay can be developed to replace the more costly and time-consuming multi-dose assay. This article outlines a method of converting from multi- to single-dose assays, using SAS procedures of GLM or PROBIT and FREQ. Using SAS statistical procedures, an | sample, proc step, regression analysis | 4 | |
| 81 | Sneak Peek: Previewing Blinded Study Results | Amy Rhoads Underwood | Frequently in clinical trials, one wants to know whether or not a study was successful before the data is completely collected and cleaned. For blinded trials, this means one wants results prior to unblinding the study. Early results may be important for many reasons, such as planning future studies, providing information to current or potential investors, or even determining whether the current study should continue as planned or be terminated before completion | change from baseline, interim analysis, confidence interval, randomization | 4 | |
| 82 | SQL for People Who Don't Think They Need SQL | Erin M. Christen | PROC SQL was added to the SAS system specifically to appease new SAS users who were already familiar with SQL language. Many programmers already familiar with SAS are hesitant to learn PROC SQL; after all, everything that can be done with PROC SQL can also be accomplished by the DATA STEP we already know. However, PROC SQL accomplishes some tasks more efficiently than the DATA STEP method. This paper covers the basics of SQL, and offers a few examples demonstrating the benefits of using |
proc sql, proc step, create table, in=, data step, group by, by group processing, inner join, full join, out=, where statement, nominal variable, macro variable, proc freq, select, merge statement, order by, right join | 8 | |
| 83 | Standardizing Data Processing and e-Publishing for the Pharmaceutical Industry | Shawn Wang | As timing and efficiency become ever more important for every company to secure a winning position in New Drug Development, after FDA requirements for e-Submissions after 2002, how are we going to get the job done quickly and accurately? Standardization is an effective generic solution applied in business re-engineering. Applying standard at every step will yield large time-compression that will result in more efficient use of available resources. This paper briefly discusses standardizing data process and e- Publishing in clinical trial and electronic submission, providing real-life examples to demonstrate the superiority of standardization processing over other approaches | raw data, data standard, dataset, clinical trial, data wrangling, macro definition, clinical research, response variable, pharmaceutical industry, format statement, excel, sas programming, proc step | 6 | |
| 84 | StARScope: A Web-Based SAS Prototype for Clinical Data Visualization | Fang Dong, Subrahmanyam Pilli, Scott Pivirotto, Jeff Van Domelen |
StARScope is a dynamic, clinical trial data visualization prototype. It was developed with SAS version 8.2, including SAS/INTRNET, SAS Graphics and SAS ODS, and is a web-based application. The web server is on a Unix machine and the user interface is Internet Explorer (5.0 or above) running on any desktop machine. In developing the prototype, we took advantage of the SAS/INTRNET and SAS Output Delivery System to publish graphic reports with hyperlinks to relevant data. We also utilized the newly developed Pfizer corporate data standard- -Global Reporting and Data Exchange Standards (GRADES), which follows the proposed pharmaceutical industry standards defined by the Clinical Data Interchange Standards Consortium (CDISC) group. This way, the prototype can be used by any clinical trial studies that comply with the GRADES. StARScope provides interactive access to clinical trial data to non- programmer clients, including clinicians, statisticians, and medical writers |
clinical trial, macro variable, data visualization, data standard, sas output, macro definition, clinical data, data cleaning, cdisc | 6 | |
| 85 | Statistical Processing and Analysis of Proteomic and Genomic Data | Mark Carpenter, Sreelatha Melath, Sijian Zhang, William E. Grizzle |
the training set. Their supervised classification algorithm yielded 100% correct classification on t he t raining s et (a res ult w e reproduced us ing t raditional s tatistical t echniques using SAS) with 95% specificity in the validation set. A ll processed data for this ex ample c an be dow nloaded from t he f ollowing website: http://clinicalproteomics.steem.com. Table 1 di splays sample points excerpted from the entire dataset comprising 218 variables (one variable representing m/z and 218 representing the unique serum samples). Figure 1 c ontains a s ample spectrum from two ovarian c ancer s ubjects' and t wo norm al s ubject's s erum proteomic profiles. Emerging microarray and prot eomic t echnologies bri ng m any interesting issues and c hallenges f or t he s tatistician | sample, data science, data wrangling, proc step, macro definition, training set | 4 | |
| 86 | Techniques to Create a Standard Folder Structure in a Windows NT/XP Share Area for Submission Projects and Their Protocols | Share Area | A well-organized environment with standard folder structure and implemented access control in the Windows NT/XP share area will benefit the submission team to conduct the statistical analysis and prepare the statistical portion of an electronic submission to the FDA. This paper presents the SAS techniques that can be used to create the standard folder structure in the Windows share area. These techniques include: interacting with the user during SAS session, checking the existence of a folder or file, controlling the program flow, issuing Windows commands and creating a shortcut for SAS program. Operation System: Windows NT/XP SAS Version: 8 | macro variable, proc calis, access control, error message, sysrc, macro flow control, infile statement, label statement, batch job, data science, macro quoting function, macro definition, %if-%then-%else | 6 | |
| 87 | Testing CDISC's Operational Data Model in SAS | Michael C. Palmer | CDISC recently released version 1-1-0 of its Operational Data Model (ODM v1-1-0) for the pharmaceutical and biotech industries. ODM uses the clinical trial's case report form (CRF) paradigm to represent clinical data and metadata in XML. Pre- release, pharma and vendor companies tested ODM's functionality with clinical data systems. This paper reports on the | clinical data, crf, clinical trial, cdisc | 4 | |
| 88 | The behind the Scenes of Extracting SAS Data Sets from Oracle Clinical | Angela S. Ringelberg | The first step in creating external applications related to statistical analysis and reporting is to extract clean data from a clinical database management system (DBMS). One such system is the Oracle Clinical system. Oracle Clinical converts study protocols that are different into a precise data collection and management system. Since much of the work to create one clinical study is identical or similar to the work required in other studies, the goal is to automate the entire process of data extraction from Oracle Clinical to SAS data sets. This paper presents an automated process of extracting the Oracle Clinical views into SAS data sets for analysis and reporting using a single SAS v8.2 program (_GETDATA.sas). The program is written so that it can be implemented for different clinical studies without the need of any custom modifications. The actual extraction of SAS data sets is simple. The ability to understand and use the Oracle Clinical tables to your advantage is the goal | proc step, proc sort, tables, create table, dataset, macro call, sysdate9, data step, proc sql, clinical data, clinical trial, label statement | 6 | |
| 89 | The Confidence in the 95% Confidence Interval of Mean | Bhargavi A. Raghavan | Different statistical procedures like PROC MEANS, |
confidence interval, proc step, proc means, degrees of freedom | 2 | |
| 90 | The SAS Data Step/Macro Interface | Lawrence Heaton-Wright | The SAS macro facility is an extremely useful part of the SAS System. However, macro variables and the macros themselves do not have to be exclusively referenced from within the macro facility. The SAS DATA STEP can create, resolve and execute macros using 2 functions: SYMGET and RESOLVE and 2 routines: CALL SYMPUT and CALL EXECUTE. This paper aims to demonstrate the usefulness and relative ease of two of these functions: The CALL SYMPUT routine allows the creation of SAS macro variables from within a SAS DATA STEP The CALL EXECUTE routine allows the execution of a SAS macro from within a SAS DATA STEP The audience for this paper does not require a high level of technical expertise but should be familiar with the workings of the SAS macro facility. The contents are not restricted to one particular operating system |
macro variable, data step, call symput, call execute, macro facility, macro definition | 4 | |
| 91 | The Use and Abuse of the Program Data Vector | Jim Johnson | Have you ever wondered why SAS does the things it does or why your programs "get away with" the things that they do or why SAS would not do what you wanted it to? A key operational component of SAS is the program data vector. Without it SAS would not function, as we know it. With knowledge of the program data vector, programmers can better understand how SAS works. This paper will help you understand how the program data vector works, how data steps use it, and how you can exploit, manipulate, and trick it | pdv, where statement, data step, keep statement, drop statement, set statement, dataset, if-then-else, subsetting if, rename statement, character variable, proc print, automatic variable, retain, proc step, missing value | 10 | |
| 92 | Tips to Enhance Your SAS Statistical Graphics Output | Shi-Tao Yeh | SAS/GRAPH/g226 software procedures produce several types of graphics output from a procedure. The procedures require a few statements to produce statistical graphics output. Statistical graphics illustrate trends and test results. Many things can be done to improve your SAS/G226 graphics output. This paper provides tips to enhance your SAS statistical graphics output with sample code and illustrations. The SAS products used in this paper are SAS BASE /g226 and SAS/GRAPH on UNIX platform |
sample, proc sgplot, proc step | 6 | |
| 93 | Tired of Defining Axis Scale for SAS Graphs? A Solution with an Automatic Optimizing Approach | Don (Dongguang) Li | SAS will automatically set the default axis scale for graphical output. However, the default axis scale is not ideal and many times the user has to define the scale by means of ORDER= option in AXIS statement. In the case that many graphs are created at the same time, defining axis scale for all the graphs is a hard and boring work. This article introduces an approach derived from the practical work that can automatically determine the optimized way of displaying the axis scale for SAS graphs with numeric axes. Key Words: SAS Graphs, Axis Scale, GPLOT procedure, Automation |
call symput, proc gplot | 4 | |
| 94 | To Wrap up an Extremely Long Comments Variable - Take Advantage of SAS ODS Template, Listing | Peter Jiang | The MS Access database provides users a capability of writing infinitely long comments in the "Memo" field. After the Access database is converted to a SAS data file, the field, "Memo" becomes a SAS character variable with width of over 1,000 characters. Each comment in the Memo can be very short or very long with no patterns among them. This paper presents a simple, efficient and flexible method to handle these free style comments. It uses the SAS ODS Template, ODS LISTING, to wrap these long comments to a certain length defined in the Template by programmer and then to output them as a text file through Listing. Finally it employs a _NULL_ DATA STEP to output these wrapped comments with other variables in an FDA submission document format |
txt, length statement, proc step, dataset | 4 | |
| 95 | Understanding ODS Styles: A Better Styles.Default | Christopher R. Olinger | Much of the complexity of the Output Delivery System centers around ODS styles. This tutorial will show you how to decompose Styles.Default (the default style in the system) to its basic building blocks and then create a new version that is easier to understand, more functional, and a snap to extend. You can use this version of Styles.Default to create your own ODS Styles and gain greater control over your SAS output |
proc step, file statement | 8 | |
| 96 | Use of Text Miner to Automatically Abstract Patient Information from the Pharmacy Order Database | Patricia B. Cerrito, Antonio Badia, John C. Cerrito, James A. Cox |
Objective: To demonstrate how text mining can be used to extract patient diagnosis information from a pharmacy order database when treatments are ordered without a corresponding diagnosis recorded in the patient chart thereby increasing the accuracy of data extracted for billing and external reporting | clinical database, clinical endpoint | 6 | |
| 97 | Using AppDev Studio and Integration Technologies for an Easy and Seamless Interface Between Java and Server-Side SAS | Barry R. Cohen | The application landscape has changed substantially for SAS application developers. Today's landscape uses a distributed-object, component-based architecture that integrates SAS server capabilities into enterprise-wide applications that are n-tiered, Web- enabled, and thin-client. SAS has developed new products to help you build SAS applications in this landscape. These include the AppDev Studio product suite including especially webAF within it, and Integration Technologies. You will likely want to leverage a few key features of these products as you build Web-enabled SAS applications. From AppDev Studio, these features are SAS-aware Java classes for Java programs, SAS Custom Tags for JavaServer Pages, and SAS Components for applets. From Integration Technologies, the feature is the Application Facility. These product features allow an easy and seamless interface between your client- or server-side Java applications and your server-side SAS programs and data | proc step, dataset, sas base | 10 | |
| 98 | Using Compute Blocks in Proc Report | Sharon Gonzalez | PROC REPORT is widely used to generate tables and listings. By incorporating the use of compute blocks within the PROC REPORT, we can utilize data null capability to get around some common challenges. For anyone still using a version of SAS 8.1 or earlier, there is a 10 line text limitation for titles and footnotes. Compute blocks will allow for as many lines of text as needed, as well as letting you create page specific titles and footnotes. They can also be a very useful way of specifying varying category titles within the report body, particularly if the data dictates which titles should be used. Another advantage of using compute blocks is to create the line 'Number of Patients Dosed' without having to add an observation to your dataset. You simply calculate macro variables for these N's and use a compute block to output the information into the report |
proc report, proc step | 4 | |
| 99 | Using Indexes to Perform Table Look-up | Sandra Lynn Aker | set statement, key=, proc step, automatic variable, error message, proc sql, proc sort, adverse events | 4 | ||
| 100 | Using Model Fitting Information and PROC PLOT to Select the Type of Variance-Covariance Structure for Repeated Measures | Radhi Abdulnabi | The statistical analysis of repeated measures data must address the issue of covariation between measures on the same unit, because it is usually plausible to assume that observations on the same unit are correlated. The selection of an appropriate type of covariance structure can substantially improve the efficiency of inferences made about the mean structure and provide better estimates of standard errors of estimated mean parameters. The REPEATED statement in the MIXED procedure provides a rich selection of covariance structures. Selecting the appropriate covariance structure is a major hurdle in the use of PROC |
covariance, covariance structure, repeated measures, proc step, proc mixed, likelihood function, macro variable, compound symmetry, longitudinal data, sample, ar(1), correlation | 8 | |
| 101 | Using RTF, EPS and PDFMARK to Automate the Creation of Your DEFINE.PDF Document for Electronic Submissions | Dirk Spruck, Aventis Behring |
The Food and Drug Administration's (FDA) guideline 'Providing Regulatory Submissions in Electronic Format' outlines a data definition table, the so-called DEFINE document. This PDF document consists of three parts 1) a list of all datasets with labels and location, 2) a variable definition table grouped by datasets and 3) a list of all variables in alphabetical order. This paper describes the technology used to create a DEFINE.PDF document complete with bookmarks, internal links and hyperlinks to external files. SAS is used to create an RTF file with all contents and structural document information, e.g. paper size and font color, including bookmarks and embedded Encapsulated Postscript (EPS) files. These EPS files contain PDFMARK language used by Acrobat to create external hyperlinks when the document is distilled to PDF | annotated crf, excel, macro call, dataset, concomitant medications, macro variable, proc step, data science, cdisc | 6 | |
| 102 | Using SAS Drug Development as a Report Management Application | Barry R. Cohen | Many statisticians and statistical programmers in the pharmaceutical industry will first come to know SAS Drug Development as a product that addresses their regulatory-compliance issues (auditing, versioning, and security) as they develop their on-going analysis programs, data, and documents for NDA filings. However, the product provides a full, flexible processing environment that can be used in other ways. In this paper, I examine standard features of SAS Drug Development that allow it to serve as a Web-enabled report management application for a library of SAS-based report programs. Such an application could cover typical functions such as report program loading; report parameter solicitation; report program selection and execution; and report output file viewing | clinical research, sas programmer, statistical programmer, dataset | 8 | |
| 103 | Using SAS Software and Visual Basic for Applications to Produce Microsoft Graph Charts | Lori S. Parsons | While SAS/GRAPH software produces high quality graphs for presentations, it is often desirable to provide Microsoft Graph charts. An analyst often produces statistics and graphs for an investigator. The investigator writes up the results and presents a talk accompanied by Microsoft PowerPoint slides or prepares a manuscript in Microsoft Word. A SAS graph can easily be inserted into PowerPoint or Word. However, if the investigator wants changes in the appearance of the graph, the analyst must re- write and re-run SAS code. By providing a Microsoft Graph chart, the investigator can modify the appearance. This paper describes a technique to automatically populate the data cells of a Microsoft Graph chart directly from SAS. This method can be used for any of the chart types in Microsoft Graph and use data from any SAS procedure. SAS 7.0 or higher, Microsoft Office 97 or higher, and knowledge of ODS and VBA are required |
dataset, proc logistic, macro definition, proc step, logistic distribution, macro variable, proc freq, predictor variable, logistic regression, output delivery system, confidence interval, macro call, insurance risk score, txt, regression analysis, roc curve | 10 | |
| 104 | Using SAS Software for a Numerical Approximation and the Area Under a Curve Calculation | Shi-Tao Yeh, Ellen Forman |
The trapezoidal rule and Simpson's rule are the numerical approximation methods to be used to approximate the integral or the area under a curve. The integration of [a, b] from a functional form is divided into n equal pieces, called a subinterval or trapezoid. Each subinterval is approximated by the integrand of a constant value. This paper provides four SAS /g226 macros to perform the area under a curve (AUC) calculation by the trapezoidal rule and Simpson's rule. The first macro allows you to select the number of subintervals from a continuous smooth curve and then performs the area under the curve calculation. The second macro allows you to select the accuracy level of the approximation and then performs the necessary iterations to achieve the required accuracy level of approximation. The third macro is for discrete points of ( x, y ) and performs the area calculation. The fourth macro uses Simpson's rule for the areae computation | macro definition, proc step, proc nlin | 6 | |
| 105 | Using SAS software for Multilevel Models in Survival Analysis | Gisela Tunes da Silva | Multilevel models have been used to analyze nested data to take into account the dependence structure that may be present. This is accomplished by including random coefficients in the model. In this work, we consider the multilevel framework adapted to common situations in Survival Analysis. Two approaches are discussed, one based on a parametric model (Weibull), and other on the semi parametric Cox proportional hazards model. Assuming that the random coefficients are normally distributed, we discuss how to use GLIMMIX macro to fit both models. We also present a macro to optimize the procedure and show how to use it in a real data example, based on an experiment with rats | likelihood function, proc mixed, poisson distribution, proc phreg, hazard function, survival analysis, covariance, response variable | 4 | |
| 106 | Using SAS to Evaluate Integrals and Reverse Functions in Power and Sample Size Calculations | Xingshu Zhu, Shuping Zhang |
We have recently created SAS codes for calculating integration and reverse function. The codes were conceived during the development of macro %SSEQ_BE, which is used to calculate power, sample size, and other parameters, including expected mean difference and equivalence limits. These SAS codes have been successfully executed in both SAS V6.12 and SAS V8.2 within the Windows 98 operating system. In this paper, we use the calculation of power and sample size as an example to demonstrate how SAS tools can be used to evaluate integrals and reverse functions. KEY WORDS: Integrals, Reverse Functions, Power Calculations, Sample Size Calculations | sample size, sample, power analysis | 6 | |
| 107 | Using SAS to Perform a Single-Stage Multiple Comparison Procedure for All Pair-wise Comparisons in a One-Way Layout with Unequal Variances | Paul Juneau | A one-way layout with three or more treatments is a common study design employed in basic drug discovery research for the analysis of continuous responses. When an investigator is interested in simultaneously comparing all pairs of three or more mean treatment responses, he/she often employs the Tukey-Kramer multiple comparison procedure. One of the chief assumptions necessary for the application of the Tukey-Kramer procedure is the presence of equal treatment response variances (homoscedasticity). Departures from this assumption (heteroscedasticity) can lead to the execution of the test under an inflated Type I family-wise error rate. Dunnett proposed a solution to this problem with the introduction of the T3-Procedure. The author will demonstrate an example of the problem of heteroscedasticity in a standard one-way layout. He will then offer a solution via the introduction of a simple SAS macro designed to perform the Dunnett T3-Procedure using the PROBMC function. | dataset, standard deviation, macro loop, macro variable, sample, proc step, macro definition | 6 | |
| 108 | Using symbol plots to represent symptom severity over time | Elisa Knutsen | Plotting different shaped and colored symbols to represent symptom severity over time allows the viewer to see a summary of the severity and frequency of symptoms experienced by individuals throughout a trial. By using an annotate DATA STEP after normalizing the data, programmers can easily create symbol plots to supplement or replace data listings that describe volunteer safety over time |
numeric variable, in= | 4 | |
| 109 | Version Comparison: A Powerful, Flexible and Easy Method | Vineet K. Mathur | How many times have you moved your programs from 'development' to 'production' and wondered if the output files they generate are identical to the ones from 'development'? How many times have some of the datapoints changed in your database and you needed to find and document what changed in the resulting statistical tables and listings? In situations like these there is a common need for a reliable 'version comparison software package'. Some version comparison packages may not be flexible and powerful enough for your needs. Also, sometimes it is hard to exclude useless information from printing on the final document. Here is a simple piece of code that can take care of your version comparison documentation. You can compare all the files in two directories in one go. The best part is that it is written in SAS and only requires Base SAS to run so you can alter the code anytime to fit your specific needs | proc sort, proc tabulate | 4 | |
| 110 | Web-based Information Management for Data Monitoring Committees | Mike Bodenstab | A Data Monitoring Committee (DMC) is a group of individuals with relevant expertise who review on a regular basis data accumulating from a clinical trial (or trials) with some predefined purpose. This paper discusses the use of a web-based system to manage DMC information. SAS is used to generate reports and Microsoft FrontPage is used to publish contents to the DMC web server. The advantages and disadvantages of a web-based system are discussed | clinical trial, sas output | 4 | |
| 111 | Why SAS is the Best Place to Put Your Clinical Data | Steven A. Wilson | The SAS System contains an incredible amount of functionality for managing your data files as a database. In most respects, including data storage, data access, and data management, SAS can be considered a database. This paper will review the database functionality available within the SAS System and how this relates to the task of clinical trails data management. Sample code, which illustrates many of these database techniques, is provided | audit trail, constraint, key, proc step, dataset, foreign key, proc sql, missing value, data integrity, clinical data, proc datasets, clinical trial, proc print, sas base, data governance, traffic data, options statement, adverse events | 10 | |
| 112 | XML? We do that! | Anthony L. Friebel | This paper is a tour of what's new in the development arenas for | libname statement, sas 9, sample, ods xml | 6 | |
| 113 | XPDL - An Extensible Project Database Loading and Table Translation Program | John H. Adams | XPDL is a SAS macro program that can combine and re-map data for any number of trials to create a drug project database (PDB) for project type of analysis . The program is actually driven by tables that describe the source-to-target data relationships (many-to-one) and any required re-mapping. These tables are initially created by XPDL and are then easily modified by the user with EXCEL. The tables not only provide the documentation of the PDB, but they are what drives the macro program for the 'LOADPDB' function. No custom programs are required! A large PDB that used to take several programmers 6+ months to develop and 1+ programmer to maintain, can now be implemented in a few weeks. Future changes to the PDB are done by non-programmers. The savings in time and quality are immense. XPDL can also be used re-map existing data sources into new layouts or table structures. This is called Table Translation |
sample, master data management, excel | 8 | |
| 114 | You be Your Own QC Cop - Check your .SAS, .LOG and .LST dates from Within SAS Using Operating System Commands | Venky Chakravarthy | The answer lies in the realm of the Operating system and its mechanism f or s toring f ile c reation and modification dates. The Inter nal Q uality Contr ol Depar tment tr ies to ensure that a quality product, which is fully compliant with internal SOPS, is delivered to the F DA. We as programmers are als o oc cupied w ith the quality of our pr oducts i.e., w hether they m eet the specifications of our inter nal c lients. W e c reate excellent code that pr oduces the des ired r esult. However, we may forget to ex ecute a piec e of code after making a cosmetic change in the end. This may mean that the .LO G and the .LST f ile predate the .SAS program file. This has potential for QC trouble | date and time functions, sas base | 4 | |
| 115 | You've Got Data! : Using SAS from Data Receipt to Reporting | Philip Vecchione | In a fast-paced, pharmaceutical data analysis environment, the transfer of data needs to be quick and accurate. A number of steps are involved in coordinating data transfers. Data must be received, processed, and verified; proof of receipt needs to be communicated to the sender; and the internal team needs to be updated about the newly arrived datasets. These steps can be time consuming, prolonging the time to analyze and submit data to the FDA | proc step, proc contents, proc report, data science, macro call, ods html, proc sql, dataset, data dictionary, proc print, study day, variable list, label statement, proc freq, output delivery system, control total, missing value | 8 |
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