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BUGSPharmaSUG 2016 Papers - Index
178 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | "I Want the Mean, But not That One!" | David W. Franklin | The 'Mean', as most SAS programmers know it, is the Arithmetic Mean. However, there are situations where it may necessary to calculate different 'means'. This paper first looks at different methods that are widely used from a programmer's perspective, starting with the humble Arithmetic Mean, then proceeding to the other Pythagorean Means, known as the Geometric Mean and Harmonic Mean, before ending with a quick look at the Interquartile Mean and its related Truncated Mean. During the journey there will be examples of data and code given to demonstrate how each method is done and output | numeric functions, geomean, harmean, sas log, outlier | source | 5 |
| 2 | "It is a standard, so it is simple, right?": Misconceptions and Organizational Challenges of Implementing CDISC at a CRO | Susan H.M. Boquist, Adam J. Sicard |
Implementing CDISC standards into the clinical trial process presents several challenges ranging from understanding the complexity of the task to whom should be doing which part of it but can yield many positive effects. There are many possible approaches, all with advantages and disadvantages. After experimenting with several different options, we have adopted a two team model of database build and "end to end" SAS. While the approach is relatively new, by beginning to include CDISC standards into both teams, we are starting to see anecdotal evidence of positive effects in terms of reduced timelines, increased quality of outputs and employee engagement | clinical programming, data review, raw data, sdtm programming, clinical trial, sas programming, sas base, data science, cdisc | source | 5 |
| 3 | -The car is in the shop but where are the mechanics?- The future of Standard Scripts for Analysis and Reporting | Dirk Spruck, Jeanina M. Worden |
The PhUSE/FDA Computational Science Standard Analyses Working Group has met its main goal: to establish a framework for standardizing analyses across the industry. Recent progress falls mainly in 3 areas: 1) The White Paper project has developed guidance on 9 data analysis topics, and has published 5 white papers. 2) The Infrastructure project has established a GitHub code repository. 3) The Content project has established implementation guidelines and a qualification process, and has published scripts that display standard measures of Central Tendency. The focus for 2016, and moving forward, is to promote adoption of these standard analyses; to coordinate review of, finalize and publish white papers; and to increase the scope, quality and usability of the corresponding R and SAS scripts. A framework for collaboration enables standard analyses, but it is just a starting point | clinical data, central tendency | source | 3 |
| 4 | A Data Preparation Primer: Getting Your Data Ready for Submission | Janet E. Stuelpner | Does your data conform to industry standards? Is it in the format that is necessary for submission to the regulatory authorities? Beginning next year, the FDA will require that submission data adhere to CDISC industry standards. All data will need to be in SDTM and ADaM standard format. Above and beyond that, there will be requirements to submit your data in very specific formats. What do you need to do to make sure that your data is in the correct format? The first thing to do is create procedures to know what to do and where to start. This presentation will point out some of the requirements and make sugg estions as to the things that you need to do to have a better chance to have your submission accepted and not sent back | missing value, character variable, cdisc | source | 3 |
| 5 | A Different Approach to Create Swimmer Plot Using Proc Template and SGRENDER | Jui-Fu Huang | Creating figures for statistical analysis of clinical trials are challenging when limited to specific SAS version and internal SOP. Previous work (Stacey Phillips, 2014; Sanjay Matange) has been done mainly with PROC SGPLOT. In this paper, I introduce a different way to create swimmer plot by using PROC TEMPLATE and PROC SGRENDER, to give additional information such as dose level, cancer type, or genetic type. Additionally, instead of marking "ongoing response" with annotation, I assign a subject-level format with PROC TEMPLATE function "BARLABELFORMAT" to simplify and automatically update output figures when receiving new data. This paper will demonstrate detailed steps of creating swimmer plot as well as compare the advantages and disadvantages of different methods |
swimmer plot, proc step, proc template | source | 6 |
| 6 | A Dose Escalation Method for Dual-Agent in Phase 1 Cancer Clinical Trial using the SAS MCMC Procedure | Gwénaël Le Teuff, Mohamed Amine Bayar |
Continual reassessment method (CRM) is a model-based dose escalation method commonly used to design a phase 1 trial in oncology when evaluating one agent. The main characteristics include the definition of a working model for dose levels, an acceptable level of toxicity (target) and a model defining the dose-toxicity relationship (for example, power or logistic function). This relationship is updated after the toxicity evaluation of each patient in the cohort and we assign to the next cohort the dose level closest to the target. This allows estimating the maximum tolerated dose (MTD). With the advance of targeted therapies era in oncology, more and more phase 1 trials aim to identify one or more MTDs from a set of available dose levels of two or more agents. Combining several agents can indeed increase the overall anti-tumor action but at the same time increase toxicity | phase i, dose escalation, dose, clinical trial, macro definition, proc step | source | 14 |
| 7 | A Macro to Automatically Flag Baseline in SDTM | Taylor Markway | The derivation of the baseline flag in SDTM is a good candidate for using a standard macro because 1) SDTM allows for a generic definition of baseline and 2) baseline derivations can be broken down into a few simple steps. The macro presented in this paper translates the steps for derive baseline into a set of macro parameters. It also provides an option to use SDTM to our advantage by automatically determining the correct parameters, instead of manually determining parameters that should be passed to the macro. By leveraging SDTM, the user is only required to provide input and output data sets | macro variable, sdtm domain, cdisc | source | 8 |
| 8 | A Practical Approach to Re-sizing Character Variable Lengths for FDA Submission Datasets (both SDTM and ADaM) | Xiangchen (Bob) Cui, Min Chen |
FDA issued Study Data Technical Conformance Guide [1] in October 2015, which stipulates "The allotted length for each column containing character (text) data should be set to the maximum length of the variable used across all datasets in the study". FDA/PhUSE 'Data Sizing Best Practices Recommendation' suggests optimizing the size of dataset through managing character variable length to save wasted space. OpenCDISC has built the checks for the compliance. Re-sizing character variable length from the pre-determined in SDTM and ADaM to the maximum length of the variable on the actual data values is the common solution to be compliant with FDA rule. Some sponsors resize character variable length at post database lock for FDA submission to solve the challenge of identifying or predicting up front the longest potential value for each character variable, to avoid risk of truncation | character variable, adam dataset, length statement, sdtm domain, dataset, macro call, database lock, sdtm programming, cdisc | source | 19 |
| 9 | A Review of "Free" Massive Open Online Content (MOOC) for SAS Learners | Kirk Paul Lafler | Leading online providers are now offering SAS users with "free" access to content for learning how to use and program in SAS. This content is available to anyone in the form of massive open online content (or courses) (MOOC). Not only is all the content offered for "free", but it is designed with the distance learner in mind, empowering users to learn using a flexible and self- directed approach. As noted on Wikipedia.org, "A MOOC is an online course, content, or other resource aimed at unlimited participation and made available in an open access forum using the web." This presentation illustrates how anyone can access a wealth of learning technologies including comprehensive student notes, instructor lesson plans, hands-on exercises, PowerPoints, audio, webinars, and videos | sas base, sas studio, sample | source | 16 |
| 10 | A SAS Macro Tool to Automate Generation of Customized Patient Profile in PDF Documents | Haining Li, Merit E. Cudkowicz, Hong Yu |
Clinical trials data are collected from many different sources and the data are often stored in many SAS tables while each SAS table includes multiple patients. Once the trial begins, all of the data needs to be reviewed and cleaned in real time. To effectively monitor study progress and subject safety, patient profiles are commonly used in this review process. These profiles will contain all current data for each patient that will allow reviewers to rapidly assess the patient's overall status as well as any level of detail information. To reduce the effort in organizing the huge volumes of tables and variables within each clinical trial, this paper will provide a SAS Macro to automatically drop, keep or rename variables from all or some of the tables, and generate the ultimate patient profile by subject in PDF documents. This macro also provides options to automatically drop any column when all the data in the column are missing or equal to certain values | clinical trial, macro definition, dataset | source | 6 |
| 11 | A SAS Macro Tool to Automate Generation of Define.xml V2.0 from SDTM Specification for FDA Submission | Min Chen, Xiangchen (Bob) Cui |
A define-xml file is required to be submitted in FDA electronic submission, in addition to SDTM and ADaM datasets. An insufficiently documented define.xml frequently arouses a common complaint from FDA reviewers. Compared to define.xml Version 1.0, define.xml version 2.0 is a more powerful and user-friendly tool. An SDTM programming specification spreadsheet provides the SDTM mapping rules and derivation rules for SDTM programming and QC, which can naturally serve as a define file. In this paper, the standard SDTM specification spreadsheets were re-designed for define.xml V2.0 new features, which provided complete details for derived variables. A metadata-driven SAS macro tool was developed to automate the creation of define.xml V2.0 from CDSIC SDTM specification spreadsheets, ensure the consistency of the two files, and achieve technical accuracy and operational efficiency | controlled terminology, where statement, macro definition, excel, cdisc controlled terminology, dataset, sdtm domain, sdtm programming, sample, cdisc | source | 24 |
| 12 | Access OpenFDA: A Cloud Based Big Data Portal Using SAS | Jie Roger Zhou, James Sun |
With the rise of data sharing and cloud data porta, big dada and analytics are gradually being adopted across pharmaceutical industry. It made first impact on part of business such as sales and marketing which data have readily available. Now big data analytic gradually reaching the core of pharma business- R&D. One such example is newly emerging big data OpenFDA. As FDA collects all new drug submissions and pharmacovigilance data, its importance should not be underestimated. The paper briefly introduced OpenFDA, and its API when used in extracting data. We explore ways of handling such online data in JSON format with SAS data steps and procedures. We compare the strength of different methods and discuss some information Keyword: OpenFDA, Big Data, Pharmacovigilance, JSON, PROC HTTP, PROC DS2, PROC JSON, PROC GROOVY |
proc step, proc groovy, big data, proc http, data step, dataset | source | 8 |
| 13 | Achieving Clarity through Proper Study Documentation: An Introduction to the Study Data Reviewer's Guide (SDRG) | Michael Stackhouse, Terek J. Peterson |
With the ever-growing standardization requirements of FDA submissions, it can be difficult to understand where exactly you place non-conformant, essential information that does not have a home within other submission documentation. Now, with the Study Data Reviewer's Guide (SDRG), this information has a home and can be effectively communicated. The SDRG provides reviewers with key details they will need to perform a thorough review of the data, along with a space to answer questions from the reviewer before they are asked, expediting the review of the full documentation package of your trial. PhUSE has provided a step-by-step template that helps you understand what to report, where to report it, and makes sure nothing is lost so that traceability is clear. This paper will help you understand why it is important to think about the SDRG from the start, how to use these documents to your advantage, and provide examples of their flexibility and utilization | data standard, data cleaning | source | 5 |
| 14 | Adding Statistics and Text to the Panel Graphs using INSET option in PROC SGPANEL | Ajay Gupta | The SGPANEL procedure creates a panel of graph cells for the values of one or more classification variables. Business users often request to add a text or statistics to each panel cell of the graph. Normally, this task can be accomplished using Graph Template Language (GTL). But, the GTL approach require s a good amount of programming time and can be tedious. In SAS v9.4, the INSET statement is added in PROC SGPANEL. Using the INSET statement, text and statistics can be added in the panel graph very easily. This paper will introduce the INSET statement in the SGPANEL procedure and provide a brief overview of a ll options (with examples) which can be used in the INSET statement to enhance the appearance of text and statistics in the panel graph |
proc sgpanel, proc step, proc means, histogram, box plot | source | 6 |
| 15 | AE: An Essential Part of Safety Summary Table Creation | Rucha Landge | Adverse event (AE) summary tables are an imperative part of the study and depict a clear picture of safety of a drug to the patients. Hence, it is very essential that we are clear and very careful while creating these tables and displaying correct counts. This pa per will illustrate some basic conc epts of the ADaM ADAE dataset and common AE tables created in the study and provide some quick tips to self-validate and cross-check the counts that we display | adverse events, system organ class, adam dataset, proc step, proc sql, verbatim term, medical history, proc freq, adverse event summary, cdisc | source | 12 |
| 16 | All Aboard! Next Stop is the Destination Excel | William E. Benjamin Jr. | Over the last few years both Microsoft EXCEL file formats and the SAS interfaces to those EXCEL formats have changed. SAS has worked hard to make the interface between the two systems easier to use. Starting with "Comma Separated Variable" files and moving to PROC IMPORT and PROC EXPORT, LIBNAME processing, SQL processing, SAS Enterprise Guide, JMP, and then on to the HTML and XML TAGSETS like MSOFFICE2K, and EXCELXP. Well, there is now a new entry into the processes available for SAS users to send data directly to EXCEL. This new entry into the ODS arena of data transfer to EXCEL is the ODS destination called EXCEL. This process is included within SAS ODS and produces native format EXCEL files for version 2007 of EXCEL and later. It was first shipped as an experimental version with the first maintenance release of SAS 9.4. This ODS destination has many features similar to the EXCELXP TAGSETS |
excel, proc step, xls, output delivery system, by group processing, ods excel | source | 15 |
| 17 | An Intersection of Pharma and Medical Devices - Development of Companion Diagnostics in Conjunction with Targeted Therapies | Carey G. Smoak | The intersection of pharma and devices is growing. For example, devices can be used to deliver drugs, e.g., drug- eluting heart stents. A growing area of drugs and devices is targeted therapies and companion diagnostics. This means that a subject who tests positive for a gene mutation (the companion diagnostic test) will receive the study drug while subjects who test negative for a gene mutation test will not receive the study drug. The clinical validation of the companion diagnostic test is done in conjunction is done during the conduct of a pharma clinical trial (Phase II and/or III). Thus collection of data on screen failures (subjects who test negative) is necessary for calculation of sensitivity and specificity, e.g., using SAS PROC FREQ. Sensitivity and specificity are key analyses for companion diagnostic tests. This paper will demonstrate the clinical development of a companion diagnostic test during testing of a drug in Phase II and/or III |
clinical trial, data cleaning, clinical data | source | 4 |
| 18 | Annotating Graphs from Analytical Procedures | Warren F. Kuhfeld | Y ou can use annotation, modify templates, and change dynamic variables to customize graphs in SAS software. Standard graph customization methods include template modification (which most people use to modify graphs that analytical procedures produce) and SG annotation (which most people use to modify graphs that procedures such as PROC SGPLOT produce). However, you can also use SG annotation to modify graphs that analytical procedures produce. Y ou begin by using an analytical procedure, ODS Graphics, and the ODS OUTPUT statement to capture the data that go into the graph. Y ou use the ODS document to capture the values that the procedure sets for the dynamic variables, which control many of the details of how the graph is created. Y ou can modify the values of the dynamic variables, and you can modify graph and style templates |
sg annotation, kaplan-meier plot, call execute, proc step, macro definition | source | 18 |
| 19 | Are You Thinking About Becoming an Independent Contractor? Things to Consider as you Plan for Your New Entrepreneurship | Kathy Bradrick, Dawn Edgerton |
Have you contemplated starting your own business as an independent contractor? There are many things to consider such as financial planning, establishing a business infrastructure, and support activities you will need such as IT. This paper will explore the many items to consider as you make your decision | account, clinical research | source | 4 |
| 20 | Array of Sunshine: Casting Light on Basic Array Processing | Nancy Brucken | An array is a powerful construct in DATA STEP programming, allowing the application of a single process to multiple variables simultaneously, without having to resort to macro variables or repeat code blocks. Arrays are also useful in transposing data sets when PROC TRANSPOSE does not provide a necessary degree of control. However, they can be very confusing to less-experienced programmers. This paper shows how arrays can be used to solve some common programming problems |
array, proc transpose, vital signs, proc step | source | 5 |
| 21 | Associated Persons Domains - Who? What? Where? When? Why? How? | Alyssa M. Wittle, Michael Stackhouse |
Many types of clinical studies collect information on people other than the person in the study. Family medical history - MH or a custom domain? What about organ donor data? Care-giver information? Until now we have there was no home in the CDISC SDTM structure to standardize this information. With the publication of the Associated Persons SDTM IG this standardization has arrived! However, since it is so new, many don't know that it exists and/or how to apply it. This presentation will explore the Associated Persons SDTM Implementation Guide by exploring six investigational questions to make the application of the AP SDTM IG very logical and even easy. During the presentation we will discuss "What" the Associated Person IG is and for "Who" are these domains intended? "When" do these domains apply in a study and "Where" do I put all the information? One of the biggest questions that will be discussed is "How" to apply the information from the IG | medical history, adverse events, clinical trial, cdisc | source | 6 |
| 22 | Automatic Consistency Checking of Controlled Terminology among SDTM Datasets, Define.xml, and NCI/CDISC Controlled Terminology for FDA Submission | Min Chen, Xiangchen (Bob) Cui |
In FDA electronic submission, the most current version of NCI/CDISC controlled terminology for SDTM variables was expected to be submitted in define-xml. With the large amount of controlled terminology and the occasional update of NCI/CDISC controlled terminology, the controlled terminology applied in the SDTM programming was often out-of- date, and subject to the OPENCDISC report. It is desirable to ensure the consistency among SDTM datasets, define.xml, and NCI/CDISC controlled terminology to achieve the technical accuracy. In this paper we created a library of controlled terminology in SDTM specifications spreadsheet which contains both standard NCI/CDISC controlled terminology and sponsor-defined controlled terminology. A SAS macro tool was applied to automate consistency-checking of controlled terminology between SDTM datasets and define.xml | controlled terminology, sdtm domain, cdisc controlled terminology, sdtm programming, dataset, macro definition, clinical trial, cdisc | source | 13 |
| 23 | Automating Patient Narratives - The Medical Writer Loves Me! | Scott Burroughs | medical history, proc step, concomitant medications, proc report | source | 7 | |
| 24 | Becoming a more efficient programmer with SAS Studio | Max Cherny | SAS Studio is a web-based interface to SAS. It is similar to PC-SAS; however, SAS Studio provides additional features to make SAS programming easier. These features include auto-generation of SAS code and output, automatic formatting of SAS code, syntax checking, and better ways of handling SAS errors and warnings. SAS studio is even capable of predicting and suggesting the next word a SAS user may wish to type. SAS Studio makes it easier to run, maintain and document SAS programs with Process Flows feature. This paper describes how a SAS user can become more productive with SAS Studio | sas studio, sas syntax, line chart | source | 9 |
| 25 | Best Practice Programming Techniques for SAS Users | Kirk Paul Lafler, Mary F. O. Rosenbloom |
It's essential that SAS users possess the necessary skills to implement "best practice" programming techniques when using the Base-SAS software. This presentation illustrates core concepts with examples to ensure that code is readable, clearly written, understandable, structured, portable, and maintainable. Attendees learn how to apply good programming techniques including implementing naming conventions for datasets, variables, programs and libraries; code appearance and structure using modular design, logic scenarios, controlled loops, subroutines and embedded control flow; code compatibility and portability across applications and operating platforms; developing readable code and program documentation; applying statements, options and definitions to achieve the greatest advantage in the program environment; and implementing program generality into code to enable its continued operation with little or no modifications | proc step, if-then-else, options statement, proc print, subsetting if, dataset, proc sql | source | 10 |
| 26 | Best Practices: Subset Without Getting Upset | Mary F. O. Rosenbloom, Kirk Paul Lafler |
You've worked for weeks or even months to produce an analysis suite for a project, and at the last moment, someone wants a subgroup analysis and they inform you that they need it yesterday. This should be easy to do, right? So often, the programs that we write fall apart when we use them on subsets of the original data. This paper takes a look at some of the best practice techniques that can be built into a program at the beginning, so that users can subset on the fly without losing categories or creating errors in statistical tests. We review techniques for creating tables and corresponding titles with by-group processing so that minimal code needs to be modified when more groups are created, and we provide a link to sample code and sample data that can be used to get started with this process | proc step, proc means, proc sql, proc format, macro variable, proc freq, standard deviation, class variable, subgroup analysis, missing value, proc report, proc tabulate, by group processing, out=, macro loop, do loop, where statement, nominal variable, sample | source | 17 |
| 27 | Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code | Joshua M. Horstman | Nearly every SAS program includes logic that causes certain code to be executed only when specific conditions are met. This is commonly done using the IF…THEN…ELSE syntax. In this paper, we will explore various ways to construct conditional SAS logic, including some that may provide advantages over the IF statement. Topics will include the SELECT statement, the IFC and IFN functions, the COALESCE function, as well as some more esoteric methods, and we'll make sure we understand the difference between a regular IF and the %IF statement in the macro language |
if-then-else, ifn, select, sas programmer, macro definition, subsetting if, whichn, %if-%then-%else, missing value, proc format, ifc, when, proc step, coalesce function | source | 6 |
| 28 | Breaking up (Axes) Isn't Hard to Do: A Macro for Choosing Axis Breaks | Alexandra Buck | SAS 9.4 brought some wonderful new graphics options. One of the most exciting is the addition of the RANGE option for SGPLOT. As the name suggests, specifying ranges for a broken axis is controlled by the user. The only question left is where to set the breaks and if a break is actually needed. That is what this macro is designed to do. The macro analyzes the specified input parameter to create macro variables for the overall minimum and maximum, as well as macro variables specifying values prior to and following the largest difference that occurs between successive parameter values. The macro will also create variables for suggested break values to ensure graphic items such as markers are displayed in full. The user then utilizes these macro variables to determine if an axis break is needed and where to set those breaks. With the macro's dynamic nature it can be incorporated into larger graphics macro programs easily while making specific recommendations for each individual parameter |
macro variable, scatter plot, proc sql, %if-%then-%else | source | 6 |
| 29 | Building a Better Dashboard Using Base SAS Software | Kirk Paul Lafler, Roger D. Muller, Joshua M. Horstman |
Organizations around the world develop business intelligence dashboards, sometimes referred to as enterprise dashboards, to display the current status of "point-in-time" metrics and key performance indicators. Effectively designed dashboards extract real-time data from multiple sources for the purpose of highlighting important information, numbers, tables, statistics, metrics, performance scorecards and other essential content on a single screen. This presentation explores the basic rules for "good" dashboard design, the metrics frequently used in dashboards, and the use of best practice programming techniques in the design of highly interactive, filterable, and drill-down dashboards using Base-SAS software | proc step, bar chart, vertical bar chart, proc format, if-then-else, dataset, proc template, proc print, ods html, footnote statement, proc sgpanel, user-defined format, histogram | source | 17 |
| 30 | Building and Using User Defined Formats | Arthur L. Carpenter | Formats are powerful tools within the SAS System. They can be used to change how information is brought into SAS, how it is displayed, and can even be used to reshape the data itself. The Base SAS product comes with a great many predefined formats and it is even possible for you to create your own specialized formats. This paper will very briefly review the use of formats in general and will then cover a number of aspects dealing with user generated formats. Since formats themselves have a number of uses that are not at first apparent to the new user, we will also look at some of the broader application of formats. Topics include; building formats from data sets, using picture formats, transformations using formats, value translations, and using formats to perform table look-ups | proc format, proc step, char., character variable, put statement, where statement, sysdate9, sas programmer, proc tabulate | source | 11 |
| 31 | Building Efficiency and Quality in SDTM Development Cycle | Kishore Pothuri, Bhavin Busa |
The typical cycle for generating compliant CDISC SDTM datasets is to develop mapping specifications, programming and QC of the domains, followed by checking these domains for compliance issues per the FDA requirements. In some cases, the compliance checks of the SDTM domains take place towards the end of the study (i.e. closer to or after the database lock). This seems like a logical approach to the programming of the datasets, however checking for compliance and addressing issues identified by Pinnacle 21 validator once the programming is complete could result in re-work. A programmer will come across three possible scenarios: 1) compliance issues that will require programmer to make update to their dataset programs, 2) compliance issues that will require update to the dataset mapping specifications, SDTM aCRF, and to their dataset programs, and 3) compliance issues that will not require any update but will need explanation in the SDRG | sdtm domain, data cleaning, data validation rules, clinical trial, cdisc | source | 6 |
| 32 | Capturing Macro Code when Debugging in the Windows Environment: The Power of MFILE and the Simplicity of Pasting | Kevin R. Viel | Macros are ubiquitous in a programmer's toolkit. During development, especially of long or complex or even simple macros, deciphering warnings and errors can be problematic. Copying and editing the log is resource intensive and subject to errors. The goal of this paper is to demonstrate a macro that captures the SAS code produced by a macro using the MFILE system option and allows the user to simply paste that code in the Windows environment to an Enhanced editor for review and interactive submission or %INCLUDE it, thus making possible the tracking of errors and warnings to their exact line number in the resulting log |
macro parameter, macro call, options statement, cpu time, macro facility, macro definition, macro quoting function, data step, binary variable, macro variable | source | 10 |
| 33 | Career Path for SAS Profession in Pharmaceutical Industry | James Meiliang Yue | This paper outlines career path for SAS professional in the pharmaceutical industry. Provides knowledge and skills preparation guidance in career development. Younger generations entered into the job market used to ask me: I am here after I finished a degree in quantitative field. I had SPSS or R in the course work. I learned SAS and I want to be in the pharmaceutical, biotech or healthcare industry. How is the career picture? What is the path for career advancement? What kind of experience should I have? From the organization perspective, SAS skills are useful in many departments. With a Bachelor's degree, jobs like data management and SAS programming are good start. The position allows you be familiar with the data and structure, access and process data, and make tables, listings and graphics. | pharmaceutical industry, data science | source | 2 |
| 34 | CDISC Standards End-to-End: Transitional Hurdles | Alyssa M. Wittle, Christine McNichol, Antonio Cardozo |
"Plus", "-like", "-ish" - We have all heard it in some variation: SDTM…plus, ADaM…ish, CDISC…like. I t is evident there are still some things preventing us from accepting pure CDISC. Many companies find the transition to CDISC difficult for a variety of reasons. They enjoy a "CDISC+" philosophy and believe it is "compliant enough" to work. The types of changes to the standards might be adding or ignoring controlled terminology, changing the definitions of CDISC specific variables, adding non-CDISC compliant variables, and only using CDISC standards for some datasets in a study, but not for all datasets. This presentation discusses common challenges encountered while a company transitions onto CDISC. The pitfalls of the "CDISC+" design will be discussed in depth. Conversely, the pros of what having a fully CDISC - and CDASH - compliant database will also be covered | controlled terminology, proc freq, cdisc | source | 6 |
| 35 | Change Management: The Secret to a Successful SAS Implementation | Gregory S. Barnes Nelson | Whether you are deploying a new capability with SAS or modernizing the tool set that people already use in your organization, change management is a valuable practice. Sharing the news of a change with employees can be a daunting task and is often put off until the last possible second. Organizations frequently underestimate the impact of the change, and the results of that miscalculation can be disastrous. Too often, employees find out about a change just before mandatory training and are expected to embrace it. But change management is far more than training. It is early and frequent communication, an inclusive discussion, encouraging and enabling the development of an individual, and facilitating learning before, during, and long after the change. This paper not only showcases the importance of change management but also identifies key objectives for a purposeful strategy. We outline our experiences with both successful and not so successful organizational changes | source | 20 | |
| 36 | Clinical Graphs Using SAS | Sanjay N. Matange | Graphs are essential for many clinical and health care domains, including analysis of clinical trials safety data and analysis of the efficacy of the treatment, such as change in tumor size. Creating such graphs is a breeze with procedures from SAS 9.4 ODS Graphics. This paper shows how to create many industry -standard graphs such as Lipid Profile, Swimmer Plot, S urvival Plot, Forest Plot with Subgroups, Waterfall Plot, and Patient Profile using Study Data Tabulation Model (SDTM) data |
proc sgplot, kaplan-meier plot, forest plot, swimmer plot, adverse events, scatter plot, hazard ratio, waterfall chart, box plot, confidence interval, proc step, cdisc | source | 19 |
| 37 | Codelists Here, Versions There, Controlled Terminology Everywhere | Shelley Dunn | Programming SDTM and ADaM data sets for a single study based on a one quarterly version of NCI Controlled Terminology (CT) can give a false sense that implementing CT correctly is a straight forward process. The most complex issues for a single study may rely on generating sufficient extensible codelists and ensuring all possible values on a CRF are accounted for within the study metadata. However, when looking beyond a single study towards developing processes to support end to end management, the maintenance of controlled terminology involves more complex processes requiring ongoing upkeep, compliance checking, archiving, updating, and governance. To ensure FDA compliance, it behooves sponsors to develop processes for maintenance and organization of CT. It is not enough to hire an external vendor and hope they apply CT correctly and/or to assume the CT used is correct based on a clean vendor compliance report | controlled terminology, adverse events, cdisc controlled terminology, cdisc | source | 12 |
| 38 | Color, Rank, Count, Name; Controlling it all in PROC REPORT | Arthur L. Carpenter | Managing and coordinating various aspects of a report can be challenging. This is especially true when the structure and composition of the report is data driven. For complex reports the inclusion of attributes such as color, labeling, and the ordering of items complicates the coding process. Fortunately we have some powerful reporting tools in SAS that allow the process to be automated to a great extent. In the example presented in this paper we are tasked with generating an EXCEL spreadsheet that ranks types of injuries within age groups. A given injury type is to receive a constant color regardless of its rank and the labeling is to include not only the injury label, but the actual count as well. Of course the user needs to be able to control such things as the age groups, color selection and order, and number of desired ranks |
proc step, proc report, proc format, excel, proc rank | source | 9 |
| 39 | Combining TLFs into a Single File Deliverable | William Coar | In day-to-day operations of a Biostatistics and Statistical Programming department, we are often tasked with generating reports in the form of tables, listings, and figures (TLFs). A common setting in the pharmaceutical industry is to develop SAS code in which individual programs generate one or more TLFs in some standard formatted output such as RTF or PDF. As trends move towards electronic review and distribution, there is an increasing demand for producing a single file as the deliverable rather than sending each output individually. Various techniques have been presented over the years, but they typically require post-processing individual RTF or PDF files, require a knowledge base beyond SAS, and may require additional software licenses. The use of item stores as an alternative has been presented more recently for TLFs [6][7][8]. Using item stores for PROC REPORT or the SG procedures, SAS stores the data and instructions used for the creation of each report |
proc step, proc report, sample, adverse events, pharmaceutical industry | source | 15 |
| 40 | Compilation of Errors, Warnings and Notes! | Anusuiya Ghanghas, Houde Zhang, Rajinder Kumar |
Since SAS has been leading as a promising tool in clinical industry, people become more fancy and positive to understand background play of how SAS works, then errors, warnings and notes are no more to see like technical red flags or loopholes of system, rather they became teachers to beginners or intermediate programmers. It has been proved in many cases that knowing your log helps you to improve basic understanding of SAS than your typical way of learning any tool, it is palpable that most expert SAS programmers admitted that remedial learning is best way of gaining expertise to improve further to write optimized programming, so this paper tries to touch sensible errors, warnings and notes that are really worth of knowing to make your work technically beautiful. Errors, warnings and notes are part and parcel of programming. But for someone who is new to programming th ese unwanted messages in log can prove a big headache | missing value, error message, numeric variable | source | 7 |
| 41 | Conformance, Compliance, and Validation: An ADaM Team Lead-s Perspective | John K. Troxell | Conformance, compliance, and validation checks have become a hot topic. In 2008 as I became ADaM Team Lead, I observed that SDTM computer validation checks had not been developed by CDISC. I believe that CDISC alone should define what constitutes CDISC compliance and, specifically, the algorithms for testing those aspects of compliance that are checkable by computer. Compliance rules, in my view, are naturally part of the standard. The ADaM Team published the first version of the ADaM validation checks in 2010, based on the ADaM Implementation Guide published in 2009. Updated versions have been released to reflect subsequent ADaM publications. However, all is not yet perfect. This paper provides a suggested map of the space comprising various aspects of regulatory agency conformance, CDISC compliance, and computer validation checks. To facilitate discussion, the paper also attempts to define vocabulary to refer to various components of the space | error message, data cleaning, cdisc | source | 7 |
| 42 | Consider Define.xml Generation during Development of CDISC Dataset Mapping Specifications | Vara Prasad Reddy Sakampally, Bhavin Busa |
As per the submission data standards set by the FDA for the new drug application, the sponsor has to provide a complete and informative define.xml as part of the dataset submission packet along with other required components. FDA specifies to submit the metadata information of a submitted dataset as per the CDISC Define-XML file standard because of its virtue of both machine and human readable properties. Most sponsors consider generating or receiving define document from their vendor during the final steps of dataset submission. There are multiple papers discussing different approaches to create the define.xml file. In this paper, we are presenting an approach where we have used Pinnacle21 validator to generate define.xml during the specification and dataset development phase. This paper also provides an insight on using Pinnacle21 validator as a tool with a constructive approach to generate define.xml and validation of the datasets during the SDTM development lifecycle | sample, sdtm domain, data cleaning, cdisc | source | 6 |
| 43 | Considerations and Conventions within the Therapeutic Area User Guides (TAUGs) | Jerry Salyers, Kristin Kelly, Fred Wood |
One of the major initiatives in the pharmaceutical industry involving standards is the development of Therapeutic Area User Guides (TAUGs) under the CFAST (Coalition for Accelerating Standards and Therapies) initiative. CFAST is a collaborative effort among CDISC, with participation from TransCelerate Biopharma, Inc,, FDA, and the National Institute of Health. Currently, there are seventeen TAUGs (three at Version 2, fourteen at Version 1) available for download from the CDISC website. There are also a number currently in development that are moving towards public review and eventual publication. The development of these standards includes the design of case report forms and resulting metadata using the CDASH standard, the mapping of the collected data to SDTM-based datasets, and examples of how the SDTM-based data would be used in the production of ADaM (analysis) datasets | clinical trial, clinical research, cdisc | source | 8 |
| 44 | Controlling Colors by Name; Selecting, Ordering, and Using Colors for Your Viewing Pleasure | Arthur L. Carpenter | Within SAS literally millions of colors are available for use in our charts, graphs, and reports. We can name these colors using techniques which include color wheels, RGB (Red, Green, Blue) HEX codes, and HLS (Hue, Lightness, Saturation) HEX codes. But sometimes I just want to use a color by name. When I want purple, I want to be able to ask for purple not CX703070 or H03C5066. But am I limiting myself to just one purple? What about light purple or pinkish purple. Do those colors have names or must I use the codes? It turns out that they do have names. Names that we can use. Names that we can select, names that we can order, names that we can use to build our graphs and reports. This paper will show you how to gather color names and manipulate them so that you can take advantage of your favorite purple; be it 'purple', 'grayish purple', 'vivid purple', or 'pale purplish blue'. Much of the control will be obtained through the use of user defined formats | proc step, proc format, txt, dataset, proc report, excel, proc sql | source | 11 |
| 45 | Cox proportional hazards regression to model the risk of outcomes per double increase in a continuous explanatory variable | Seungyoung Hwang | The Cox proportional hazards model to explore the effect of an explanatory variable on survival is by far the most popular and powerful statistical technique. It is used throughout a wide variety of types of clinical studies. If the explanatory variable is continuous, the hazard ratio per 1-unit of change in the continuous explanatory variable is estimated by default in the PHREG procedure in SAS . However, the estimates may not reflect a clinically meaningful change, especially for continuous and highly dispersed measurement. This paper introduces the hazard ratio per 'double' increase in continuous covariate of interest as another tool for comparing the two hazards. The author is convinced that this paper will be useful to any level of statistician, SAS programmer, or data analyst with an interest in medical follow -up studies and in general time-to-event studies |
hazard ratio, predictor variable, hazard function, proc phreg, regression analysis | source | 4 |
| 46 | Creating Define-XML version 2 including Analysis Results Metadata with the SAS Clinical Standards Toolkit | Lex Jansen | Analysis Results Metadata provide traceability for a given anal ysis result to the specific Analysis Dataset Model (ADaM) data that were used as input to generating the analysis result; they also provide information about the analysis method used and the reason the analysis was performed. Analysis Results Metadata will assist the reviewer by identifying the critical analyses, providing links between results, documentation, and datasets, and documenting the analyses performed. This paper describes the Analysis Results Metadata extension 1.0 for Define-XML version 2, and also, how the SAS Clinical Standards Toolkit 1.7.1 can be used to create Define-XML version 2.0 documents including Analysis Results Metadata. This paper assumes that the reader is familiar with some basic XML concepts, and also with Define-XML version 2 | where statement, adam dataset, clinical trial, dataset, csr, format statement, sample, data science, data cleaning, csv, reference data, cdisc | source | 29 |
| 47 | Creating Time to Event ADaM Dataset for a Complex Efficacy Endpoint in Multiple Sclerosis Therapeutic Area | Ittai Rambach | This article presents my solution for the creation of Time to Event ADaM Dataset (ADTTE) for a complex efficacy endpoint - Time to Confirmed Disease Progression (CDP) which is evaluated by survival analysis, and is commonly used in Multiple Sclerosis (MS) therapeutic area studies. CDP is defined as: 1. An increase from baseline of ≥1.0 points if the baseline EDSS was 5.0 points or less, or an increase of ≥0.5 point if the baseline EDSS was ≥5.5 points 2. The increase should be sustained for at least xx months 3. Progression cannot be confirmed during a confirmed relapse The exact definition should be defined in the study protocol and SAP (usually the sustained should be at least for 12 or 24 weeks). In this article I first introduce the MS TA, explain the EDSS scale, and look in the Data - Neurological Assessments and Relapses | adam dataset, sdtm domain, randomization, cdisc | source | 8 |
| 48 | Data Validation: Bolstering Quality and Efficiency | Anusuiya Ghanghas, Houde Zhang, Rajinder Kumar |
We are working in health care industry and here a small mistake means risk for many lives. Statistical programming is a small area of this industry but our tiny efforts help people to live a better life. It is necessary that all the work done here should be done with higher quality. To maintain quality, validation process needs to be defined. This paper includes general outline of two validation processes of statistical programs used in clinical r esearch. In t he f irst process, validation programmer will not write independent code but manually check results or small portion of results. This is normally preferred in analyzing small data. In the second process, validation programmers will write their own code and compare the final datasets with programming codes or procedures (PROC COMPARE). The second process is more precise, less dependent and more aligned |
data cleaning, proc compare, proc step, sas log | source | 3 |
| 49 | Data Visualization for Quality Control in NONMEM Data set | Linghui Zhang | Non Linear Mixed Effects Model (NONMEM) data set is w idely used for pharmacokinetics (PK) / pharmacodynamics (PD) modeling and simulation, w hich studies the drug concentration in the body over time (measured in terms of absorption, distribution, metabolism, and excretion [ADME]) an d the body's pharmacological response t o a drug (measured in terms of adverse event s [AE] and efficac ies). In a very specific pre-defined format, the NONMEM data set includes a chronological mixture of dosing records, PK/PD observations and covariates of t he dosing and observation records. To create NONMEM data sets, it takes tremendous programming efforts for programmers to derive dosing history, order PK/PD observations and merge various types of covariates. The variables required for NONMEM data are ofte n complicated and come from different sour ce data sets. It is a tough challenge to perform data validation and cleaning | pd, scatter plot, outlier, sample, box plot, clinical trial, histogram, raw data, clinical data, proc step, data cleaning | source | 9 |
| 50 | Deconstructing ADRS: Tumor Response Analysis Data Set | Steve Almond | From the perspective of someone new to both the oncology therapy area and working with ADaM, this paper describes the process of designing a data set to support the analysis of tumor response data. Rather than focus on the programmatic implementation of particular response criteria (e.g., RECIST, Cheson, etc.), we instead concentrate on the structural features of the ADaM data set. Starting from a simple description of a study's primary analysis needs, we explore the design impact of additional protocol features such as multiple response criteria algorithms, multiple medical evaluators, and adjudication. With an understanding of the necessary derived parameters to support our analyses, we have a better conceptual link between the collected SDTM RS data and our ADRS analysis data set -- regardless of response criteria being used (the implementation of which is beyond the scope here) | best response, interim analysis, cdisc | source | 11 |
| 51 | Delivering a quality CDISC compliant accelerated submission using an outsourced model | Mei Dey, Diane Peers |
Pharmaceutical companies are faced with the challenge of developing innovative treatments for patients and replacing older and expiring pipelines whilst containing research and development costs. Outsourcing models, whether functional outsourcing or full service outsourcing, are increasingly being relied upon to meet these challenges. This includes the outsourcing of the analysis and reporting of clinical trials and the creation of an electronic submission package to clinical research organizations (CRO). Sponsors are owners of drug compounds and are ultimately accountable for the quality of submission packages delivered to regulatory agencies. How to ensure submission quality in an outsourced model is a significant challenge facing all sponsors. To what extent should sponsors be reviewing the CRO's work and what level of checking is sufficient? This paper uses a case study approach to examine a recent sponsor/CRO partnership in support of an accelerated oncology drug submission | data review, phase ii, data cleaning, clinical trial, cdisc | source | 8 |
| 52 | Displaying data from NetMHCIIPan using GMAP: the SAS System as a Bioinformatics Tool | Kevin R. Viel | The binding of peptides to HLA Class II molecules is a seminal event in adaptive immunology. From vaccine development to immunogenicity, the prediction of binding affinity (strength) is extremely important information. The NetMHCIIpan 3.1 Server provides estimates of the binding affinity for specific peptides and given HLA molecules. The goal of this paper is to describe the basics of immunology with respect to HLA, to describe how to obtain estimated binding affinity interactively using NetMHCIIpan, and how to present these data using the SAS system using the GMAP procedure complimented by the ANNOTATE facility |
proc sql, proc gmap, data step, proc sgplot, sample, macro variable | source | 22 |
| 53 | Don't be a Diamond in the Rough: Tips to Employment | Janet E. Stuelpner | No one can foresee a merger or acquisition. When it happens, big changes happen as well. So, what can you do to make yourself employable? What puts you ahead of the rest so they choose you instead of someone else? What do you need to do to get your next job? What are the resources that are available to you to help in the pursuit of your next opportunity? In this presentation, I will explore the types of things that will help you to succeed without spending time on the bench | source | 8 | |
| 54 | Don-t Agonize, Organize: Maximizing efficiency and effectiveness of SAS Programming in Clinical Trials projects, by using Project Management Organizing Methodologies | Shefalica Chand | Technology plays a major role in implementing and completing a project, however there are certain project management methodologies that can help maximize efficiency and effectiveness of a project. These methodologies can provide an organized structure to improve integration, communication, conflict management, time and cost management, risk analysis, quality control, providing clear directions to achieve enhanced benefits and results. This paper talks about how project management organizing methodologies can be advantageous to SAS Programming projects in the Clinical Trials Industry. We will learn about creating a project management plan and using Five Process Groups of Project Management for SAS Programming projects. Initiating - To authorize a project or the next ph ase within the project. Establishing objectives, scope, deliverables, resources and responsibilities Planning - Core planning and facilitating | adam dataset, clinical trial, annotated crf, data science, cdisc | source | 8 |
| 55 | DOSUBL and the Function Style Macro | John Henry King | The introduction of the SAS function DOSUBL has made it possible to write certain function style macros that were previously impossible or extremely difficult. This Beyond the Basics talk will discuss how to write a function style macro that uses DOSUBL to run SAS code and return an "Expanded Variable List" as a text string. While this talk is directed toward a specific application the techniques can be more generally applied | macro variable, proc transpose, proc step, variable list, variable, proc sql, select | source | 6 |
| 56 | Duplicate records - it may be a good time to contact your data management team | Sergiy Sirichenko, Max Kanevsky |
Most programmers are already familiar with the concept of duplicate records, where multiple records are identical in values across all variables. These duplicates are easy to catch and clean. However, there are also cases where clinical data has more than one expected record for the same assessment at the same time point with different results. These are much harder to manage and can complicate analysis by producing incorrect outcomes. In this presentation, we will examine the concepts of the unique observation and key variables. We'll review common causes and examples of duplicate records during the data collection and mapping process. And finally we'll demonstrate how to detect, clean, and document duplicate records | surrogate key, natural key, data cleaning, data governance, sdtm programming, sample, adverse events, cdisc | source | 8 |
| 57 | Efficient Safety Assessment in Clinical Trials using the Computer-generated AE Narratives of JMP Clinical | Richard C. Zink, Drew Foglia |
ICH Guideline E3 recommends that sponsors provide written narratives describing each death, serious adverse event (SAE), and other significant AE of special interest to the disease under investigation. Narratives summarize the details surrounding these events to enable understanding of the circumstances that may have led to the occurrence and its subsequent management and outcome. Ultimately, narratives may shed light on factors associated with severe events, or describe effective means for managing patients for appropriate recovery. Narratives are written from the original SAE report in combination with summary tables and listings that are generated as part of the study deliverables. Information contained in the typical narrative requires the medical writer to review these many disparate sources. This is time consuming and often requires additional review and quality control. Too often, narratives may not be composed until the full data for a patient becomes available | adverse events, concomitant medications, medical history, where statement, clinical trial, serious adverse event, quality control, analysis set, sdtm domain, vital signs, proc sql, cdisc | source | 13 |
| 58 | Elevate your Graphics Game: Violin Plots | Spencer Childress | If you've ever seen a box-and-whisker plot you were probably unimpressed. It lives up to its name, providing a basic visualization of the distribution of an outcome: the interquartile range (the box), the minimum and maximum (the whiskers), the median, and maybe a few outliers if you're (un)lucky. Enter the violin plot. This data visualization technique harnesses density estimates to describe the outcome's distribution. In other words the violin plot widens around larger clusters of values (the upper and lower bouts of a violin) and narrows around smaller clusters (the waist of the violin), delivering a nuanced visualization of an outcome. With the power of SAS/GRAPH, the savvy SAS programmer can reproduce the statistics of the box-and-whisker plot while offering improved data visualization through the addition of the probability density 'violin' curve. This paper covers various SAS techniques required to produce violin plots |
box plot, proc means, interquartile range, proc step, proc sgpanel, outlier | source | 9 |
| 59 | Empowering Users By Creating Data Visualization Applications In R/Shiny | Sudhir Singh, Brian Munneke, Amulya R. Bista, Jeff Cai |
A statistical programmer often receives several requests from different functions for exploratory data analysis. This results in several programming hours spent on unplanned exploratory analyses. We believe a better and more efficient approach would be to build data visualization applications that allow our users to directly interact with the data. R + Shiny helps us develop scalable simple web based data applications that can access the data in real time [1]. This paper demonstrates several existing applications that we have developed to assist various functional groups. Our applications help us communicate information or provide analyses clearly and efficiently in real time. The system is scalable and more customized applications can be built within short development cycles | confidence interval, sample size, sample, null hypothesis, binomial distribution, clinical trial, stage 2, laboratory data, outlier, clinical data | source | 8 |
| 60 | Enhanced OpenCDISC Validator Report for Quick Quality Review | Ajay Gupta | OpenCDISC validator provides great compliance checks against CDISC outputs like SDTM, ADaM, SEND and Define.xml. This valida tion tool will provide a report in E xcel or CSV format which contains errors, warnings, and notice, information. At the initial stage of clinical programming when the data is not very clean, this report can sometimes be very large and tedious to review. Als o, if there is data or code list issue s in the report then the user needs to check the physical SAS data sets or SDTM controlled terminology separately which can be very time consuming. In order to expedite quality review time, this paper will introduce an enhanced version of the OpenCDISC validator report. This enhanced report will have SDTM data (only row with issue) and SDTM terminology added as separate worksheet s in the original report |
excel, macro definition, clinical data, macro call, data cleaning, macro variable, cdisc | source | 12 |
| 61 | Enhancing the SAS Enhanced Editor with Toolbar Customizations | Lynn Mullins | One of the most important tools for SAS programmers is the Display Manager window environment in which programs are developed. Most programmers like to use shortcuts when developing programs to save time; the less typing we have to do; the more satisfied we are, especially with repetitive tasks. The default Enhanced Editor window can help you edit SAS files and code using built-in tools, one of which is the toolbar. This paper will describe how to customize the toolbar to perform tasks by just one click of an icon | sample, sas base | source | 6 |
| 62 | Enough of Clinical ... Let's talk Pre-Clinical! | Arun Raj Vidhyadharan, Sunil Mohan Jairath |
In drug development, pre-clinical development, also named preclinical studies and nonclinical studies, is a stage of research that begins before clinical trials (testing in humans) can begin, and during which important feasibility, iterative testing and drug safety data is collected. The main goals of pre-clinical studies are to determine the safe dose for First-in-man study and start to assess product's safety profile. Products may include new or iterated or like- kind medical devices, drugs, gene therapy solutions, etc. This paper talks about preclinical trials conducted in animals prior to testing in human subjects | clinical trial, clinical research | source | 5 |
| 63 | Everything or Nothing - A Better Confidence Intervals for Binomial Proportion in Clinical Trial Data Analysis | Sakthivel Sivam, Subbiah Meenakshisundaram |
Often in Pharmaceutical Research, Confidence intervals have become an important aspect of reporting statistical results. In particular, extensive literature is available for the interval estimators for a binomial proportion (p) that usually arises from the inferential problem for number of successes X ~ Binomial (n, p). However, the occurrences of the event exactly at boundaries (x = 0 or n) have drawn more research interest in spite of few recommendations such as continuity corrections and truncating the limits with 0 or 1. In this paper, one of the widely applied methods, score interval has been considered to improve its performance exactly when x = 0 or n. The proposed approach is based on boundary corrections due to exact method and the Score Interval in its original form; this alleviates the issues related to continuity corrections and adding any pseudo successes or failures | confidence interval, binomial distribution, confidence level, rmse, sample, sample size, mse, proc freq, clinical trial | source | 8 |
| 64 | Exploring HASH Tables vs. SORT/DATA Step vs. PROC SQL | Richann J. Watson, Lynn Mullins |
There are often times when programmers need to merge multiple SAS data sets to combine data into one single source data set. Like many other processes, there are various techniques to accomplish this using SAS software. The most efficient method to use based on varying assumptions will be explored in this paper. We will describe the differences, advantages and disadvantages, and display benchmarks of using HASH tables, the SORT and DATA STEP procedures, and the SQL procedure |
hash table, proc sql, proc step, lookup | source | 14 |
| 65 | Fifty Ways to Change your Colors (in ODS Graphics) | Shane Rosanbalm | Back in the good ole days (think GPLOT) there was one primary way to change the colors of your symbols and lines: the COLOR= option of the SYMBOL statement. But now that ODS graphics are taking over (think SGPLOT and GTL), color management is not so straightforward. There are plot statement attribute options, style modifications, the %modstyle macro, discrete attribute maps, and more. Sometimes it feels like there must be 50 ways to change your colors. In this paper we will explore the various ways to manage colors in ODS graphics. Complex topics will be demystified. Strengths and weaknesses will be examined. Recommendations will be made. And with any luck, you will come away feeling less confused and more confident about how to manage colors in ODS graphics |
regression analysis, proc step, proc template | source | 14 |
| 66 | Formats and Informats - Concepts and Quick Reference Table | Emmy V Pahmer | Using formats and informats is very common in SAS programming. They are used to read external data, to temporarily or permanently change how data are displayed, to categorize, or to look up related values as with a lookup table. This paper will look at how to create and use formats and informats in various contexts, and provide a quick-reference table with examples | proc format, proc step, put statement, lookup, input statement, format statement, proc freq | source | 8 |
| 67 | Four "Oops" Moments While Using Electronic Health Records to Identify a Cohort of Medication Users | Steve Ezzy | In life, and when using electronic health record (EHR) data, things don't always go the way you plan. Random quirks in software, metadata and clinical data can lead you down rabbit-trails that take hours to discover, diagnose and correct. I present four quirks encountered while identifying a cohort of users of a particular medication within an EHR and describe procedures to help safeguard against them. 1. Use of mapped fields, such as medication codes, can simplify a myriad of local EHR code issues. However detailed knowledge about how the mapping was performed is essential. 2. Determining which dates are best to use from Rx data is complicated --> Issue Date, Patient-Report Date, Action Date, Med Reported Date, Update Date, Discontinue Date, Expiration Date? Choice of date should be determined by whether the research question is aimed at topics such as prescribing behaviors, adherence or drug utilization. 3 | insurance claims, clinical data | source | 5 |
| 68 | Generalized Problem-Solving Techniques for De-bugging and Diagnosing Logic Errors | Brian P. Fairfield-Carter, Tracy Sherman |
The most troublesome code bugs are those that seem to elude rational diagnosis: these are the 'logic errors' in syntactically-correct programs, where the SAS log offers little or no insight. More disturbingly, these crop up in programs in which we otherwise place reasonable confidence, convinced as we are of the logic behind our initial efforts. Syntax errors are almost always easy to spot, as they are the primary focus of built-in de-bugging features, and also enjoy a wealth of documentation to aid in diagnosis and correction. When it comes to logic errors, however, we're very much on our own: logic errors are rarely captured by built-in de-bugging features, and discussion of strategy seldom extends beyond general recommendations like "fully understand your data" and "carefully review output" | proc step, proc report, proc sql, create table, out= | source | 12 |
| 69 | Get a Quick Start with SAS ODS Graphics By Teaching Yourself | Roger D. Muller | SAS Output Delivery System (ODS) Graphics started appearing in SAS 9.2. When first starting to use these tools, the traditional SAS/GRAPH software user might come upon some very significant challenges in learning the new way to do things. This is further complicated by the lack of simple demonstrations of capabilities. Most graphs in training materials and publications are rather complicated graphs that, while useful, are not good teaching examples. This paper contains many examples of very simple ways to get very simple things accomplished. Over 20 different graphs are developed using only a few lines of code each, using data from the SASHELP data sets. The usage of the SGPLOT, SGPANEL, and SGSCATTER procedures are shown. In addition, the paper addresses those situations in which the user must alternatively use a combination of the TEMPLATE and SGRENDER procedures to accomplish the task at hand |
proc step, proc template, proc sgplot, proc sgpanel, sas enterprise guide, ods graphics, output delivery system, sample, excel | source | 25 |
| 70 | Getting Clouds Moving across the Pacific - a case study on working with a Chinese CRO on SAS Drug Development | Chen Shi | California has been in a long drought and some parts across the Pacific have been suffering from storms during the past 2 years. Why not move the clouds over? This can be a dream but not only a dream if we know how to manage the clouds. In this case study, we will share our experiences and lessons learned about delivering study packages on the SAS Drug Discovery (SDD) platform with a newly on-boarded Chinese CRO. This paper will elaborate on topics including project setup, training, scheduling, and communications as well as debugging, quality control and project summarizations. It was a fun and challenging project as it proved that SDD can be a useful tool for enabling cross- continent teams to work together | clinical research, quality control | source | 4 |
| 71 | Getting Started with Data Governance | Gregory S. Barnes Nelson | While there has been tremendous progress in technologies related to data storage, high performance computing and advanced analytic techniques, organizations have only recently begun to comprehend the importance of parallel strategies that help manage the cacophony of concerns around access, quality, provenance, data sharing and use. While data governance is not new, the drumbeat around this, along with master data management and data quality, is approaching a crescendo. Intensified by the increase in consumption of information, expectations about ubiquitous access, and highly dynamic visualizations, these factors are also circumscribed by security and regulatory constraints. In this paper we provide a summary of what data governance is and its importance, which is, as you would expect. However, we go beyond the obvious and provide practical guidance on what it takes to build out a data governance capability appropriate to the scale, size and purpose of the organization and its culture | data governance, master data management | source | 15 |
| 72 | Go Compare: Flagging up some underused options in PROC COMPARE | Michael Auld | proc compare, proc step, by group processing, numeric variable, create table, pharmaceutical industry | source | 14 | |
| 73 | Good versus Better SDTM: Data Listings | Mario Widel, Henry B. Winsor |
What does SDTM have to do with data listings? The popular answer is not much, if all you have is "good" SDTM. SDTM (or its predecessor with FDA, Item 11 data sets) have been supposed to replace data listings for about 15 years now. However, data listings aren't going away anytime soon, so while they are still here why not make the absolute best of them? They are still a required component of an ICH compliant CSR. Too many people think of data listings as a simplistic dump of the data, but when properly developed by the right people they can be a very useful tool for data review and CSR preparation. The authors will design and prepare an Abnormal Lab Listing (which is actually referenced as a table in ICH compliant CSRs) and show some ways in which SDTM creation can be used to store lab metadata that improves the listing appearance and utility while simplifying the creation | adverse events, adam dataset, proc print, protocol deviations, sdtm domain, study day, efficacy summary, cdisc | source | 11 |
| 74 | Handling Interim and Incomplete Data in a Clinical Trials Setting | Paul Stutzman | This paper discusses potential problems that can arise when working with interim and incomplete data. It identifies ways to mitigate these issues through preparation and ongoing reassessment. Techniques and programmatic tools for performing these preparation and reassessment tasks are also presented. Programmers and statisticians are frequently presented with interim and incomplete data. This is often the case when preparing reports for Data Monitoring Committees (DMCs) or Data Safety Monitoring Boards (DSMBs), performing interim analyses, or developing standards-compliant data set programs. Thus, it is important to understand the constraints and potential pitfalls of working with preliminary data, to write programs that handle both anticipated and unanticipated values, and to develop tools and techniques for understanding how data change over time | nominal variable, sas log, clinical trial, numeric variable, proc step, interim analysis, record count, adverse events, outlier, proc means, proc report, interval variable, xls, laboratory data, excel, sdtm domain, sample, proc contents, proc freq, data science, proc sql, cdisc | source | 13 |
| 75 | Hands-On SAS Macro Programming Essentials for New Users | Kirk Paul Lafler | The SAS Macro Language is a powerful tool for extending the capabilities of the SAS System. This hands-on workshop teaches essential macro coding concepts, techniques, tips and tricks to help beginning users learn the basics of how the Macro language works. Using a collection of proven Macro Language coding techniques, attendees learn how to write and process macro statements and parameters; replace text strings with macro (symbolic) variables; generate SAS code using macro techniques; manipulate macro variable values with macro functions; create and use global and local macro variables; construct simple arithmetic and logical expressions; interface the macro language with the SQL procedure; store and reuse macros; troubleshoot and debug macros; and develop efficient and portable macro language code |
macro variable, macro facility, proc step, autocall macro, macro definition, macro parameter, %macro parameters, macro function, dictionary tables, options statement, proc sql, macro loop, &variable | source | 10 |
| 76 | I Object: SAS Does Objects with DS2 | Peter W. Eberhardt, Xue Yao |
The DATA STEP has served SAS programmers well over the years, and although it is powerful, it has not fundamentally changed. With DS2, SAS has introduced a significant alternative to the DATA STEP by introducing an object -oriented programming environment. In this paper, we share our experiences with getting started with DS2 and learnin g to use it to access, manage, and share data in a scalable, threaded, and standards-based way |
missing value, macro function, macro variable, sas function, proc step, dataset, sas base, return, sysdate9, sas programmer, proc sql | source | 27 |
| 77 | Implementation of ADaM Basic Data Structure for Cross-over Studies | Songhui Zhu | ADaM standards defined several classes of data structures, including Subject Level Analysis Data (ADSL), Occurrence Data (OCCDS), Basic Data Structure (BDS), and ADaM Other. These classes of datasets usually are all utilized in a complete study. But BDS is the most widely-used class of ADaM datasets and suitable for most safety and efficacy datasets such as lab data analysis, accountability analysis, exposure analysis, time-to-event analysis, and questionnaire analysis. To seasoned ADaM programmers, the implementation of ADaM-compliant study package is relatively easy when the study is a parallel design. However, the implementation of ADaM datasets can be challenging when the trial is a cross-over study | crossover study, treatment period, adam dataset, change from baseline, adverse events, bds, concomitant medications, cdisc | source | 10 |
| 78 | Importance of Niche Provider for Successful NDA Submission: Rescue Case Study | Aparna Poona, Bhavin Busa, Tim Southwick |
Of the several outsourcing models, a Functional Service Provider (FSP) model has become most successful and significant especially in data management, statistical programming and biostatistics service areas of clinical drug development. Most Sponsors prefer to have a single, full-service contract research organization (CRO) offering end-to-end services for an entire study including data submission components. However, with the stringent requirements for the CDISC compliant datasets by the regulatory agencies, the Sponsors are at a higher risk of getting a refuse to file (RTF) due to compliance issues with the submitted data. The full-service CRO may not have an all-round expertise in each of the service areas which will lead to poor quality of the submission deliverables and delays | adam dataset, service provider, phase i, phase ii, phase iii, sdtm domain, clinical trial, clinical research, clinical programming, reviewer guide, cdisc | source | 9 |
| 79 | Importing Data Specifications from .RTF and .DOC files and producing Reports | Sandesh Jagadeesh | Programming reports that are used in the process of data cleansing and patient level checks can be tedious. These reports are of great value to data management teams, providing in-depth information needed to identify adverse events, compare variables against database standards, and to reconcile data. The report specifications can be provided in .RTF or.DOC format, which would require manual programming to generate the desired reports. The number of variables in these reports can be quite extensive and time-consuming to review and program. This paper will introduce a method for importing the required data sets, variables and labels directly from the specification, reconciling them to the variables in the database, and producing reports in .PDF, .RTF, or .XLS format for review | sample, output delivery system | source | 5 |
| 80 | Increase Your Bottom-line and Keep More Money in Your Pocket - A Practical Guide to the Self-Employed | Margaret Hung | This paper outlines some common and not so common deductions which will help you increase your profit margins. It highlights and explains fourteen (14) deductions with certain pitfalls for some | source | 5 | |
| 81 | Introduction of Semantic Technology for SAS programmers | Kevin Lee | There is a new technology to express and search the data that can provide more meaning and relationship - semantic technology. The semantic technology can easily add, change and implement the meaning and relationship to the current data. Companies such as Facebook and Google are currently using the semantic technology. For example, Facebook Graph Search use semantic technology to enhance more meaningful search for users. The paper will introduce the basic concepts of semantic technology and its graph data model, Resource Description Framework (RDF). RDF can link data elements in a self-describing way with elements and property: subject, predicate and object. The paper will introduce the application and examples of RDF elements. The paper will also introduce three different representation of RDF: RDF/XML representation, turtle representation and N-triple representation | clinical trial, clinical data, char. | source | 15 |
| 82 | Journey from the student to the full-time programmer | Viktoriia Vasylenko | Suppose you want to work in the pharmaceutical industry, but you are studying in the field of applied mathematics. One natural question that may arise: what are the chances of me getting into such an industry? For me the answer turned out to be: 'pretty high!' In hindsight, I understand that pursuing a degree in applied mathematics is a bit of a risk; particularly, for those of us who enrolled in the Clinical SAS University Program 2013 -2014. Our class was the first experiment with this kind of program, so we were not sure about its success rate. Furthermore, we entered the program without fully understanding the benefits of pursuing such a field. Additionally, there will come a time, where we as students will question if the substantial effort we put to our education and the knowledge that we have accumulated so far will result in a better opportunity to secure a career in the field we want | clinical research, clinical sas, sas programming | source | 4 |
| 83 | Keeping Up with Updating Dictionaries | Divyaja Padamati | Often in Pharmaceutical Industries we work with data for poster presentations, manuscripts or for additional exploratory analyses after the CSR submission. Here comes the important question. How often do we check for updated dictionary versions and what do we do when this occurs? In this paper we shall discuss about; (1) Different checks that are required when the data is using updated dictionary terms. (2)If updated dictionary terms are not used, then how should we proceed? The skill level needed will be intermediate to advanced SAS programmer | sas programmer, proc sql | source | 3 |
| 84 | Lab CTCAE - the Perl Way | Marina Dolgin | The NCI Common Terminology Criteria for Adverse Events (CTCAE) is a descriptive terminology which is utilized for Adverse Event (AE) reporting. A toxicity grading scale is provided for each AE term, it varies from 1 (mild) to 5 (death). Typically, CTCAE grading is directly collected from the site on the adverse experience case report form. However, this may not be the case for laboratory results. Usually, the calculation of CTCAE grading for laboratory results would require an explicit coding of each criteria, per each lab test, by means of "If" and "Else" statements. This paper demonstrates a method (macro) for calculation of toxicity grade based on laboratory value and original NCI CTCAE grading file. This macro is using Perl Regular Expressions patterns and functions, which is much more compact solution to a complicated hard coding task and also eliminates a need for per test programming of the toxicity grades. Lastly, macro may be used with both ADaM and SDTM structures | ctcae grade, adverse events, laboratory data, prxchange, cdisc | source | 7 |
| 85 | Latent Structure Analysis Procedures in SAS | Deanna Naomi Schreiber-Gregory | The current study looks at several ways to investigate latent variables in longitudinal surveys and their use in regression models. Three different analyses for latent variable discovery will be briefly reviewed and explored. The latent analysis procedures explored in this paper are PROC LCA, PROC LTA, PROC TRAJ, and PROC CALIS. The latent variables will then be included in separate regression models. The effect of the latent variables on the fit and use of the regression model compared to a similar model using observed data will be briefly reviewed. The data used for this study was obtained via the National Longitudinal Study of Adolescent Health, a study distributed and collected by Add Health. Data was analyzed using SAS 9.4. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in behavioral science and/or statistics |
latent variable, proc step, regression analysis, factor analysis, logistic distribution, logistic regression, correlation, proc calis, proc surveylogistic, response variable, sample, proc means | source | 8 |
| 86 | Let's Make Music: Using SAS Functions for Music Composition | Kim Truett, Zak Truett |
SAS has been experimented with for entertaining diversions before, such as the Pegboard Game, Tetris and Solitaire, so we wondered if we could get SAS to write music using improvisational rules. Improvisation in music is not just playing random notes - it follows a defined set of rules - sometimes just one or two, sometimes many. Following a basic set of these rules for note progression and duration, we use SAS random number functions and weighted probabilities to determine what the next note will be (the pitch), and how long the note is played (the duration). The goal of this light-hearted talk is to illustrate the basic use of SAS functions to improvise a few bars of music that can be played | proc format, proc step | source | 8 |
| 87 | Log Checks Made Easy | Yogesh Pande | It is Good Programming Practice (GPP) for a programmer to check the SAS log for errors, warnings, and all other objectionable SAS NOTES. In order to successfully create tables, listings, and figures, the programmer m ust ensure that code is correct . The accuracy of the code, assuming that the program logic is correct, solely depends on SAS log. Using SAS macro language and Base SAS , this paper will introduce a macro that will enable a programmer or statistician to check all SAS logs in a folder and tabulate the log issues by SAS log files using PROC REPORT. Keywords: SAS Log, Macro, report, infile, print |
sas log, merge statement | source | 4 |
| 88 | Macro to get data from specific cutoff period | Kiranmai Byrichetti, Jeffrey Johnson |
During the clinical development of an investigational drug, periodic analysis of safety information is of great importance to the ongoing assessment of risk to trial subjects. It is also important to inform regulators and other interested parties (e.g., ethics committees) at regular intervals about the results of such analyses and the evolving safety profile of an investigational drug, and apprise them of actions proposed or being taken to address safety concerns. For periodic analysis, data needs to be pulled based off of specific time period and doing this on each and every output or dataset is time consuming and having a macro to do such task is definitely time saving. Also, Manual selection of data based off particular dates can be prone to errors. For this purpose, a macro has been developed and this paper discusses a macro that applies cutoff based off specific dates on all SDTM datasets available in both production and validation folders | date variable, proc sort, cdisc | source | 11 |
| 89 | Making Greedy into Optimal! A Poor Woman's Attempt to Get Optimal Propensity Score Matching from a Greedy Matching Algorithm | Janet M. Grubber, Carl Pieper, Cathleen Colon-Emeric |
Propensity score matching (matching on the probability of treatment assignment (treated vs. untreated) conditional on observed baseline characteristics) is a popular method used by many observational studies to approximate, as much as possible, randomized clinical trial methodology. In the medical literature, greedy matching is the form of matching most often reported, though optimal matching is often said to be a superior method. In our real world example, our goal was to match 1 treated patient to 3 untreated controls if 3 suited controls existed; however, if fewer (1 or 2) existed, we still wanted to retain the 1:2 or 1:1 matched pair to increase our power to detect significant differences in analyses. Optimal matching was well-suited to accomplish our goal; however, our organization lacked funds to pay for the needed SAS module | propensity score, propensity score matching | source | 12 |
| 90 | May the Function Be With You: Helpful SAS Functions, Particularly When Handling CDISC Data | Angela Lamb | SAS Functions are a basic component of the DATA STEP, but some lesser-known functions are often overlooked, leading to lengthy, complicated, and unnecessary code. Programmers can get stuck in a rut, habitually handling data one way, while a more efficient and more robust function could simplify the process. CDISC data presents us with unique programming challenges. One such challenge is the need to handle long, ISO 8601 formatted dates. Functions can be very helpful in processing these dates-for instance, in breaking them apart to calculate durations. This paper explores the basics of SAS functions, some that are especially useful for handling CDISC data, and functions that are lesser-known but invaluable to the clinical trials programmer |
character functions, date variable, cdisc | source | 9 |
| 91 | Meaningful Presentation of Clinical Trial Data with Multiple Y-Axes Graph | Mina Chen | Graphics are a powerful way to display clinical trial data. Graphs are widely generated in the pharmaceutical industry to review the results of clinical trials. For example, mean plot is often used to show laboratory results change over time. Sometimes there's a need to display the changes of two laboratory parameters over time within one mean plot. A second y-axis is useful for plotting data with different scales on the same plot. This paper will show, by example, how such graphs can easily be created using the SAS/GRAPH |
clinical trial, line chart, clinical data, proc step, bar chart, proc template | source | 5 |
| 92 | Mixed Effects Models | Yan Wang | The MIXED procedure has been commonly used at the Bristol-Myers Squibb Company for quality of life and pharmacokinetic/pharmacodynamics modeling. This paper provides two examples to explore the use of the SAS PROC MIXED procedure. One example is a phase 3 neuroscience study, where we use this example to demonstrate the longitudinal data analysis. The other example is a phase 2, PK, HIV, cross-over study. The paper describes the programs that have been used to carry out these analyses, and the interpretation of the outputs |
proc mixed, proc step, least squares mean, response variable, fixed effects, proc glm, proc genmod, interaction effect, main effect, significance level, covariance, confidence interval, predictor variable, data science | source | 7 |
| 93 | Moving from Data Collection to Data Visualization and Analytics: Leveraging CDISC SDTM Standards to Support Data Marts | Steven R. Kirby, Terek Peterson |
Data from clinical trials supports a wide range of clinical, safety, regulatory, and analytic groups who all share the same basic need: to efficiently access, analyze and review the data. When clinical data from multiple studies are combined into a "data mart" and linked to visualization and analytical tools, data consumers are able to efficiently find the information they need to make informed decisions. The raw data as collected in individual studies will vary (at a minimum) based on the specific collection system and forms used. Due to that variability, a foundational step in creating a data mart is to ensure that the data from across studies has a consistent, standard format. We will share our experience leveraging CDISC SDTM standards to support data marts containing data from many studies across several therapeutic areas | data mart, raw data, clinical trial, data standardization, cdisc | source | 8 |
| 94 | Moving up! - SDTM v3.2 - What is new and how to use it | Alyssa M. Wittle, Christine McNichol, Antonio Cardozo |
December of 2013 the CDISC world blessed us with SDTM version 3.2. Now that the FDA has approved the use of the new version of the implementation guide and the requirement is on the horizon, we need to consider all that the new IG has in store for our clinical trials and what handling or considerations should be made for the noted exceptions. Don't be overwhelmed by the file size or the fancy, new format, this session will lay out the new IG in easy to understand ways and show you how to apply this to your studies today! It will cover new domains, significant changes to our existing domains and applied examples to walk you through how to transition to this new version. We will cover in depth bigger changes, such as the EX/EC adjustment, but will highlight even in the smaller changes to make sure you don't miss them as you become familiar with the new document. This session will have you SDTM v3.2 fluent in no time! | controlled terminology, per protocol, cdisc | source | 8 |
| 95 | My First Job Dos-and-Don'ts: A Survival Guide for Your First Statistical Programming Job in the Industry | Assir Abushouk, Thu-Nguyen Nguyen |
Are you a statistical programmer who's new to the industry? Have no fear: some useful tips and tricks are here! After working a little over a year for a CRO, we wanted to share some things we've learned along the way, as well as include some personal anecdotes of our successes and failures. We hope that these ideas will be a surefire way to help you keep your first job and wow your boss. Throughout our presentation, we will discuss how to take charge of your own career, program more efficiently, and stay organized in the workplace. Although many of these skills can be applied to any first job, our focus is to help new industry programmers. These suggestions may seem very simple and straightforward, but building good habits from the beginning is a great way to get your career off to a wonderful start | proc compare, error message | source | 4 |
| 96 | Name that Function: Punny Function Names with Multiple MEANings and Why You Do Not Want to be MISSING Out | Ben Cochran, Arthur L. Carpenter |
The SAS DATA STEP is one of the best (if not the best) data manipulators in the programming world. One of the areas that gives the DATA STEP its flexibility and power is the wealth of functions that are available to it. With over 450 functions in the DATA STEP it is difficult to learn and remember them all, however understanding how to take advantage of the power of these functions is key to taking full advantage of the DATA STEP. This paper takes a PEEK at some of those functions whose names have more than one MEANing. While the subject matter is very serious, the material will be presented in a way that is guaranteed not to BOR the audience. Syntax will be discussed and examples of how these functions can be used to manipulate data will be demonstrated. With so many functions available and with less than an HOUR to present, we obviously will have to TRIM our list so that the presentation will fit within the allotted TIME |
date variable, sysdate9, character functions, poisson distribution | source | 10 |
| 97 | New Features in Define-XML V2.0 and Its Impact on SDTM/ADaM Specifications | Hang Pang | Define-XML is required for NDA/BLA submission (FDA Study Data Technical Conformance Guide, V2.3, Oct. 2015). CDISC Define-XML Specification V2.0 (2013) has some significant changes compared with CRT-DDS (Case Report Tabulation Data Definition Specification (define.xml), V1.0, 2005). This paper will discuss the new features in Define- XML V2.0 (e.g. integrates with industry standard CDISC NCI controlled terminologies, and supports more complicated value-level metadata (VLM) definitions…) , and its impact on SDTM/ADaM data specifications for submission readiness. The SDTM/ADaM data specifications will be used as a metadata for Define-XML (V2.0) generation, and simplified the NDA/BLA e-submission preparation process. An example of a SAS macro and ADaM specifications for Define-XML (V2.0) generation will be presented | controlled terminology, adam dataset, where statement, macro definition, cdisc | source | 12 |
| 98 | New for SAS 9.4: A Technique for Including Text and Graphics in Your Microsoft Excel Workbooks, Part 1 | Vincent DelGobbo | A new ODS destination for creating Microsoft EXCEL workbooks is available starting in the third maintenance release for SAS 9.4. This destination creates native Microsoft EXCEL XLSX files, supports graphic images, and offers other advantages over the older ExcelXP tagset. In this presentation you learn step-by-step techniques for quickly and easily creating attractive multi-sheet EXCEL workbooks that contain your SAS output. The techniques can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real time using SAS server technology is discussed. Although the title is similar to previous presentations by this author, this presentation contains new and revised material not previously presented. Using the ExcelXP tagset with earlier versions of SAS to create multi-sheet workbooks is also discussed |
excel, xls, sas output, sample, proc report, proc sgplot, sas base, scatter plot, proc step, dataset, proc tabulate, format statement | source | 23 |
| 99 | Novel Programming Methods for Change from Baseline Calculations | Mina Chen, Peter W. Eberhardt |
In many clinical studies, change from baseline is a common measure of safety and/or efficacy in in the clinical data analysis. There are several ways to calculate changes from baseline in a vertically structured data set such as Retain statement, Arrays, DO-Loop in DATA steps or PROC SQL. However, most of these techniques require operations such as sorting, searching, and comparing. As it turns out, these types of techniques are some of the more computationally intensive and time-consuming. Consequently, an understanding of these techniques and a careful selection of the specific method can often save the user a substantial amount of computing resources. This paper will demonstrates a novel way of calculating change from baseline using Hash objects |
hash table, change from baseline, data step, baseline, repeated measures, sysrc, clinical trial, proc step, proc sql | source | 7 |
| 100 | Now You Can Annotate Your GTL Graphs! | Dan Heath | For some users, having an annotation facility is an integral part of creating polished graphics for their work. To meet that need, we created a new annotation facility for the SAS ODS Graphics (SG) procedures in SAS 9.3. Now, with SAS 9.4, the Graph Template Language (GTL) supports annotation as well! In fact, the GTL annotation facility has some unique features not available in the SG procedures, such as using multiple sets of annotation in the same graph and the ability to bind annotation to a particular cell in the graph. This presentation will cover some basic concepts of annotating that are common to both GTL and the SG procedures. Then, I will apply those concepts to demonstrate some unique abilities of GTL annotation |
proc step, proc sgplot, bubble plot | source | 14 |
| 101 | Outcome of a Clinical SAS University training program in Eastern Europe - How are graduates performing in a real work environment? | Donnelle LaDouceur, Sergey Glushakov |
We introduced our Clinical SAS University training program at PharmaSUG 2015 - presenting the impetus for the program as well as the structure, development, obstacles, and keys for success. Our first class of graduates now have over a year of work experience, our second class has just gotten started with client work, and our third class of students are over half-way through their school year. The education provided in the program is valuable only if it translates to preparing a work force. Therefore, this presentation will provide updates on how our graduates are performing now that they've been assigned to client teams. As presented previously, the interns were split into 2 groups - one group shadowing client work and the other group working with US-based mentors. Regardless of where they started, all of the students that were accepted into the internship program are currently engaged in client work | clinical sas, pharma programming, sas programming, clinical programming | source | 7 |
| 102 | Performing Pattern Matching by Using Perl Regular Expressions | Arthur X. Li | SAS provides many DATA STEP functions to search and extract patterns from a character string, such as SUBSTR, SCAN, INDEX, TRANWRD, etc. Using these functions to perform pattern matching often requires utilizing many function calls to match a character position. However, using the Perl Regular Expression (PRX) functions or routines in the DATA STEP will improve pattern matching tasks by reducing the number of function calls and making the program easier to maintain. In this talk, in addition to learning the syntax of Perl Regular Expressions, many real- world applications will be demonstrated |
numeric variable, prxparse, prxchange, prxmatch, sas log, substr function, cpu time, sas output, numeric constant, prxposn, character constant | source | 12 |
| 103 | Plan Your Work Using PROC CALENDAR | Suresh Kumar Kothakonda | Most of the time, we rely on our mailbox calendar, notepads etc., to get a snapshot of our schedules. Everyone loves to have visual flow of their activities. To make this convenient to everyone by using SAS system, there is a simple and easy way to organize your work events using PROC CALENDAR which can give a visual representation of collective information of tasks for a single or multiple projects of the team. CALENDAR procedure combines data from individual personal schedule with corporate schedule/events and print out calendars for individu als. While scheduling applications abound, CALENDAR procedure serves more than just printing calendars . Besides, it also organizes calendar schedules for a group or a company and keeps the data for easy retrieval. It's easy and beneficial to maintain Individuals, team's schedules. More information is detailed in this paper |
proc step, dataset | source | 5 |
| 104 | Practical Implications of Sharing Data: A Primer on Data Privacy, Anonymization, and De-Identification | Gregory S. Barnes Nelson | Researchers, patients, clinicians, and other healthcare industry participants are forging new models for data sharing in hopes that the quantity, diversity, and analytic potential of health-related data for research and practice will yield new opportunities for innovation in basic and translational science. Whether we are talking about medical records (e.g., EHR, lab, notes), administrative information (claims and billing), social contacts (on-line activity), behavioral trackers (fitness or purchasing patterns), or about contextual (geographic, environmental) or demographic (genomics, proteomics) data, it is clear that as healthcare data proliferates, threats to security grow. Beginning with a review of the major healthcare data breaches in our recent history, this paper highlights some of the lessons that can be gleaned from these incidents | privacy, data protection | source | 23 |
| 105 | PrecMod: An Automated Precision SAS Macro for Random Effects Models | Jesse Albert Canchola, Pari Hemyari |
Typical random effects linear model estimation involves fitting a model with main effects that may or may not include nesting with other study factors. Part of the challenge comes when having to calculate the confidence intervals for the variance components using the correct effective degrees of freedom (Satterthwaite, 1946) and then iterating the macro over different grouping levels (if they exist) . The current precision macro , PrecMod, surmounts these challenges and provides a clear and concise path towards efficient and timely calculations ready for reporting | proc mixed, macro definition, restricted maximum likelihood, create table, numeric variable, standard deviation, macro parameter, maximum likelihood estimation, likelihood function, sample, anova | source | 15 |
| 106 | Prepare for Re-entry: Challenges and Solutions for Handling Re-screened Subjects in SDTM | Charity Quick, Paul Nguyen |
A common issue with SDTM is tabulating data for subjects who enroll multiple times in a single trial. Currently, the FDA advises that as long as there is one USUBJID for each specific subject, a different subject identifier (SUBJID) value can be used for each screening attempt. However, it can be challenging to handle this data while conforming to both the SDTM Implementation Guide and FDA-published validation rules. This paper will detail how to deal with subjects who have multiple screening visits while conforming to SDTM and FDA rules. The intention is to produce high quality data while maintaining traceability with the multiple screening visits. We will look at how to structure the Demographic Domain (DM) and the accompanying supplemental qualifiers data set accordingly. Furthermore, we explore how to identify additional domains that could be affected by re- screening | medical history, raw data, cdisc | source | 5 |
| 107 | Preparing Legacy Format Data for Submission to the FDA - When & Why Must I Do It, What Guidance Should I Follow? | David C. Izard | The U.S. Food & Drug Administration (FDA) released a number of binding guidance documents and companion materials that require clinical studies initiated on or after December 17, 2016 to utilize FDA endorsed data standards at the time the study is planned and executed if you intend to include the study as part of a future New Drug Application (NDA), Abbreviated New Drug Application (ANDA) or Biologics License Application (BLA). These guidance documents spend considerable effort documenting these new requirements but give little consideration to the body of clinical data that currently exists in legacy format. Furthermore, all previous guidance documents have now been deprecated in favor of these new, forward-looking guidances, leaving a void for how a Sponsor or Service Provider should prepare legacy data and related documentation for regulatory submission let alone when the submission of legacy format data is required or expected | data standard, clinical data, clinical trial, regulatory submission, crf, raw data, adam dataset, service provider, csr, sdtm domain, cdisc | source | 11 |
| 108 | Preparing the Data to Ensure Correct Calculation of Relative Risk | Ravi Kankipati, Abhilash Chimbirithy |
Relative Risk (RR) used frequently in statistical analysis can be easily obtained by using SAS procedure. However, how the data are organized before calling the SAS procedure will affect the outcome and could result in a n incorrect RR. From a programmer's perspective when asked to provide RR for a group, or subgroups within that group one should be very careful in having the data organized correctly. In this paper we will walk through one such approach to derive the desi red classification variables based on the data, focusing on organizing the data before finally calling SAS procedures to obtain a correct RR calculation | proc step, proc freq, proc tabulate, relative risk, sample, data science, sas programming, adam dataset | source | 13 |
| 109 | PROC REPORT: Compute Block Basics | Arthur L. Carpenter | One of the unique features of the REPORT procedure is the Compute Block. Unlike most other SAS procedures, PROC REPORT has the ability to modify values within a column, to insert lines of text into the report, to create columns, and to control the content of a column. Through compute blocks it is possible to use a number of SAS language elements, many of which can otherwise only be used in the DATA STEP. While powerful, the compute block can also be complex and potentially confusing. This tutorial introduces basic compute block concepts, statements, and usages. It discusses a few of the issues that tend to cause folks consternation when first learning how to use the compute block in PROC REPORT |
proc report, proc step, class variable, put statement, proc calis | source | 16 |
| 110 | PROC SQL: To Create Macro Variables with Multiple Values and the Uses in Clinical Programming | Anish Kuriachen | It is very easy to do programming with help of only data steps and procedures. But at times this might not be efficient. Especially when dealing/ working with multiple datasets with same operations. For example we may need to copy multiple datasets from one location to other location. We could do this using multiple data steps but it will be very lengthy and we may miss some of the variables or datasets in the operations. In these cases we could use of macro variable with multiple values and PROC SQL. The intent of this paper is to present a method to handle the macro variables with multiple values and its usage to work or modify multiple datasets and multiple variables in a dataset. This logic could be used in clinical programming in various scenarios |
macro variable, proc sql, proc step, clinical programming | source | 4 |
| 111 | Programming checks: Reviewing the overall quality of the deliverables without parallel programming | Shailendra Phadke, Veronika Csom |
The pharmaceutical and Biotech industry trend is slowly shifting from programming deliverables in house to outsourcing programming and data management responsibilities to Clinical Research Organizations (CROs) and Functional Service Providers (FSPs) using their knowledge and expertise. While the responsibilities are being handled by these external sources the programming lead at the pharmaceutical company is still accountable for the quality, accuracy and CDISC compliance of created deliverables. It's a daunting task to check and confirm the accuracy without actually performing parallel programming when presented with a tight timeline, certain key programming checks will assist in checking the overall quality of the deliverable. This paper illustrates a list of such programming and CDISC compliance checks that will aid a reviewer in assessing a deliverable efficiently | in=, proc sql, adam dataset, group by, secondary endpoint, proc step, cdisc | source | 8 |
| 112 | Programming Support for Exposure-Response Analysis in Oncology Drug Development | Peter Lu, Hong Yan |
Over the last decade Exposure-Response (ER) analysis has become an integral part of clinical drug development and regulatory decision-making. It plays an increasingly important role in the identification of early evidence of drug efficacy and safety and thus can support internal and external decision-making processes for evaluating drug benefit- risk and optimizing trial design. Unlike population pharmacokinetics analysis, however, the regulatory guidance and industry recommendations for ER analysis are still lacking in terms of the details of statistical modeling approaches, including multivariate logistic regression, linear mixed effects, nonlinear regression, and non-parametric or parametric Cox proportional-hazards regression. To ensure a successful ER analysis, quality SAS programming is essential in data preparation and presentation | regression analysis, proc phreg, ctrough, best response, proc step, geomean, logistic regression, change from baseline, logistic distribution, clinical research, proc nlin, response variable, study day, proc sgplot, adam dataset, adverse events, clinical trial, exposure, efficacy summary, overall survival, sample, data cleaning | source | 8 |
| 113 | QA and Compliance Insights Using the SCAPROC Procedure | Ben E. Bocchicchio, Sandeep Juneja |
The SCAPROC procedure is a new procedure that runs a SAS Code Analyzer. This SAS Code Analyzer captures metadata information about the contents of the SAS code that is run. It collects information on files that are used in the code's input, the code's output, and macro variables used while the code is running. How does this procedure relate to quality assurance (QA) and compliance? Just imagine if you could collect all the metadata about all the SAS code that is run to generate output for a FDA submission. You could programmatically prove that all the data referenced for input is consistently used, all macros called in the programs are consistent (without worrying about calling a generic macro when a project-specific macro is required), and determine that all designated output is saved to the correct location. Would this make you feel more confident about your submission? This presentation reveals these uses of SCAPROC |
proc step, view | source | 5 |
| 114 | Quality, Timely and Within Budget Analysis and Reporting - Yes, you can have all three! A process and tool to help achieve this goal | Wilminda Martin, Sharon Niedecken, Syamala Schoemperlen |
As the cost of conducting clinical trials is skyrocketing in today's competitive climate, the Pharma and the Biotech industries are stressing better efficiency, more precise resource forecasting, and tighter budgeting. The key to success is to implement efficient processes to allocate, monitor, and manage budgets and resources. In addition, early planning of analysis and reporting tasks and the resources to support those tasks is crucial. These provide a roadmap to operate efficiently and avoid uncontrolled changes to project scope, therefore enabling organizations to be cost-effective. To create accurate timelines and realistic budgets, the analysis and reporting requirements must be fully defined. It is important to have a complete understanding of the scope of the tasks in the preliminary stages of budgeting in order to set accurate budgets. Otherwise, significant changes to requirements will require significant modifications to the initial budgets | data cleaning, sample, clinical programming | source | 12 |
| 115 | Quick and Efficient Way to Check the Transferred Data | Divyaja Padamati | Consistency, quality and timelines are the three milestones that every department strives to achieve. As SAS Programmers, we often have to work with data generated by another team or a competitor company. Checking the data for compliance with CDISC or company standards will be a major hurdle in latter cases. In this paper we shall discuss how data compliance can be achieved in a quick and efficient manner. The first step in this process will be checking for redundant data i.e., removing any variables with all missing data or an observation where all the variables are missing. Secondly, we need to make a quick check for the presence of required variables as per the required standards. Finally, we have to create a list of required variables which are absent/not in transferred data. By letting SAS do all the work for us, the result will be faster and efficient. This paper will offer an approach to achieving all these goals by a click of a button | character variable, numeric variable, raw data, macro variable, missing value, in=, proc contents, proc step, cdisc | source | 6 |
| 116 | Recruiting and Retention Strategies for 2016 in the SAS Programmer Staffing Organizations | Helen M. Chmiel, Mindy Kiss, Andrea Moralez |
SAS Programmers in the IT staffing sector continue to be in high demand with projections of continued growth of 6% in 2016 (Braswell, December, 2015). IT overall employment from 1999 to 2013 jumped more than 41% compared with 5.5% for nonfarm employment. Additionally, median unemployment rate for "computer and mathematical occupations" through 3Q14 was 2.9% compared to the 5% overall median unemployment rate (Braswell, January, 2015). Based on these statistics, it is critical to business strategy to understand the challenges both recruiters and managers face in order to successfully mitigate them. An additional pressure on the Staffing sector is that organizations are relying more and more on a contingent workforce. The fierce competition to attract the best and the brightest talent among the limited supply of skilled workers to a market that is increasing in demand results in both a difficult recruiting and equally difficult retention environments | sas programmer, sas programming | source | 9 |
| 117 | Remember to always check your simple SAS function code! | Yingqiu Yvette Liu | In our daily programming work we may not get expected results when using seemingly clear logic and simple SAS functions. When we dig into the problem, we may discover the issue: either a SAS function was utilized incorrectly or the programming logic wasn't applied properly. In this paper, the author will use examples such as SCAN function and LAG function to demonstrate these points in an effort to share potential pitfalls and challenges when using SAS functions. Reviewing others' mistakes is often an excellent way to learn and improve our programming skills! | character functions, lag function, data step, character constant, in=, sas function | source | 8 |
| 118 | Removing Duplicates Using SAS | Kirk Paul Lafler | We live in a world of data - small data, big data, and data in every conceivable size between small and big. In today's world data finds its way into our lives wherever we are. We talk about data, create data, read data, transmit data, receive data, and save data constantly during any given hour in a day, and we still want and need more. So, we collect even more data at work, in meetings, at home, using our smartphones, in emails, in voice messages, sifting through financial reports, analyzing profits and losses, watching streaming videos, playing computer games, comparing sports teams and favorite players, and countless other ways. Data is growing and being collected at such astounding rates all in the hopes of being able to better understand the world around us | proc sort, proc step, proc means | source | 3 |
| 119 | Results-Level Metadata: What, How, and Why | Frank C. DiIorio, Jeffrey M. Abolafia |
The power of well-designed metadata has been widely demonstrated in recent years. Organizations creating standard-compliant FDA deliverables (SDTM and ADaM datasets, define.xml) find that the creation was simplified by using data that describe the deliverables' workflow and content - that is, metadata | adam dataset, where statement, sample, data cleaning, cdisc | source | 11 |
| 120 | ROC Curve: Making way for correct diagnosis | Manoj Pandey, Abhinav Jain |
The performance of a diagnostic test is based upon two factors, one how accurately it detects the disease and another how accurately it rules out the disease in healthy subjects. Scientifically, ROC (Receiver Operating Characteristics) curves help to evaluate the predictive accuracy of a diagnostic test. ROC Curves provides the ability to identify optimal cut-off points, evaluate performance comparison across multiple diagnostic tests and evaluate performance of a diagnostic test across multiple population samples. The property that makes this ROC Curves more desirable is that the indices of accuracy are least affected by arbitrarily chosen decision criteria. Calculations of Area Under Curve (AUC) and measures of accuracy determines the differentiating power of the test. This paper will focus on application of ROC Curves in clinical trial data analysis and deriving insights from ROC measures like sensitivity, specificity, AUC, Optimal Cut-off Point and Youden Index | roc curve, type ii error, type i error, proc logistic, logistic distribution, proc step, negative predictive value, sample | source | 12 |
| 121 | SAS Abbreviations: a Shortcut for Remembering Complicated Syntax | Yaorui Liu | One of many difficulties for a SAS programmer is remembering how to accurately use SAS syntax, especially the ones that include many parameters. Not mastering the basic syntax parameters by heart will definitely make one's coding inefficient because one would have to check the SAS reference manual constantly to ensure that one's syntax was implemented properly. One of the more useful tools in SAS, but seldom known by novice programmers, is the use of SAS Abbreviations. It allows users to store text strings, such as the syntax of a DATA STEP function, a SAS procedure, or a complete DATA STEP, with a user-defined and easy-to-remember abbreviated term. Once this abbreviated term is typed within the enhanced editor, SAS will automatically bring-up the corresponding stored syntax. Knowing how to use SAS Abbreviations will ultimately be beneficial to programmers with varying levels of SAS expertise. In this paper, various examples by utilizing SAS Abbreviations will be demonstrated |
date variable, proc import | source | 8 |
| 122 | SAS and R - stop choosing, start combining and get benefits! | Diana Bulaienko | The R software is powerful but it takes a long time to learn to use it well. However, you can keep using your current software to access and manage data, then call R for just the things your current software doesn't do. Learning to use a data analysis tool well takes significant effort and quite a lot of time, so this paper will be valuable to experienced SAS users who don't want totally switch to R, but want to use advantages of both SAS and R software. This presentation introduces a minimal amount of R commands you will need to know to work this way. It will also describe how to call R routines from SAS to create beautiful graphs | proc iml, dataset, proc step, data science | source | 7 |
| 123 | SAS Functions You May Have Been MISSING | Mira Shapiro | Those of us who have been using SAS for more than a few years often rely on our tried- and-true techniques for standard operations like assessing missing values. Even though the old techniques still work, we often miss some of the "new" functionality added to SAS that would make our lives much easier. In effort to ascertain how many people skipped questions on a survey and, what percentage of people answered each question, I did a search of past conference papers and came across 2 functions that were introduced in SAS 9.2-- CMISS and NMISS. By using a combination of these functions and PROC TRANSPOSE, a full missing assessment can be done in a concise program. This paper will provide examples and explore the features of the "newer" functions NMISS and CMISS and compare them with the longer standing MISSING function |
missing value, missing function, numeric variable, character variable, proc step | source | 8 |
| 124 | SAS integration with NoSQL data | Kevin Lee | We are living in the world of abundant data, so called "big data". The term "big data" is closely associated with unstructured data. They are called "unstructured" or NoSQL data because they do not fit neatly in a traditional row- column relational database. A NoSQL (Not only SQL or Non-relational SQL) database is the type of database that can handle unstructured data. For example, a NoSQL database can store unstructured data such as XML (Extensible Markup Language), JSON (JavaScript Object Notation) or RDF (Resource Description Framework) files. If an enterprise is able to extract unstructured data from NoSQL databases and transfer it to the SAS environment for analysis, this will produce tremendous value, especially from a big data solutions standpoint. This paper will show how unstructured data is stored in the NoSQL databases and ways to transfer it to the SAS environment for analysis. First, the paper will introduce the NoSQL database | sas programmer, dataset, unstructured data, rest api, big data, libname engine, proc sql, structured data | source | 9 |
| 125 | SAS Macro for Summarizing Adverse Events | Julius Kirui, Rakesh Mucha |
Adverse Events (AE) summaries are very important in determining the continuity of most early phase studies. These summaries can be reported in various ways or formats as requested by study reviewers, FDA, or other regulatory agencies. The majority of the mock shells created by the biostatistician specify that an AE dataset is best summarized at patient level, System Organ Class (SOC), and Preferred Term (PT) by worst grade and a subset of relatedness or seriousness. Writing and validating SAS programs to mimic these mock shells, especially for multi- cohort or randomize studies can be challenging, time consuming, and tedious. This paper will discuss a SAS macro program (%sumAE) that can summarize raw or ADaM AE datasets by simple manipulation of macro parameters. The flexibility to output one summary table at a time or output as many summary tables as possible to one folder is accounted for. This macro can be used by individuals with minimum SAS 9.3 skills | macro parameter, macro variable, proc report, system organ class, proc step, adverse event summary, adverse events, ctcae grade, group by, treatment arm, conditional do loop | source | 11 |
| 126 | SAS Office Analytics: An Application In Practice - Monitoring and Ad-Hoc Reporting Using Stored Process | Mansi Singh, Smitha Krishnamurthy, Chaitanya Chowdagam, Kamal Chugh |
There is always time constrains when it comes to ad-hoc reporting while the project work is on full swing. There are always numerous and urgent requests from various cross-functional groups regarding the study progress, e.g. enrollment rate and so on. Typically a programmer has to work on these requests along with the study work, which can be stressful. To address this need for monitoring of data in real time and tailor the requirements of clients to create portable reports, SAS has come out with a powerful tool SAS Office Analytics/Visual Analytics. SAS Office Analytics with Microsoft Add-in provides excellent real-time data monitoring and report generation capabilities with which a SAS programmer can take the reporting and monitoring to the next level. Using this powerful tool, a programmer can build interactive customized reports, which can be saved as a "stored process" and anyone can view, customize, and comment on these reports using Microsoft Office | excel, sas enterprise guide, sas management console, xls, sas programmer, dataset | source | 12 |
| 127 | SAS Techniques for Managing Large Datasets | Rucha Landge | As SAS programmers we often have to work with large data, having millions of rows, hundreds of column s. Usually it takes enormous time to process these datasets which can have an impact on delivery timelines. This problem triggers us to think that how can we reduce the time required for the execution and compress the size of the output data without losing any valuable information from the data. In this paper we will focus on techniques and concepts to compress the size of huge datasets and work with them efficiently to reduce the processing time. We will have a look at a few features available in the SAS System like COMPRESS, INDEX, and BUFSIZE which provide you with ways of decreasing the amount of room needed to store these data sets and decrease the time in which observations are retrieved for processing | dataset, character variable, numeric variable, bufsize, length statement, cpu time, keep statement, if-then-else, proc sql, set statement, where statement, proc step, index | source | 7 |
| 128 | Scalable Vector Graphics (SVG) using SAS | Yang Wang, Vinodita Bongarala |
Scalable Vector Graphics (SVG) is an XML -based vector graphic format, compatible with most modern browsers. Unlike pixel -based graphics which lose resolution when enlarged, Scalable Vector Graphics can be magnified infinitely without loss of quality for any screen resolution and size. Starting with SAS 9.2 version, Scalable Vector graphics is supported through Vector Graphics device. SVG can be produced by Graph Template Language (GTL) and traditional SAS/GRAPH procedures like PROC GPLOT/GCHART. This paper explains the difference between the two kinds of graphs and discusses the pros and cons of using them for clinical outputs. SAS features such as GTL and SGRENDER can be efficiently used to produce high quality SVG. Examples that use traditional SAS/GRAPH such as PROC GPLOT/GCHART procedures to produce SVG graphs are also included |
proc step, proc gplot | source | 6 |
| 129 | Schoveing Series 2: Self-Management: The Science of Balancing Work and Life | Priscilla Gathoni | Ever wondered why you don't have time to do something like reading, thinking, planning, developing (things and yourself), dreaming, or simply embracing harmoniously the beauty spots of life? Have your daily habits and mental attitude led you to a life of continued compliance with an unfathomable comfort zone that is slowly eating away at your strength, intelligence, and courage to change? Do your find yourself compromising your values, ability to be great, ability to achieve optimal health, and your ability to love? Is your knowledge limited to only that which you think you know? Are you living in the future and not forgiving the past? This paper will help you unlock 7 keys in your life that will help you embrace right-mindedness that will drive you, and help you prevail after dying to your old ways and being born to the perfect creation of your Mind | source | 13 | |
| 130 | Scrambled Data - A Population PK/PD Programming Solution | Sharmeen Reza | Population pharmacokinetics/pharmacodynamics (pop-PK/PD) modeling and simulation is a necessity in the drug discovery process. It allows PK scientists to evaluate and present safety and potency of a drug. Also regulatory agencies require population analysis results as part of submission package. Scientists' involvement in mainstream clinical study team is essential in aligning analysis timelines with study conduct activities. In order to support analyses, pop-PK/PD programmers create NONMEM-ready data sets at different stages of a trial. It is critical to deliver data sets to PK scientists in a timely manner enabling them to prepare models, and optimize based on updated data at each stage. Upon receiving final data, pop-PK/PD programmers produce NONMEM-ready data set in a short window after a study database lock. Due to the sensitivity of PK data, accessibility is a major difficulty that programmers face during the development phase | key, sample, clinical trial, data governance, clinical research, missing value, proc step, randomization, population pk | source | 8 |
| 131 | SDTM Automation with Standard CRF Pages | Taylor Markway | Much has been written about automatically creating outputs for the Study Data Tabulation Model (SDTM). This paper provides brief descriptions of common approaches in practice and details an approach to automatically create SDTM outputs when case report form (CRF) pages are uniquely identifiable. The process uses a fixed SAS program structure that allows programs to be automatically constructed and deconstructed. This approach depends on three assumptions: 1) all CRF pages are identifiable, 2) all CRF pages that have the same set of identifiers are unique, and 3) all CRF pages are consistently represented in the electronic database. At SCRI, the statistical programming team and the data management team worked to meet these assumptions while designing the standard database. After meeting these three assumptions we can automatically create SAS SDTM programs for each domain. These programs that are easy to read due to their systematic structures | sdtm domain, data cleaning, macro call, independent qc, raw data, sdtm programming, cdisc | source | 10 |
| 132 | SDTM Metadata: The Output is only as Good as the Input | Sue Sullivan | Capturing all of the intricacies, exceptions to the rules, and finer points of the SDTM IG takes both attention to detail and manual effort. Many companies configure their own interpretation of SDTM metadata to meet their specific sponsor needs and ensure compliance with federal regulations. However, CDISC has created SHARE, "…an electronic repository for developing, integrating and accessing CDISC metadata standards in electronic format."1 As part of SHARE, eSHARE files have been created that contain SDTM metadata for SDTM models v1.2-1.5, which are available to download only for CDISC Gold members. The eSHARE files may be used in a number of ways. For one, the files may be used as a starting point to compile a sponsor's metadata by IG and model version. Additionally, the files may be used for compliance checking of sponsor standards, MDR content, or study level metadata | label statement, trial summary, character variable, clinical trial, excel, cdisc | source | 9 |
| 133 | SDTM Trial Summary Domain: Putting Together the TS Puzzle | Kristin Kelly, Jerry Salyers, Fred Wood |
The SDTM Trial Summary (TS) domain was updated with new variables and implementation strategies with the publication of the SDTM v1.3/SDTMIG v3.1.3. This was an attempt to make the domain more useful to reviewers and to be machine readable to facilitate data warehousing. The FDA has recently considered TS as being essential to include in a study submission. They have developed tools that check that the TS domain is populated according to the guidance in the SDTMIG and the FDA's Study Data Technical Conformance Guide (TCG). Despite the advice for implementation, constructing an informative and complete TS domain can be challenging for sponsors in terms of interpreting the guidance and using the correct controlled terminologies. This paper will discuss some of the challenges associated with populating specific TS parameters, and provide some solutions for addressing these challenges | clinical trial, trial summary, controlled terminology, cdisc | source | 13 |
| 134 | Selecting Analysis Dates - A Macro Approach Using Raw Data for Expediting Results | Abdul Ghouse | Clinical trials data contain a variety of values including dates which support several key analyses such as Time to Event (TTE). It's often seen that the algorithms defined to conduct TTE analyses require the selection of a particular date that can be found across large number of datasets. Using a macro to create a single dataset with all the CRF collected dates allows selecting the event or censoring per patient basis. This approach simplifies the process improving efficiency and quality. In addition, using data from the raw SAS library allows for faster output creation and review of interim efficacy endpoints for decision making in a phase 1 study | adverse events, survival analysis | source | 4 |
| 135 | Setting the Percentage in PROC TABULATE | David W. Franklin | PROC TABULATE is a very powerful procedure which can do statistics and frequency counts very efficiently, but it also it has the capability of calculating percentages on many levels for a category. This paper looks at the automatic percentage calculations that are provided, and then delves into how a user can specify the denominator for your custom percentage |
proc tabulate, proc step, proc freq | source | 6 |
| 136 | Simulation of Data using the SAS System, Tools for Learning and Experimentation | Kevin R. Viel, Bevan K. Youse |
Statistical models, like many fields of mathematics, rely upon assumptions (postulates). The successful use of these tools conventionally involves examination of corresponding statistics that inform the statistician whether violations of model assumptions might have occurred. Simulation, enabled by ever increasing computational power, is making experimentation a mainstay of statistics and mathematics. Further, the ability to simulate data should be required of every student of statistics, much like how the fluency of matrix "language" and the ability to code the likelihood should be requisite skills. The goal of this paper is to introduce simulations using the SAS System and to provide the technical (programming) and statistical basis to examine the use of models in time to event data with special consideration of recent and important reports of inhibitors in Hemophilia A patients | confidence interval, binomial distribution, logistic regression, poisson distribution, regression analysis, logistic distribution, macro facility, poisson regression, likelihood function, sample | source | 11 |
| 137 | Standards Implementation & Governance: Carrot or Stick? | Julie Smiley, Judith Goud |
data cleaning, clinical data | source | 6 | |
| 138 | Strategic Considerations for CDISC Implementation | Amber Randall, William Coar |
The Prescription Drug User Fee Act (PDUFA) V Guidance mandates Electronic Common Technical Document (eCTD) format for regulatory submissions by May 2017. The implementation of CDISC data standards is not a one- size-fits-all process and can present both a substantial technical challenge and potential high cost to study teams. There are many factors that should be considered in strategizing when and how to implement which include timeline, study team expertise, and final goals. Different approaches may be more efficient for brand new studies as compared to existing or completed studies | adam dataset, data cleaning, data governance, clinical research, interim analysis, raw data, sdtm domain, cdisc | source | 6 |
| 139 | Stylish Kaplan-Meier Survival Plot using SAS 9.4 Graph Template Language | Setsuko Chiba | An eye catching graph provides a visual display of data summaries reported in tables. In Oncology clinical trials, the commonly used method to display the results of time to event data analysis is to combine the Kaplan-Meier Survival Plot in PROC LIFETEST with ODS Graphics to compare different treatment groups based on the log-rank test. The new release of SAS/STAT 14.1 added many options to control the appearance and format of survival outputs not available in previous versions. Nevertheless, SAS programmers still find that the options in PROC LIFETEST are not sufficient to produce the outputs data reviewers desire. If you are in this situation, the solution is to use SAS 9.4 Graph Template Language (GTL). This paper provides a step-by-step approach to modify the macros and macro variables used in the graph template. SAS programmers at all levels are able to control the results of survival plots on Windows and Unix OS environments |
kaplan-meier plot, graph template language, macro variable, proc step, sas programmer, macro definition | source | 7 |
| 140 | Submission-Ready Define.xml Files Using SAS Clinical Data Integration | Melissa R. Martinez | SAS Clinical Data Integration simplifies the transformation of raw data into submission-ready datasets that conform to CDISC data standards. It also has a built-in transformation that creates a define.xml file from a study's CDISC domains, with just a few simple selections required from the end user. With the appropriate meta data definitions, the built-in transformation will pick up and include computational algorithm s and controlled terminology codelists in the resulting define.xml file. In SAS Clinical Data In tegration 2.6, a new feat ure was added that simplif ies the process of adding information about supplemental documents and value-level me tadata to the defi ne.xml file. This paper will provide examples and instructions for creating a submission-ready define.xml file complete with the appropriate computational algorithms, controlled terminology codelists, value-level metadata, and supplemental documents using SAS Clinical Data Integration | data standard, controlled terminology, cdisc | source | 10 |
| 141 | Summarizing Adverse Events of Interest - Onset, Duration, and Resolution | John Saida Shaik, Avani Kaja |
One of the main objectives of clinical trials is to study the safety of the drug. To understand the safety of a drug in a first-in-human clinical trial, it is important to study adverse events and their corresponding severity grades. In most of the clinical trials there will be specific AE's of interest that need to be analyzed in detail. Summarizing the onset, duration and resolution of AE's of interest will provide the medical monitor with important information to determine the safety of the drug. In this paper, we will introduce the data analysis & presentation of treatment -emergent AE onset, AE duration and time-to -resolution of AE's of interest, after End-of -Treatment using SAS. The onset data analysis provides a summary of time to first onset of AE of interest. The duration data analysis provides the duration of first occurrence of grade 3 or 4 events, to improvement or resolution | adverse events, clinical trial, data science, trial elements | source | 6 |
| 142 | Superior Highlighting: Identifying New or Changed Data in Excel Output using SAS | Kim Truett | Non-SAS-users often review data in EXCEL. As these outputs grow larger, the ability to identify new or changed data is inval- uable to the reviewer. This is easily identified in SAS using procedures such as PROC COMPARE. However, it is not obvi- ous how to automate representation of this in EXCEL. Just prior to PharmaSUG 2013, I was approaching a solution to get SAS to automate this but could not figure out the final steps. So I took this puzzler to Coders Corner. During the hour that fol- lowed, John King, Gary Moore, and I devised a solution. This paper is a presentation of that brainstorming session. This paper will show how a SAS program can be used to automatically identify new or changed data using rtf codes that change the color of the cell background to indicate new or changed data in an EXCEL output |
change from baseline, excel, proc compare, proc step, ods excel, flag variable, proc report | source | 8 |
| 143 | Surviving the SAS Macro Jungle by Using Your Own Programming Toolkit | Kevin Russell | Almost every night there is a reality show on TV that shows someone trying to survive in a harsh environment by using just their wits. Although your environment is not as harsh, maybe you feel you do not have all the tools you need to survive in the macro programming jungle. This paper's purpose is to provide you with more programming tools, to make life in the jungle a whole lot easier. For example, the new DOSUBL function is the flint that will ignite your code and enable you to dynamically invoke your macro using DATA STEP logic. Another tool is the stored compiled macro facility, which can provide protection from the elements by enabling you to keep your code safe from prying eyes. Also included is a discussion of the macro debugging system options, MLOGIC, SYMBOLGEN, and MPRINT. These options act as a compass to point you in the right direction. They can help guide you past a log riddled with errors to an error-free log that puts you back on the right path |
dosubl, macro variable, call symputx, options statement, call execute, macro facility, macro definition, proc report, stored macro, proc step, macro debugging, by group processing | source | 16 |
| 144 | Swimmer Plot by Graphic Template Language (GTL) | Baiming Wang | Time-to-Event analysis is one of the key analyses for oncology studies. Besides the Kaplan-Meier survival step curve to summarize overall drug performance, investigators and sponsors are also very interested in individual subject response situations. Many oncology clinical trials request Swimmer plots to show individual response trends on a continuing basis. Graphic Template Language (GTL) is a powerful approach for all kinds of visual clinical data presentations. Swimmer plots can presents all subject time-to-event data such as Complete Response Start, Partial Response Start, Continued Response after Study Drug Exposure, Disease Progression, Death, tumor types, etc. All these data can be shown on a Swimmer bar with different symbols/color combinations. Different colors can represent different tumor types or tumor stages. Subject IDs can be shown on each bar for easy review. Each event has data labeled on each marker to show exact days for that event | swimmer plot, exposure, complete response | source | 9 |
| 145 | The Benefits and Know-how of Building a Central CDISC-Terminology Dictionary with a Macro System | Solomon Lee, Ronghai Bo |
As FDA pushes for submission in CDISC format for NDAs, more and more companies start to pay more attention to handling the time consuming compliance issues in CDISC terminology. An example of the issue is that in the CM domain of a particular study, there are about 150 unique entries in the variable cmdosfrq. Less than one third of them are C-term compliant while the rest are not. This shows that there is a need for a systematic and efficient way to handle this issue. The benefits are: First, a study level format dictionary can be efficiently established so as to map the non-compliant entries of those variables that fall into the covering range of the C-term (CDISC terminology). Second, the study level C-term dictionary can be used as a building block for a central C-term dictionary either at the project level or at the therapeutic level. As a result, the processing efficiency will be further improved as more studies are processed | excel, create table, cdisc | source | 4 |
| 146 | The CDISC's are coming!! | Annapurna Ravi, Caroline Gray |
2016 is here, bringing with it the much awaited NDA requirements to be CDISC-compliant for FDA submissions. For those few teams who are still latching onto a legacy environment, the need of the hour is to be CDISC -'trained'. How does the management team choose the correct approach that is a right fit for their team unaccustom ed to CDISC standards? Who will train the teams, maintain team morale and all the while keeping an 'eye' on the budget ? Prioritizing and preparing a team for a major change such as legacy to CDISC seems to be the cross -roads where most CROs still dealing with legacy data are at these days. How do we hit the bullseye? | clinical trial, audit trail, cdisc | source | 7 |
| 147 | The Devil is in the Details - Reporting from Pinnacle 21 (OpenCDISC) Validation Report | Amy Garrett, Chris Whalen |
Pinnacle 21 Community Validator (P21C, previously known as OpenCDISC Community) is a valuable tool for SDTM implementers, but the resulting report has some limitations. The P21C report does not display full information about erroneous records, making it difficult for the reviewer to discern which issues are caused by dirty data versus incorrect programming or data mapping. This limitation means programmers must manually look up records based on observation number (provided on the details tab of the P21C report), which is extremely time-consuming. This cumbersome process creates the need for a detailed listing report to expedite the review of P21C validation findings. The resulting report includes a series of customizable listings organized by domain and issuer ID (FDA Publisher ID) | sample, macro variable, raw data, data review, proc sql, data cleaning, cdisc | source | 11 |
| 148 | The Dynamic Duo: ODS Layout and the ODS Destination for PowerPoint | Jane Eslinger | Like a good pitcher and catcher in baseball, ODS layout and the ODS destination for PowerPoint are a winning combination in SAS 9.4. With this dynamic duo, you can go straight from performing data analysis to creating a quality presentation. The ODS destination for PowerPoint produces native PowerPoint files from your output. When you pair it with ODS layout, you are able to dynamically place your output on each slide. Through code examples this paper shows you how to create a custom title slide, as well as place the desired number of graphs and tables on each slide. Don't be relegated to the sidelines-increase your winning percentage by learning how ODS layout works with the ODS destination for PowerPoint |
proc step, output delivery system, title statement, end, by group processing | source | 14 |
| 149 | The Hadoop Initiative: Supporting Today's Data Access and Preparing for the Emergence of Big Data | Michael Senderak, David Tabacco, Robert Lubwama, David O'Connell, Matt Majer, Bryan Mallitz |
Currently Merck's observational research activities rely heavily upon electronic medical record (EMR) and electronic administrative insurance claim (AIC) data, which are purchased from vendors and often stored in-house on the current Oracle Exadata platform. This platform provides efficient storage and access to these types of electronic databases, which usually are organized as traditional structured relational database tables that may approach billions of observations in size. However, as the rapid acceleration of worldwide electronic data generation continues, new sources of nontraditional data are expected to become increasingly relevant to pharmaceutical research. These new sources, collectively termed 'Big Data', are characterized as potentially massive and arriving in various formats, often unstructured--features that will render them increasingly less compatible with traditional computer architecture | pharmaceutical industry, data wrangling, insurance claims | source | 9 |
| 150 | The Impact of Change from wlatin1 to UTF-8 Encoding in SAS Environment | Hui Song, Anja Koster |
As clinical trials become globalized, there has been a steadily strong growing need to support multiple languages in the collected clinical data. The default encoding for a dataset in SAS is "wlatin1". wlatin1 is used in the "western world" and can only handle ASCII/ANSI characters correctly. UTF-8 encoding can fulfill such a need. UTF-8 is a universal encoding that can handle characters from all possible languages, including English. It is backward compatible with ASCII characters. However, UTF-8 is a multi-byte character set while wlatin1 is a single- byte character set. This major difference of data representation imposes several challenges for SAS programmers when (1) import and export files to and from wlatin1 encoding, (2) read in wlatin1-encoded datasets in UTF-8 SAS environment, and (3) create wlatin1-encoded datasets to meet clients' needs | dataset, txt, proc step, excel, subject visits, proc contents, sas enterprise guide, length statement | source | 12 |
| 151 | The Little-Known DOCUMENT Procedure, a Utility for Manipulating Output Delivery System (ODS) Content | Roger D. Muller | The DOCUMENT procedure is a little-known procedure that can save you vast amounts of time and effort when managing the output of your SAS programming efforts. This procedure is deeply associated with the mechanism by which SAS controls output in the Output Delivery System (ODS). Have you ever wished you didn't have to modify and rerun the report-generating program every time there was some tweak in the desired report? PROC DOCUMENT enables you to store one version of the report as an ODS Document Object and then call it out in many different output forms, such as PDF, HTML, listing, RTF, and so on, without rerunning the code. Have you ever wished you could extract those pages of the output that apply to certain "BY variables" such as State, StudentName, or CarModel? With PROC DOCUMENT, you have where capabilities to extract these |
proc step, output delivery system, sas enterprise guide | source | 8 |
| 152 | The New STREAM Procedure as a Virtual Medical Writer | Joseph W. Hinson | One of the really cool features of SAS 9.4 is the STREAM procedure. This new tool allows free- form text containing macro elements to be directly streamed to an external file, completely bypassing the SAS Compiler. The SAS Word Scanner allows the Macro Processor to resolve the macro elements after which text and graphical elements are streamed directly to the external file without engaging the SAS Compiler. This means SAS syntax violators like HTML and XML tags would not trigger errors. This exception permits greater freedom in composing text for processing, and thus very much suited for reports such as patient narratives, patient profiles, reviewer's guide, and investigator's brochures. Such documents typically contain data in a variety of formats: textual paragraphs interspersed with bulleted lists, tables, images, graphs, schematics, and listings, all of which are easily incorporated by the use of macro elements like %includes, macro variables, and even macro calls |
proc step, macro call, macro variable, adverse events, concomitant medications, clinical trial, hash table, macro definition | source | 13 |
| 153 | The Power of Data Access Functions: An example with Dataset-XML Creation | Joseph W. Hinson | Data Access Functions are part of the SAS Component Language or "SCL" (formerly called "Screen Control Language"). Currently, they are not so widely used in clinical programming, except for the OPEN function which is commonly used in SAS macros when one might need access to data without dealing with a step boundary. But many more functions are available, such as FETCHOBS, VARNUM, and GETVARC. As the name suggests, they provide access to the SAS data set in a far more flexible way than one can obtain with the SET statement of the DATA STEP. They are particularly useful in converting tables of dissimilar structures. The SAS data set is two-dimensional, whereas data structure of the Extensible Markup Language, XML, is hierarchical. Thus converting data sets to XML can be quite challenging. This paper will show that utilizing Data Access Functions can make the conversion of SAS data sets to XML quite straightforward and easy |
sample, xml, cdisc | source | 9 |
| 154 | The Power of Perl Regular Expressions: Processing Dataset-XML documents back to SAS Data Sets | Joseph W. Hinson | The seemingly intimidating syntax notwithstanding, Perl Regular Expressions (PRE) are so powerful they can overcome and parse the most complex non-uniform textual data. A "regular expression" is a string of characters that define a particular pattern of data, and is used for matching, searching, and replacing text. In SAS, PRE is implemented via the PRX functions such as PRXPARSE, PRXMATCH, PRXCHANGE, and PRXPOSN. Consider a situation where a date has to be extracted from data and the date can be present in a wide variety of forms: "Jan 1, 1960", "January 1st, 1960", "1st January, 1960", "1/1/60", "01:01:1960". With PRE, all the above forms of dates can be deciphered with the same, single PRXPARSE code, the way the human eyes can quickly glance through all those date formats and instantly know they are always referring to the same "first day of the first month of the year 1960" | concomitant medications, medical history, cdisc | source | 9 |
| 155 | The Standard for the Exchange of Nonclinical Data (SEND): History, Basics, and Comparisons with Clinical Data | Fred Wood | The CDISC Standard for the Exchange of Nonclinical Data (SEND) Implementation Guide (SENDIG) contains domains for general toxicology, pharmacology, and carcinogenicity studies. A separate implementation guide (SEND- DART) contains domains for reproductive toxicology studie s. The first SEND Model was developed in 2002, utilizing domains described in the CDER 1999 Guidance. In 2007, an effort began to completely align the SENDIG with the SDTM Implementation Guide (SDTMIG), with the first such version (v3.0) published in 2011. Since that time, the SEND Team has been working to create more examples, clar ify existing text and examples, and add new domains. Version 3.1, which actually underwent two public reviews (i n 2014 and 2015), is expected to be posted in Q2 of this year (2016). This paper provides an over view of the history of SEND and its cl ose ties with the development of the SDTM and the SDTMIG | clinical trial, randomization, clinical data, study day, trial summary, sample, cdisc | source | 10 |
| 156 | There is No Time Like Present-Being from Elf to the True Self | Rajinder Kumar, Sudarshan Reddy Shabadu, Houde Zhang |
In the past few years, pharmaceutical industries have been evolving fast along with enhanced ethics in conducting clinical trials to improve the quality of human life. The people like Biostatisticians, Clinical SAS Programmers etc. play a vital role in clinical trial studies to bring up the quality in reports by enhancing the existing analysis or macrotizing the tedious tasks, in most cases, failure to bring hands-on-work up thats really worthy of sharing to a global audience, having had good situations and surroundings, feeling inspired from inside but failing to act out is because of lacking something to downgrade their spirits. People often fail to think "W hat Exactly is Needed to Write a Paper" than "What is Overrated by Themselves". This paper covers the worthy points that clear the air between these two declarative sentences. Going by human perceptions, people always expect to have some motivation or support or helping hand in some way to reach their goals | clinical trial, clinical sas | source | 4 |
| 157 | Tips and Tricks for Bar Charts using SAS/Graph Template Language | Randall Nordfors, Boxun Zhang |
Bar charts are frequently used in clinical reporting to visually display quantitative information and identify patterns for a variety of purposes. SAS/GRAPH Template Language (GTL) provides powerful functionality to create sophisticated analytical graphics independent of the need for statistical procedures. Programmers may face situations where a conceptual idea for a graph display style is specified by a biostatistician or medical writer, even though there may not be a natural programming technique, or available creative precedent, for generating it using SAS/GRAPH. In this paper, using selected tips and tricks, we focus on ways to help you solve bar chart programming challenges efficiently. Our examples included in this paper are: How to create mirror bar chart for side by side comparison around a single zero point on the x-axis. How to display multiple distinct bar pairs within a single axis graphing infrastructure |
bar chart, proc step, adverse events, proc template, proc freq, histogram | source | 8 |
| 158 | Tips and tricks when developing Trial Design Model Specifications that provide Reductions in Creation time | Ruth Marisol Rivera Barragan | This presentation will give you some key points when developing trial design model specification under SDTM versions (up to 3.2). After the presentation, you will have a clearer idea of what inputs are needed to gather, as well as understand in advance of starting work on the trial design domains, in order to move efficiently through the definition process. It is important to have: the latest protocol; a summary of the Inclusion/Exclusion criteria changes; the SDTM Specification Template; the latest version of Electronic Case Report Form (eCRF) as well as the specifications for DM, IE, SE and SV domains before beginning to create the Trial Design domains. The presentation will also recognize the main differences within SDTM IG version 3.1.3 and older versions for the Trial Design Model (TDM) domains. A discussion will present the critical cross checking between TDMs and other SDTM domains that is necessary to consider for the most efficient specification development, for example: 1 | trial design model, trial summary, sdtm domain, cdisc | source | 10 |
| 159 | Tips on Checking and Manipulating Filenames with SAS | Solomon Lee | This is a 10 minute paper with tips on 1. Checking the filenames in a folder against those in EXCEL TOCs. 2. Checking the timestamps of two related lists of filenames for internal logics. 3. Partial replacement of filenames for long descriptions in the bookmarks of combined PDF file with multiple tables or graphs |
source | 4 | |
| 160 | To IDB or Not to IDB: That is the Question | Kjersten Offenbecker, Beth Seremula |
In Shakespeare's Hamlet we hear Prince Hamlet ask the now cliché "To be or not to be" question as he contemplates suicide. How does this relate to ADaM integrated databases (IDBs)? As Hamlet weighs the pros and cons of death, we too must decide whether it is better to stick with the status quo or venture into the unknown world of integrating our ADaMs. We shall examine the pros and cons of ADaM IDBs as well as some of the basic pitfalls we have come across while undertaking this daunting task. Along this journey we will show why we think IDB is the future and why it is better to be on the cutting edge | adam dataset, sdtm domain, cdisc | source | 7 |
| 161 | Transforming Biomarker Data into an SDTM based Dataset | Kiran Cherukuri | Biomarkers play an increasingly important role in drug discovery and development. They are used as a tool for understanding the mechanism of action of a drug, investigating efficacy and toxicity signals at an early stage of pharmaceutical development, and in identifying patients likely to respond to a treatment. This paper provides an Introduction to the impl ementation of SDTM standards for data that defines a genetic biomarker and data about genetic observations. The draft CDISC SDTM Pharmacogenomics/Genetics Implementation Guidance will be referenced and rationale for using specific aspects of the draft guid ance or suggesting a modification will be explained. The variables used, considerations taken and the process for setting up the pharmacogenomics/ genetics biomarker domains will be described | clinical trial, dataset, sample, clinical research, sdtm domain, cdisc | source | 7 |
| 162 | Trivial Date Tasks? PROC FCMP Can Help | Jueru Fan | Imputing partial date, comparing dates and creating flags are important tasks when working with CDISC Analysis Data Model (ADaM) data sets. If function or CALL routines are available to help us deal with them and therefore free us from repetitive works, we can better focus on tasks requiring more logical thinking. Yes, the SAS Function Compiler Procedure (PROC FCMP) is there for us. This paper will first look at some basic syntax of PROC FCMP to build functions and CALL routines. Then two user -defined functions and a CALL routine will be presented on comparing complete/partial date, imputing partial date and creating pre concomitant flag, concomit ant flag, and post concomitant flag, respectively |
proc fcmp, proc step, concomitant medications, put statement, sas log, cpu time, sas function, imputation, cdisc | source | 10 |
| 163 | TRTP and TRTA in BDS Application per CDISC ADaM Standards | Maggie Ci Jiang | CDSIC ADaM Implementation Guide v1.1 (IG) [1]. has defined the standards on how to use the TRTP and TRTA terminology when developing ADaM DBS datasets, and provided users some examples to illustrate how to utilize the standards. However, the definitions and examples from ADaM IG are limited to the applications that data is expected to be in good standing. There will be some sort of challenges for crossover studies when it comes to the situation that repeated visit record, unscheduled visit record or the record out of treatment visit window occur. This paper will present a review of experiences implementing TRTP and TRTA in ADaM BDS datasets, and discuss the comprehensive utilization of TRTP and TRTA terminology step by step with some practical examples | crossover study, if-then-else, cdisc | source | 8 |
| 164 | Unequalslopes: Making life easier when the proportional odds assumption fails | Matthew J. Wiedel | With the advent of SAS 9.3 came the PROC LOGISTIC model option, unequalslopes. This new option allows the programmer to quickly produce results for cumulative logit models which fail the assumption of proportionality. Models thus become either partially proportional or non-proportional. Previously, these models could be programmed using the PROCEDURE NLMIXED, but this new capability of PROC LOGISTIC provides an easier task for the SAS writer and an analyst friendly output. Ordinal responses use these models to capture the inherent information of their ordering. Hoping to make it easy to generalize the interpretation and programming of partial and non-proportional logit equations, the response variable, number of episodes, will be a count (1,2,3, or 4) against 2 categorical independent variables, rank and level, with 5 and 3 categories, respectively |
logistic distribution, odds ratio, proc logistic, logistic regression, predictor variable, confidence interval, response variable, regression analysis, likelihood function | source | 11 |
| 165 | Up-Versioning Existing Define.xml from 1.0 to 2.0 | Jeff Xia, Lugang Xie (Larry) |
As per the latest Standards Catalog released by the FDA, both define.xml 1.0 and 2.0 are acceptable for data submissions like NDA. However, because of the technical insufficiency of define.xml 1.0, the agency specifically encourages the industry to submit the define.xml of version 2.0. It is highly possible that multiple studies are included in a data submission. Some of them may have been done in early years with an older version of define.xml, while other studies may have implemented a newer version. To keep the define xml version consistent in a single submission, there is a need for generating newer version of define.xml for these old studies. Considering that significant amount of work had been invested to check the validity and compliance of these define files in older version, it is more efficient and cost effective to convert these define.xml from version 1.0 to 2.0 without losing information | excel, sample, cdisc | source | 7 |
| 166 | Usage of Pinnacle 21 Community Toolset 2.1.1 for Clinical Programmers | Sergiy Sirichenko, Michael DiGiantomasso, Travis Collopy |
All programmers have their own toolsets like a collection of macros, helpful applications, favorite books or websites. Pinnacle 21 Community is a free and easy to use toolset, which is useful for clinical programmers who work with CDISC standards. In this Hands-On Workshop (HOW) we'll provide an overview of installation, tuning, usage and automation of Pinnacle 21 Community applications including: Validator - ensure your data is CDISC compliant and FDA submission ready, Define.xml Generator - create metadata in standardized define.xml v2.0 format, Data Converter - generate EXCEL, CSV or Dataset-XML format from SAS XPT, and ClinicalTrials.gov Miner - find information across all existing clinical trials |
data cleaning, excel, data validation rules, clinical trial, controlled terminology, cdisc controlled terminology, clinical research, cdisc | source | 19 |
| 167 | Using GTL Generating Customized Kaplan-Meier Survival Plots | Joanne C. Zhou | In cardiovascular and oncology clinical trials time-to-event endpoints such as overall survival and progression-free survival are the main focal point of clinical interests and usually graphically displays of the Kaplan Meier (KM) curve plays a key role in presenting the key clinical results and importance. With advent of SAS Graph Template Language (GTL) and newer version of the LIFETEST procedure, generation of conventional KM curves becomes much easier. However, some adhoc requests for customized KM plots could be still quite a challenge. In this paper, I use SAS GTL, ODS and SAS macros along with other SAS statistical analysis procedures to overcome some of the challenges to generate the customized KM plots |
proc means, out=, proc step, proc phreg, proc template, regression analysis | source | 12 |
| 168 | Using PROC GENMOD with count data | Meera G. Kumar | Use of PROC GENMOD in clinical trials data is quite common and more straightforward due to the availability of patient level data. How do you use the procedure to calculate event rate ratio using counts data? The key is to set up dummy variables for each dose level along with the 'offset' option. There can be situations in the Epidemiology area where you get only summary data for the number of event s in each dose group or treatment arm. In the example discussed here, there is the unique situation where the numerator is not necessarily part of the denominator. This happens because the event data is pooled from various sources. This paper demonstrates how to use the count data and set up a Poisson distribution for the calculation of rate ratio along with the confidence interval and its associated p-value |
adverse events, proc genmod, proc step | source | 4 |
| 169 | UTF What? A Guide to Using UTF-8 Encoded Data in a SDTM Submission | Michael Stackhouse | The acronyms SBCS, DBCS, or MBCS (i.e. single, double, and multi-byte character sets) mean nothing to most statistical programmers. Many do not concern themselves with the encoding of their data, but what happens when data encoding causes SAS to error? The errors produced by SAS and some common workarounds for the issue may not answer important questions about what the issue was or how it was handled. Though encoding issues can apply to any set of data, this presentation will be geared towards an SDTM submission. Additionally, a common origin of the transcoding error is UTF-8 encoded source data, whose use is rising in popularity by database providers, making it likely that this error will continue appear with greater frequency. Therefore, the ultimate goal of this paper will be to provide guidance on how to obtain fully compliant SDTM data with the intent of submission to the FDA from source datasets provided natively in UTF-8 encoding | proc step, raw data, proc contents, proc print, permanent dataset, proc options, cdisc | source | 10 |
| 170 | Validation Methods and Strategies for Presentation of Clinical Reports: The Programmers Road Map to Success | Vijayata Sanghvi | Validation is one of the core tasks of programming with in pharmaceutical industry with in clinical reporting space. This is very important job roles which weigh more responsibility on programmer's knowledge and experience to make sure the end product meet the study design requirements. This paper will outline list of methods and strategies a programmer can adopt to succeed in qualitative submission of clinical reports before been released to respective stake holders with in an organization or external agency for review. Further this paper will outline on the detailed tasks and guidelines on what elements of output will be validated and how, through a QC plan check list | data cleaning, mock shell, raw data | source | 5 |
| 171 | Waterfall plot: two different approaches, one beautiful graph | Ting Ma | This paper discusses two approaches to presenting oncologic data by use of a waterfall plot. One approach focuses on the statistical graphics (SG) family of procedure (SGPANEL) and the other utilizes the Graph Template Language (GTL). Both approaches are within the ODS Graphics system available with SAS 9.2 and later release. This paper will compare the specific SAS procedures to generate the visually effective waterfall plot. Basic plots will be built as the first step and then customized features will be added. These procedures should provide guidance to present stratified information from a complex clinical trial |
proc sgpanel, proc step, waterfall chart, in=, proc template, outlier | source | 9 |
| 172 | What HIGHLOW Can Do for You | Kristen Much, Kaitlyn Steinmiller |
Longitudinal plots that quickly, creatively, and informatively summarize study data act as powerful tools in understanding a clinical trial. The HIGHLOW statement not only creates a plot with floating bars that represent high and low values but also includes the ability to add markers and text annotations. With a wide variety of options, the HIGHLOW statement can produce a plot that summarizes a complex story with a multitude of different data in a single graphic. This paper will introduce this relatively new plot statement (in production since SAS 9.3) with an example-based approach, exploring possible applications and plot capabilities. Focus will be placed on how to create HIGHLOW plots using both the PROC SGPLOT procedure and Graph Template Language (GTL). The examples provided will demonstrate the power and flexibility of the HIGHLOW statement |
adverse events, concomitant medications, clinical trial, options statement, proc sgplot | source | 12 |
| 173 | What is high quality study metadata? | Sergiy Sirichenko, Max Kanevsky |
High quality study metadata is an important part of regulatory submission since it allows reviewers to interpret and understand submitted data, which means your submission can potentially move through the process more quickly. However, poor study metadata is most often cited by reviewers to be deficient. In fact, 77% of submissions in 2015 could not be loaded into FDA Clinical Trial Repository mostly due to issues with Define.xml and Trial Summary dataset. In this presentation, we will share the most common issues with study metadata in our industry and provide recommendations how to avoid or correct them to ensure successful regulatory submission | controlled terminology, regulatory submission, cdisc controlled terminology, edc, reviewer guide, data cleaning, clinical trial, cdisc | source | 9 |
| 174 | What makes a "Statistical Programmer" different from a "Programmer" | Arun Raj Vidhyadharan, Sunil Mohan Jairath |
In clinical SAS programming world programmers come from different backgrounds like engineering, biotech, biomedical etc. and everyone knows how to write a program as per specifications. At the same time some programmers who understand statistics can look beyond records and their statistical awareness helps them to understand data in a better way. Statistics is not about all crazy formulas, but it's a set of tools that helps us in understanding, analyzing and presenting clinical trial data. We all produce lot of tables and at the same time having basic understanding of statistics will be beneficial for us as we can contribute more than just creating TLFs. So next time when statistician asks to provide standard error instead of standard deviation; or statistician suggested using different formulae each time they calculate p-value we can also provide our input | sample, null hypothesis, p-value, proc calis, degrees of freedom, correlation, shift table, standard deviation, scatter plot, clinical trial, interval variable, sample size, statistical programmer, proc step, box plot, nominal variable, class variable, sas syntax, data science, median, alternative hypothesis, binary variable | source | 12 |
| 175 | What's Hot - Skills for SAS Professionals | Kirk Paul Lafler | As a new generation of SAS user emerges, current and prior generations of users have an extensive array of procedures, programming tools, approaches and techniques to choose from. This presentation identifies and explores the areas that are hot and not-so-hot in the world of the professional SAS user. Topics include Enterprise Guide, PROC SQL, PROC REPORT, PROC FORMAT, Macro Language, ODS, DATA STEP programming techniques such as arrays and hashing, business intelligence, sasCommunity.org, JMP, SAS/GRAPH, SAS/STAT, and SAS/AF |
sas 9, sas base, proc step | source | 6 |
| 176 | What-s the Case? Applying Different Methods of Conducting Retrospective Case/Control Experiments in Pharmacy Analytics | Aran Canes | Retrospective Case/Control matching is an increasingly popular approach to evaluating the effect of a given treatment. There is some theoretical literature comparing different methods of case/control matching, but there is a lack of empirical work in which each of these methods is employed. In this paper, I use SAS to conduct a retrospective case/control experiment on the efficacy and safety of two distinct drug options using Propensity Score Matching, Mahalanobis Metric Matching with Propensity Score Caliper and Coarsened Exact Matching. I then compare outcomes from all three methods to try to develop a sense of the advantages and disadvantages of each. In this example, despite a considerable lack of overlap in the output datasets, all three methods lead to similar results: evidence of increased effectiveness for one of the drugs with little to no difference in safety | propensity score, propensity score matching, treatment effect, mahalanobis distance, average treatment effect, clinical trial, nominal variable, insurer | source | 8 |
| 177 | When ANY Function Will Just NOT Do | Richann J. Watson, Karl Miller |
Have you ever been working on a task and wondered if there might be a SAS function that could save you some time? Let alone might be able to do the work for you? Data review and validation tasks can be time consuming efforts that with any gain in efficiency is highly beneficial, especially if you can achieve a standard level where the data itself can drive parts of the process. The 'ANY' and 'NOT' functions can help alleviate some of the manual work in many tasks from data review of variable values, data compliance and formats through the derivation or validation of a variable's datatype. The list goes on. In this paper we simply cover the functions and a summary of their details in use, and also over a couple of examples in handling SDTM/ADaM data and use for the define.xml process | raw data, time variable, cdisc | source | 8 |
| 178 | Working with Composite Endpoints: Constructing Analysis Data | Pushpa Saranadasa | A composite endpoint in a Randomized Clinical Trial consists of multiple single endpoints that are combined in order to evaluate an investigational drug with a higher number of events than would otherwise be expected during the trial. As an example of a class of applications for composite endpoint studies are Major Adverse Cardiac event (MACE) outcome studies. As a specific example, a primary endpoint might be death due to myocardial infarction. Modern treatment and intervention in a clinical setting might make this a very rare event. Other events like fatal stroke or cardiovascular hospital admission might also be rare events of interest and of comparable interest. Combining the individual events as a composite endpoint makes important events of interest less rare in the study | adam dataset, primary endpoint, data science, cdisc | source | 7 |
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