|TITLE||PRESENTER ||ABSTRACT ||SECTION |
|Compartment Models in PROC NLMIXED||Fang Chen, Director of Advanced Statistical Methods at SAS. Raghavendra Kurada, Senior Research Statistician Developer at SAS. ||PK models are nonlinear models that are widely used in the biopharmaceutical industry to predict pharmacokinetic changes in a body system. In SAS/STAT® 14.3, the NLMIXED procedure provides enhanced PK modeling capability through the new CMPTMODEL statement, which enables you to fit a large class of PK models, including one-, two-, and three-compartment models for intravascular (bolus and infusion) and extravascular (oral) types of drug administration. This talk introduces the CMPTMODEL statement, provides examples, and discusses prediction, visualization, and data input. ||Statistics & Pharmacokinetics|
|The diagnosis and handling of Missing data in clinical trials||Ying Yao, statistical programmer from BI||In this paper, we would like to display the pattern of missing data that frequently happen in clinical trials and clarify the reasons why they happen, and discuss several ways of prevention for the missing data cases. Then we will review the theory of missing data and methodology regarding how to identify and then how to handle the missing data with examples in SAS. In the last section, we will go through the technics and the pre-requisites for applying and modeling to make sure the methods are used properly.||Statistics & Pharmacokinetics|
|ADaM Structures for Integration: A Preview||Wayne Zhong, member of the ADaM Integration, ADaM compliance sub-teams.||The existing ADaM classes (ADSL, BDS, and OCCDS) already support some simple cases of integration analysis. However, there has been a need for an integration standard that supports the more complex cases. To address this need, the ADaM Integration sub-team is developing the upcoming ADaM Integration standards document. This paper introduces the new IADSL, IBDS, and IOCCDS classes found in this document. This paper also discusses the analysis needs that necessitated the creation of the new classes, and provides examples in the form of usage scenarios, data, and metadata. ||Data Standards/CDISC and Regulatory Submission|
|Advanced Figures using SAS Graph Template Language (GTL)|| Wei (Tony) Zhang, Asso. Dir. Programming Lead in Pfizer||This paper demonstrates how SAS Graph Template Language can be effectively and easily used to create plots like swimmer plot, waterfall plot, spider plot, forest plots, survival plot, and other graphs using Graph Template Language and the ODS Graphics procedure; new functions in SAS 9.4 GTL; ODS template modification, and other tips to use in GTL for complex figures.||Data Visualization and Graphics|
|Create the TA,TE models that conform to SDTM criteria by the web platform||Jianfeng Ye||This paper proposes a new solution which define the rules in the network platform, which will easily build a standardized data model through the browser. This method not only facilitates the trial designer to better understand the study design, but also enhances the quality of data delivery to some extent.||Application Development & Technical Techniques|
|Data De-identification Automation in SAS||Huan Lu, Statistical Programmer at Sanofi Aventis.||Data in clinical trials submitted to Food and Drug Administration (FDA), European Medicines Agency (EMA) or national competent authorities of EU Member States shall be shared in order to fulfill the commitment by considering how de-identification and anonymization techniques can be applied to individual patient data (IPD). Given appropriate de-identification specification and plan, automation in data de-identification process becomes very much needed, DEID as data de-identification automation SAS macro package was created under such a background, which would be an ideal tool to de-identify data automatically.||Application Development & Technical Techniques|
|SAS Utility to Combine RTF Outputs and Create a Multi-Level Bookmark Hierarchy and a Hyperlinked TOC||Lugang Xie (Larry), Statistical Programmer at Janssen China R&D. Jundong Ma, Associate Manager in department of Statistical Programming at Janssen China R&D. Jie Wang, Senior Statistical Programmer Analyst at Janssen China R&D||RTF is a popular format that most sponsors adopt to create tables, listings and figures in the pharmaceutical industry. However, there is an unmet need to concatenate the outputs into a presentable single RTF file independent of the computing system. This paper presents a simple SAS approach to concatenate any SAS generated RTF files, including both tables/listings and figures, and create a multi-level bookmark hierarchy and a hyperlinked TOC, without having to modify those the macros/programs that create the individual outputs. It can be executed on any platforms with SAS installed. In addition, a simple approach to convert the combined RTF file into a PDF or DOCX format is also introduced in this paper, and it can be used for preparation for OSI BIMO listings submission.||Application Development & Technical Techniques|
|Effective Graphical Representation of Tumor Data||Sanjay Matange is an expert in the field of data visualization using SAS graphics software including the SG procedures and GTL.||The “Duration of Treatment” and “Tumor Response” data for subjects in a study has traditionally been visualized in separate graphs where the subjects may be sorted by different criteria. In such cases, the Clinician must work harder to associate the subject across different graphs. Displaying the data together, sorted by common categories such as tumor response of subjects makes it easier for the Clinician to understand this information.
3D waterfall graphs have been proposed for such visuals. This paper will show you how to build a 3D Waterfall Graph with tumor response and duration of treatment in one graph using SAS®. We will also discuss the features of this approach. Finally, we will present effective 2D alternative displays to visualize all the data in one combined graph thus making it easier to decode the information.||Data Visualization and Graphics|
|Create your SAS programs automatically using python||Linjie Jia, Statistical Programmer at Sanofi Aventis.||As a statistical programmer, no matter you are a leading programmer or a validated programmer, you need to create many SAS programs to generate tables, listings and figures. Especially in CSR, there would be hundreds of SAS programs need to be created. However, most of us choose to copy and paste it, and then begin to programming. In this paper, it will show you a new method to create SAS programs automatically in your folder base on the spreadsheet (such as QC tracking), and it has 3 advantages:
1. All the SAS programs will be created automatically at once.
2. The SAS programs will be automatically named as you want.
3. You don’t have to modify your header one by one.||Coder's Corner
|Leading Gen Z Workers by Millennial Managers||Margaret Hung is the founder of MLW Consulting LLC specializing in managing clinical data and providing business services||Introduction: Generation Z (Gen Z) represents the greatest generational shift the workplace has ever seen. In 2019, it is estimated that there are more than 30 millions of Gen Z in the workforce. This is the new emerging workforce bringing a new set of behaviors, expectations, and preferences into the workplace. As such, Gen Z has presented profound challenges to leaders and managers in every sector of the workforce. Management needs to understand where they are coming from and key strategies for bringing out the best in this new emerging young workforce.
Many Millennials now have become managers themselves. Studies show that many millennial managers are not equipped to manage this influx of Gen Z workforce. It is now more important than ever to consider not what Gen Zers want from their work environment but how companies can best equip millennial managers to create a unique work culture for Gen Z workforce. The focus of this paper is on strategies for millennial managers to bring out the best in Gen Z in the workplace.||Management and Career Development
|Case Study: Using Base SAS to Automate Quality Checks of Excel Workbooks that have Multiple Worksheets||Andrew T. Kuligowski has been a SAS user for … well, since Version 79.5. Currently an independent consultant in Florida (USA), Lisa Mendez, Senior Consultant at IQVIA.||This case study provides a real world example of how Base SAS was used to read in over 185 Excel workbooks to check the structure of over 10,000 worksheets – and to repeat the process quarterly. It will illustrate how components such as the LIBNAME XLSX Engine, PROC SQL (to create macro variables), SAS Dictionary Tables, and SAS Macros were used together to create exception reports exported to MS Excel workbooks.
The structure of the worksheets, such as worksheet names and variable names, were checked against pre-loaded templates. Values within the worksheets were also checked for missing and invalid data, percent differences of numeric data, and ensuring specific observations were included in specific worksheets. This case study describes the process from its inception to the ongoing enhancements and modifications. Follow along and see how each challenge of the process was undertaken and how other SAS User Group conference proceeding papers contributed to this quality check process.||Data Management & Validation|