1/25/2017 0 Comments Sas Randomization ProgramPaper Presentations. Paper presentations are the heart of a SAS users group meeting. Last updated 2. 8- Apr- 2. Sections. Click on a section title to view abstracts for that section, or scroll down to view them all. Applications Development. Beyond the Basics. Paper presentations are the heart of a SAS users group meeting. PharmaSUG 2016 will feature over 200 paper presentations, posters, and hands-on workshops. The Health and Recovery Peer (HARP) Program: A Peer-Led Intervention to Improve Medical Self-Management for Persons with Serious Mental Illness. Research from JAMA — Weight Loss With Self-help Compared With a Structured Commercial Program — A Randomized Trial — ContextAlthough commercial weight loss. Paper 99-28 - 1 - The DOW (not that DOW!!!) and the LOCF in Clinical Trials Venky Chakravarthy, Ann Arbor, MI ABSTRACT Pharmaceutical companies conduct longitudinal. ![]() Introduction to Program Evaluation for Public Health Programs: A Self-Study Guide. Paper PO06 Randomization in Clinical Trial Studies David Shen, WCI, Inc. Zaizai Lu, AstraZeneca Pharmaceuticals ABSTRACT Randomization is of central importance in. ![]() Career Planning. Data Standards. Paper No. Author(s)Paper Title (click for abstract)DS0. Ruth Marisol Rivera Barragan. Tips and tricks when developing Trial Design Model Specifications that provide Reductions in Creation time. DS0. 3Frank Diiorio& Jeffrey Abolafia. Results- Level Metadata: What, How, and Why. DS0. 4Alyssa Wittle& Christine Mcnichol& Tony Cardozo. Moving up! 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. This may cause narratives to become a rate- limiting factor in completing the CSR. Further, while changes to the study database are easily reflected in statistical tables by re- running programs, changes to narratives occur manually which may result in incorrect reporting. Finally, patients in therapeutic areas for severe conditions likely experience numerous SAEs; the volume of events to summarize can consume a great deal of resources. In this talk, we describe how AE narratives can be generated directly from study data sets using JMP Clinical. Further, we discuss the importance of standards and templates for narrative text, as well as utilizing CDISC data submission standards for narrative content. Without a good macro system to process, it can be messy and time- consuming. This paper addresses the benefits and know- how of building a CDISC Terminology processing macro system. 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 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. The same method and code framework could be applied to other kinds of dictionaries in clinical trial processing such as the building of a central labname dictionary or lab test unit conversion dictionary. The term big data is closely associated with unstructured data. A No. SQL(Not only SQL or Non- relational SQL) database is exactly the type of database that can handle the unstructured data. For example, No. SQL database can store unstructured data such as XML(Extensible Markup Language), JSON(Java. Script Object Notation) or RDF(Resource Description Framework) files. If an enterprise is able to bring unstructured data from No. SQL databases to SAS environment and apply SAS analytic ability to those data, it will bring tremendous values to the enterprise, especially on a big data solution. The paper will show how unstructured data are stored in No. SQL databases, transferred to SAS environments, and analyzed in SAS. First, the paper will introduce No. SQL database. For example, No. SQL databases can store unstructured data such as XML, JSON or RDF files. Secondly, the paper will show how SAS system connects to No. SQL databases using REST(Representational State Transfer) API(Application Programming Interface). For example, SAS programmers can use PROC HTTP option to extract XML or JSON files through REST API of No. SQL database from No. SQL database to SAS environment. Finally, the paper will show how SAS programmers can convert XML and JSON files to SAS datasets for further analysis. For example, SAS programmers can create XMLMap files using XMLV2 LIBNAME engine and convert the extracted XML files to SAS datasets. Next Stop is the Destination Excel. William E Benjamin Jr, Owl Computer Consultancy LLC. Monday, 4: 0. 0 PM - 4: 2. PM, Location: Centennial GOver the last few years both Microsoft Excel file formats and the SAS. 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 2. Excel and later. It was first shipped as an experimental version with the first maintenance release of SAS. This ODS destination has many features similar to the EXCELXP tagsets. AD0. 7 : Enhanced Open. CDISC Validator Report for Quick Quality review! Ajay Gupta, PPD Inc. Tuesday, 1: 1. 5 PM - 1: 3. PM, Location: Centennial GOpen. CDISC validator provides great compliance checks against CDISC outputs like SDTM, ADa. M, SEND and Define. This validator will provide a report in excel 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. Also, if there is data or code list issues 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 Open. CDISC validator report. This enhanced report will have SDTM data (only row with issue) and SDTM terminology added as separate worksheets in the original report. Later, hyperlinks between each message in the report and the related SDTM data or SDTM code list worksheets are added using the excel formulas and Visual Basic for Application (VBA). These hyperlinks will further provide point to click options to check the data and code list related issues immediately in the enhanced report which will save significant time with minimal coding. This enhanced report can be further developed to cover ADa. M, SEND database. AD0. 8 : The Power of Perl Regular Expressions: Processing Dataset- XML documents back to SAS Data Sets. Joseph Hinson, in. Ventiv Health. Tuesday, 1: 4. PM - 2: 0. 5 PM, Location: Centennial GThe seemingly intimidating syntax notwithstanding, Perl Regular Expressions (PRE) are so powerful they can overcome and parse the most complex non- uniform textual data. 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: . 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 . Thus it comes as no surprise that the XML data format, with its disparate forms of tags and elements, can easily be processed using PRE techniques. With PRE, all the extraneous non- SDTM text can be . SAS 5 XPT file format is scheduled to be replaced, according to FDA, by the new CDISC Dataset- XML standard, and various techniques are currently being developed for processing the XML data structure. The present paper will show how to easily convert an SDTM data in XML format back to a regular SAS data set, using the PRE technique. The P2. 1 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 P2. This cumbersome process creates the need for a detailed listing report to expedite the review of P2. The resulting report includes a series of customizable listings organized by domain and issuer ID (FDA Publisher ID). The final output saves time, helps programmers understand issues more thoroughly, and provides a tangible product that can be delivered to other team members for further investigation, including data management for querying. This paper will focus on how to create a detailed report of data issues requiring further inquiry and is intended for use by the data management team. 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. Effectively designed dashboards often extract real- time data from multiple sources for the purpose of highlighting important information, numbers, tables, statistics, metrics, and other content on a single screen. This presentation introduces basic rules for . This procedure is deeply associated with the mechanism by which SAS controls output in the Output Delivery System (ODS).
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