PharmaSUG CN 2016 Papers - Index

56 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A Grid Computing Tool to Batch Run a List of SAS? Programs	Huashan Huo, Zhongyu Li, Lu Zhang	In clinical research, it is very common that a large number of SAS programs are to be repeatedly batch run due to program modifications or new data updates. In the past few years, several papers authored by pharmaceutical industry programmers (Gilbert Chen 2002; Shu 2006; Prescod Cawley 2010; Wong Sun 2010; Conover 2011; Andrew E. Hansen 2013; Huashan Huo, Fanyu Li 2015) were published in this area to describe methods and tools for automating this process. The purpose of this paper is to introduce a tool for grid computing a large number of SAS programs concurrently. The SAS computing tasks are distributed among multiple computers on a network, all under the tool's self-management. Not only workloads are distributed across a grid of computers, but also overall execution time is geometrically reduced. This paper also describes how the tool supports Multi-User Workload Balancing and Parallel Workload Balancing without `SAS/SHARE` and SAS Grid Manager.			7
2	A Macro to Import Data from CSV File to SAS	Liang Guo, Pei Zhang	`CSV` is a universal and relatively simple file format, and widely used in data transfer. Generally, `CSV` file can be imported into SAS by using `PROC IMPORT` very easily. However, when `CSV` file is converted into SAS by using `PROC IMPORT`, part of the data could be disordered or the program could be aborted due to error when line break exists in data. This paper discusses a macro which can be used to import `CSV` data into SAS, and it can overcome the line break problem yet keep most capacity of `PROC IMPORT`			10
3	A Web Interface for Efficient Clinical Trial Program Development	Hao Jin, Ping Ni	In order to improve the efficiency of code programming in clinical trial, an interface in web page form is developed to contribute several important functions to reduce unnecessary time. There are two parts are included in the interface. One is hyperlink to required documents, such as protocol, sap, TLG shells and programming tracking sheet. The program can automatically create the link by identifying the key words to locate and get these documents' path. It also can be recreated if related documents are updated. The other is a Shortcut to utility tools, which includes batch -run, compare -result, check difference and etc. These functions could make programming process more efficient. The Web interface could be generated by using SAS with a few parameters. It's very easy to implement at the start of a project .Functions of this interface could also be extended			7
4	ADaM Compliance VS Easy-Reviewed in Design and Construct Efficacy Analysis Datasets	Huadan Li	CDISC ADaM provides a fundamental principle that enables statistical analysis of the data, while at the same time allowing the data reviewers to have a clear understanding of the data's lineage from collection to analysis to results. When develop the analysis datasets, compliant with the ADaM defined structure is first consideration. However, for the efficacy analysis data, if don't think about how to make the reviewer to understand the data's lineage from collection to results, that is, the analysis dataset is not easy-reviewed and must make more explanations in ADRG. This paper will demonstrate how to design and construct CDISC-ADaM compliance and easy-reviewed ADaM dataset. Key word: CDISC ADaM Compliance, Easy-reviewed, Efficacy analysis dataset	cdisc		8
5	ADaM Tips for Exposure Summary	Kriss Harris	Have you ever created the Analysis Dataset for the Exposure Summary also known as ADEXSUMM? Do you understand the different Dose Intensity calculations such as Dose Intensity (mg/m^2 per day), Dose Intensity (mg/kg per day), and Dose Intensity (mg per day)? Do you need to calculate parameters such as Relative Dose Intensity (%), Cumulative Dose, Number of Dose Delays, Number of Dose Reductions and Number of Omitted Doses? This paper will show you can create the ADEXSUMM dataset, which includes the parameters that are frequently required and reported. The ADEXSUMM dataset will be created using the source SDTM domains, EX and SUPPEX. After producing your ADEXSUMM dataset you are going to want to produce TFLs based on ADEXSUMM, such as TFLs which include in the output the Number of Cycles Received, Duration of Therapy, Total Prescribed Amount, Relative Dose Intensity (%), etc. This paper will show you SAS tips that you can use to produce your exposure summary outputs!	cdisc		6
6	An Automatic QC Status Tracking and Management System	Yonghong Hao, Jun Wu, Yulin Li, Jingfang Wu	Tracking the developing, QC status and comments for hundreds of programs in multiple projects is a challenge work, with the programs and their outputs are frequently changed by multiple programmers every day during the project life cycle; Record and review the status manually are boring, labor intensive, and difficult to assure the accuracy and timeliness; how to improve the project progress communication efficiency is also a big topic. Based on these needs, we developed a tracking system using VBA and PHP. It stores the tracking data in an independent database, and can be reviewed and modified through Microsoft `EXCEL` and html. The tracking database can be also used for other analysis or summary for management purpose. Use this system, we can achieve the following goals: (1) Automatically track status and latest modified time of the programs, corresponding outputs and comparison status (pass/fail) timely without any manually inputting; (2) Detect time logic error between among programs, outputs and QC status timely; (3) Manage the centralized pool of TFL titles, footnotes, populations and input data sets. Additionally, any changes of them can be saved and reminded for the rerun automatically; (4) Through a hyperlink icon at the tool bar area, a web-based issues log tracking tool can be accessed for tracking the comments and its status, programmers can add, modify and close the comments for each programs, or review the summary of all comments for the project; (5) The project manage team can easily find out the up-to-date progress of each project via the web-based project progress interface with visual progress bar and see the details of the progress for each program			8
7	An Effective Way to Produce Laboratory Shift Table	Amanda Yi	'Shift table' is often required for most clinical trials, especially laboratory data analysis. The purpose of shift table is to deduce how the results are varying from the baseline to post-baseline visits in the study. This paper will start from an example and explain the anatomy, then a step-wise explanation of how to create a shift table in a wink			10
8	An efficient way to create split Axis in Graph	Ryan Feng	Presentation			20
9	An Introduction to Creating Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS	Vincent DelGobbo	This paper explains how to use Base SAS 9 software to create multi-sheet `EXCEL` workbooks (for `EXCEL` versions 2002 and later). You learn step-by-step techniques for quickly and easily creating attractive multi-sheet `EXCEL` workbooks that contain your SAS output using the ExcelXP `ODS` tagset and `ODS` styles. The techniques that are presented in this paper can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real time using SAS server technology is discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented	excel, ods excel, ods tagsets.excelxp		20
10	Analysis of Patient Reported Outcome (PRO) Endpoints in Clinical Trial	Tongda Che, Peng Wan	Presentation			20
11	Animate Your Safety Data	Kriss Harris	When reporting your safety data do you ever feel sorry for the person who has to read all of the laboratory listings and/or summaries? Or do you ever wonder that there might be a better way to visualize the safety data. Let's help make the reviewer's life easier and also understand the safety data better with the use of animation. This paper shows how you can use animation in SAS 9.4 to report your safety data. Such as visualizing a patient's laboratory results, vital sign results and electrocardiogram results and seeing how those safety results change over time. In addition you will learn how to make animate adverse events over time, and how to show the relationships between adverse events and laboratory results using animation. Animating your data will bring your data to life and help to better more lives!			10
12	Are Your Random Numbers Created by SAS? Good Enough?	Yanze Zhang	Random numbers are crucial to many statistical analyses and simulations. SAS generates pseudorandom numbers based on deterministic algorithms. There are two ways to create random number streams in SAS, Random-number functions and CALL routines. The stream of random numbers is controlled by a seed. If Random-number functions are used in a `DATA STEP` to generate the stream, the only seed matters is the first seed in the `DATA STEP`. Examples in this paper demonstrate this characteristic of the Random-number functions. This can be easily overlooked by programmers or statisticians, which may lead to the discrepancies in the results. This is actually the advantage of pseudorandom numbers since the stream of random numbers can be reproduced, and thus the results based on those random numbers can be compared and validated. On the other hand, CALL routines can generate multiple streams of random numbers in a `DATA STEP`, which may bring some potential risk in stream overlapping when the stream is long. Examples in this paper also demonstrate the loss of independence of streams in some special cases. The Mersenne-Twister pseudo-random number generator (PRNG) used by SAS is also used by many other software, such as C++, R, MATLAB, Stata, Python. This paper also explores the random number generation in R, and the random generation tests in R package "randtoolbox" are carried out to test the quality of the random numbers generated in SAS	random number generation, ranuni, seed		8
13	Automation of SDTM Programming in Oncology Disease Response Domain	Yiwen Wang, Yu Cheng, Ju Chen	Study Data Tabulation Model (SDTM) is an evolving global standard which is widely used for regulatory submissions. The automation of SDTM programming is essential to maximize the programming efficiency and improve the data quality. This paper intends to present some core programming logics to automatically create the Disease Response (RS) domain which is stated in CDISC SDTM IG v3.2 to represent the collected tumor data either quantitative measured or qualitative assessed in most of the clinical trials in oncology therapeutic area. The automation is realized by using SAS macro facilities which include 1) the environment setting; 2) meta-data automation; 3) functionality oriented macros; 4) SUPPQUAL dataset automation; 5) structured end-to-end automation; 6) log check. The SAS macros presented in this paper could also be applied to the automation of other SDTM oncology domains (i.e. TU, TR) or be extended to the domains in the SDTM Findings Observation Class. Automation of SDTM Programming in Oncology Disease Response Domain Yiwen Wang, Yu Cheng, Ju Chen Eli Lilly and Company, China 11/29/2016 CRF data Non-CRF data e.g. PK, Central Lab, IWRS Other source data Dictionary Raw	sdtm programming, macro facility, cdisc		10
14	Big Insights in Small Data with RStudio Shiny	Mina Chen	Accelerating analysis and faster data interpretation helps to stay competitive in the drug development field. It requires statistical programmers to explore and adapt new, different and suitable approaches to delivery of data analysis according to the stakeholder's requirement. A key approach to data analytics is our increasing use of visualization tools in conjunction with analyst skills to support faster decision-making. As customer demands evolving, statistical programmers are required to proactively communicate and interact with key stakeholders and clearly identify their needs. Our practice is to utilize and explore a newly developed data analysis approach 'RStudio Shiny' to support ad-hoc and exploratory analysis based on specific requirement. This paper outlines how statistical programmers support effective data review and fast decision-making by using RStudio Shiny application			5
15	Blind Data Review in Clinical Trials	Jiashu Li, Dong Guo	Presentation	data cleaning		4
16	Building Your Own CDISC Application with SAS Clinical Standards Toolkit	Jing Gao	The SAS Clinical Standards Toolkit (CST) is a framework that supports clinical research activities and focuses on the CDISC standards. The CST provides validation checks from WedSDM, OpenCDISC and SAS on the CDISC data, especially SDTM and ADaM, as well as the generation of XML-Based Standards files, and validation of XML files against an XML schema. A nice plus is that the CST is a separately orderable component that is available at no additional charge to currently licensed SAS customers. However, the CST is a set of SAS macros, metadata and configuration files, interacting with it requires a technical background of Base SAS and the SAS macro language. So can we make the using of the CST more intuitive and easier? Of course, one solution is to add an easy to use graphical user interface (GUI) for the CST and invoke the CST macros that are provided as open source and are accessible to the user. Compared with processing clinical data, it is simple to create a GUI. Many programming languages, such as Java and Python, are well suited for the GUI development. As we know, Python is an easy to learn, use and powerful programming language, thus in this paper, I will introduce how to use Python to build a CDISC GUI application based on the CST	cdisc	presentation	13
17	Clip Extreme Values for a More Readable Box Plot	Mary Rose Sibayan, Thea Arianna Valerio	The `BOXPLOT` procedure creates box-and-whiskers plots of measurement that displays the mean, quartiles, minimum, maximum and outlier values for one or several groups. When handling interim or "dirty" data, there can be numerous outliers that may affect the readability of the plots. This issue can result into compressed plots and wide y- axis ranges, from which no clear conclusions can be drawn unless summary statistic values are presented. The CLIPFACTOR option has the ability of clipping extreme values to produce readable and useful plots without affecting the summary statistic values. This paper aims to explore the CLIPFACTOR option of `PROC BOXPLOT` and present a macro that will compute for the optimal factor value based on the data. In addition, the macro also produces a data set which contains the extreme observations that were clipped from the plots	box plot, outlier, proc boxplot		7
18	Create a Graph Template on the Fly	Kathy Chen	While `PROC TEMPLATE` Graphics has enabled us to create customized graphs, the tendency is to have many static templates with minor differences. Instead, it makes more sense to create a template to work with your data in real time. Creating a Graphic Template on the fly based on actual data can greatly improve programming efficiency. This paper will show you how to dynamically create a graphic template			8
19	Creating Reader-friendly Clinical Summary Statistics Tables Using PROC REPORT and ODS RTF	Shiran Chen	In clinical trial reporting, a standard summary table displays the continuous and/or categorical variables of interest and the statistics in rows, with the treatments and the associated p-values for treatment comparison in columns. Such a summary table with statistics comparison is always generated before conducting any inferential analysis among treatments. Therefore, it is beneficial to design the table so it enables statisticians and researchers to read the document easily and process information quickly, especially when there is a need to summarize a large number of variables. One can enhance the readability of the summary statistics table by suppressing certain table gridlines to "group" results of the same variable together, formatting row headers as bold text for emphasis, creating leading space for sub row headers, and adding superscripts to indicated footnotes, etc. This paper applies these methods to create a reader-friendly version of the clinical summary table in Microsoft Word using `ODS RTF` and `PROC REPORT` techniques, with a focus on the usage of STYLES in the REPORT and COMPUTE statements			6
20	Custom useful SAS functions using the PROC FCMP procedure	Qixia Shi, Guanyu Su	As a SAS programmer, we usually do some repetitive programming work. Usually, we use `DATA STEP` or macros to ease writing repetitive program. However, `DATA STEP` are difficult to be reused in other programs, it will be useful and time-saving if small program units can be reused. Macros are easier to be reused but they are not independent from the main program, they involve non-`DATA STEP` syntax, and reuse macros can result in a large number of intermediary data sets being generated .It would be great if we could circumvent the issue of macros generated, as all we really want is one result. Now, multiple subroutines and functions can be declared by using `PROC FCMP` procedure in SAS 9.2 or higher version. Thanks to the `PROC FCMP` procedure and RUN_MACRO function, now we can custom independent and reusable subroutines, this enables programmers to read, write, and maintain complex code more easily. This paper will present some useful functions for handing calculations, standardizing and simplifying code created by using `PROC FCMP` procedure, using these functions we can use only one statement to achieve what we want, that can reduce much repetitive work and save time	proc fcmp, proc step, options statement, macro variable		10
21	Define.xml Content Validation - CRF Page Check	Shenglin Zhang	The FDA document stated that "sponsors should make certain that every data variable's codelist, origin, and derivation is clearly and easily accessible from the define file". There are some validation tools that can be used to validate the define.xml, such as SAS Clinical Standard Toolkit and Pinnacle 21 Community. However, these tools do not validate the contents of the define file, it is important to supplement it with other validation checks to ensure the accuracy of your define.xml. To do these additional validations manually can be cumbersome and time consuming. This paper presents a SAS macro ChkDefCrfPage, which extracts define.xml contents, annotations and page information from blankcrf.pdf into SAS datasets using XMLMap and then does the CRF page number consistency check between define.xml and annotated CRF	cdisc	presentation	7
22	Development Approach on the Sponsor-Defined Extensions of the Model	Gen Pan	Presentation			15
23	ESTIMATE/CONTRAST Statement too Complex? Tell the Effect Level and Let SAS Do It for You	Aide Zhou, Eric Liao	Write ESTIMATE/CONTRAST statement will be challenging when the model is complex or there are many levels for the effect, especially for the freshman. We develop a SAS macro to use 2-step approach, the 1st `PROC MIXED` step to get the coefficient data set, and then use the coefficient data set to automatically built the ESTIMATE/CONTRAST statement, what you need to do is just tell the macro which level of the effect (the actual data value in your data set) that you want to estimate/compare, then this macro will select/create the correct column (coefficient) used for the level of this effect and create the ESTIMATE/CONTRAST statement for you, then use the statement in the 2ND `PROC MIXED` step to do the analysis. This macro is easy to use and can minimize mistakes when we do the analysis			22
24	From Data Collection to Regulatory Submission - a Journey	Todd Case	An NDA/BLA is the end result of an enormous effort to submit and present summaries of clinical trial results and data to the FDA (and almost certainly other geographies will soon be requiring actual data). But just how big of an effort is this? From First Patient/First Visit until Last Patient/Last Visit hundreds of millions of data points are collected, derived, summarized and written up in a CSR, etc. For example, a recent analysis for a 'routine' drug program; for a single study, had 48,970,722 data points that were either collected or created. It doesn't take a great imagination to calculate how much data will be created if multiple studies are included in a submission. This presentation will focus on flow of clinical trial data throughout its journey from visit/collection to CSR to NDA/BLA, provide useful metrics to show how much time is saved by using data standards and provide some recommendations about how to further improve efficient data flow			4
25	General AUC calculated based on the trapezoidal rule	Jianfeng Ye	Generally, the trapezoidal is used to calculate the area under PK curve. Because the PK test data value is actual data, then all observation values should be positive. In practical application, we need to calculate the AUC using the derived data which include the positive values and negative values. For example, the change from baseline value is derived from the baseline value and the observation value, which have different sign. Therefore, this kind of data will not be directly used the trapezoidal rule to calculate the AUC. In this paper, we introduce a method that the adjacent original observations with different sign generate new dummy observations by the geometric triangle theory, to calculate the AUC use the original observations and the dummy observations by the trapezoidal rule. Keywords			7
26	Hands-on Example of Matrix Operation in SAS with Different Approaches	Huan Cheng	It's not always that lucky the modeling used in the study is well developed by certain SAS procedure. Sometimes, we need to transform mathematical representation into executable SAS code. Most of the technical statistical literature prefers to use matrix algebra. Matrix algebra is also a simple and efficient way to do the calculation. When talking about the matrix operation in SAS, `SAS/IML` language is the first to come into mind. Besides `SAS/IML`, there are other ways to achieve the matrix operation. In the paper, first, it will give a brief overview of both `PROC IML` and `PROC FCMP` approach. Simple syntax and tips to use will be presented. Next, it will use computing the beta estimate of simple linear regression as example to illustrate how to use both `IML` and `PROC FCMP` to perform the operation. At last, it will have a little discussion about the two approaches to help users choose the proper method	proc iml, proc fcmp		7
27	Hash Object Working Efficiently with Large SAS Datasets	Yazhen Wang, Hao Xu	Presentation	hash table, cpu time, laboratory data, proc step, by group processing, dataset, where statement		20
28	How to customize your business process more effectively by BPMN with SAS	Yeqian Gu	As we all know, new drug development process in pharmaceutical industry is perennial, complex and disjointed. Within every organization there are common business processes designed to meet objectives. However, the process are cross-functional, human interaction increase complexity. The need of change may come from almost anywhere, both inside and outside that may cause a series of cost on management. Therefore, how to effectively customize and optimize your business process throughout the entire life cycle becomes very important. This paper presents BPMN (Business Process Model and Notation) compliant workflow in SAS LSAF (Life Science Analytics Framework), a powerful and flexible feature that can help you design, manage and monitor your processes. It allows customers the ability to manage the creation, modification and deployment of any templates they want. Meanwhile, it is readily understandable by all business users, from the business analysts that create drafts of the process, to the technical developers that will perform the process, and finally, to the business people who will manage and monitor the process. In this paper I will introduce how to customize or optimize your business process by BPMN with SAS LSAF			11
29	How to give SAS ambiguous instructions and still win big?	Hui Liu	Presentation			18
30	How to handle the mapping of the eCRF form with the combined information	Shirley Wu	When we do SDTM mapping, we may feel difficult to decide how to map those information which we don't see frequently. Then we map them to a farfetched domain. However it's not the best way to catch information when they can go to the specific domains separately. I will share my experience of how to handle the combined information form through one example. And we should be aware that understanding how the study collects the data is important for the mapping	cdisc		4
31	Interactive SAS Analysis Service (iSAS) within AIX Unix SAS Server	Stanley Wei	Presentation	macro definition, sample		15
32	Macro Quoting: Which Function Should We Use?	Pengfei Guo	There are several macro quoting functions in SAS and even some experienced programmers can't distinguish them exactly. We usually look up the SAS help document when encountering some log issue during using. This article will summarize the difference and list some fallible situations. At the end, we also provide a step-wise method to help user make easy decision	macro quoting function, macro variable, sample, macro definition, macro facility		5
33	Make the Most Out of Your Data Set Specification	Thea Arianna Valerio	A data set specification usually contains multiple data sets and variables that will be derived for a certain clinical study. Aside from ensuring the data set derivations are correct and robust, it is also important that the data set metadata is consistent with the specification. Manually verifying the data set and variable attributes against the specification is time consuming and might cause risk in ensuring quality. It is also inefficient typing all the variable names, labels, lengths and formats in the programs most especially when working on a dataset with many variables. Through the macros presented in this paper, these steps can be automated and thus allow more time to review the variable derivations in the data set program. The specification is used as the input file to create macro variables that will hold the information on the attributes of all variables in a data set. This way, any changes in the specifications during the course of the study are automatically captured by the macro. Certain techniques are also presented to make the most out of your data set specification	macro variable, macro parameter, metadata management		8
34	Making Quicker Progress in Career Path for SAS Programmer	Victor Wu	Presentation			21
35	Maximizing the use of SAS Shortcuts	Eduard Joseph Siquioco	Using the point and click method of opening files and datasets may sometimes be tedious especially if you have a lot of files and datasets in the viewer. This paper will explore the different commands available thru SAS DM statements to ease this tedious task; but typing the DM statements may also get tedious as we'll type them to run them. To ease this task, the paper will also explore other shortcuts such as Keyboard abbreviations and assigning function keys to do these DM tasks	macro call, macro function		3
36	MRCT and Project Centric Approach	Jingwei Gao	Presentation			24
37	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		12
38	PPG, a metadata driven solution for patient profile generation	Weina Jia, Eric Li, Vincent Fan, Yihong Chi, Qingan Chang	To facilitate the medical review, data management programmer need to provide the patient profile (PP) to medical reviewer. In most cases, delivering a set of PP for one study takes at least 5 days as data manager need to prepare its specification and then the programmer need to develop the SAS code accordingly. But with PP Generator (PPG), specification preparation and routine programming are not needed any more. It will create PP based on RAVE study design specification (SDS) and source data extracted from clinical database directly, minimize the mistake you might make and greatly reduce repetitive programming, so as to improve the programming efficiency and accuracy and save development time ( taking no more than 3 hours). PPG also has the flexibility by providing the interface for PP population, context, styles and format customization to meet data manager's individual demands while reviewing	sas programmer, macro definition, raw data		5
39	Practical Experience in Implementing CDISC Standards for Global Cardiovascular, Clinical Outcome Assessment and Respiratory Trials	David Ju	Presentation	cdisc		20
40	Proportion difference and confidence interval based on Cochran-Mantel-Haenszel method in stratified multi-center clinical trial	Huiping Zhang	In stratified randomized multi-center clinical trials, we often take account of stratified center factors in the estimation of common treatment effect. In particular, strata -adjusted proportion difference can be obtained by weighted average of stratum-specific proportion differences. Several weighting strategies are available such as Cochran -Mantel-Haenszel (CMH) method, etc. There is no SAS option in freq uency procedure to produce strata-adjusted proportion difference from CMH method. In the text, we will develop a program to calculate 95% asymptotic confidence interval for strata- adjusted proportion difference based on Wald-type statistic	confidence interval, clinical trial, treatment effect		5
41	Proposal for Streamlining the SDRG and ADRG Authoring Process	Stanley Wei	Presentation	adam dataset, data cleaning, cdisc		1
42	SAS Automation and More	Frank Fan	SAS programmers/statistical analysts spend much time on repeating tasks, such as rerunning programs with different parameters and creating similar reports. Using different techniques, we automate the tasks to free the programmers from these tedious, manual burdens. These techniques include the Windows task scheduler, script/batch files, method to find a specific input from a folder, SAS tools like the email file engine, macros to identify error/warning messages in the SAS log file, etc. This paper will demonstrate how we use these tools to benefit the programmers	subgroup analysis, batch processing		7
43	Smart Statistical Graphics - A Comparison Between SAS ? and TIBCO Spotfire ? In Data Visualization	Yi Gu	Known for the best analytic software, SAS has always been challenged by emerging analytic tools. TIBCO Spotfire 6.5, an analytics and business intelligence platform, which enables data visualization in an interactive mode, has been brought into the competition in pharmaceutical industry these years. With its increasing implementation in the field of safety monitoring and dose escalation, TIBCO Spotfire presents its superiority for exploratory analysis. On the other hand, the capability allowed in the efficiency, strong statistical analysis and data processing defends SAS software. This article is to demonstrate the two softwares in terms of data visualization and explore the possible integration between them			5
44	Strategy to Start to Implement CDISC Standards	Victor Wu	Presentation	cdisc		24
45	Survival Analysis Translation from Statistician to Programmer	Amanda Yi	Survival analysis is very common in clinical trials. Programmers are often requested to program tables and figures that are often referred to Kaplan Meier plots simply. There can be a huge gap between the language of a statistician and a programmer. This paper will provide a basic understanding of survival analysis by using example data and code to compare what the programmer sees to what the statistician would describe. The output data from `PROC LIFETEST` will be explained			7
46	The Dream of a Common Vision: (Evolving) Standards	Todd Case	On July 21, 2004 the US Food and Drug Administration (FDA) announced a format, called the Study Data Tabulation Model (SDTM) that sponsors can use to submit data to the agency. Twelve years later the FDA is only now (as of December 17, 2016) enforcing the requirement of standardized electronic data submissions in SDTM format and now, in addition to SDTM, there are multiple sources (and versions) of data Standards which impact data supporting applications to the FDA: the FDA Data Standards Catalog (primary list and source of standards), the SDTM itself (Version 1.4), the SDTM Implementation Guide (SDTMIG - Version 3.2), the Analysis Data Model (ADaM) - Version 2.1, the ADaM Implementation Guide (Version 1.1), the FDA Guidance for Industry (December, 2014), the Study Data Technical Conformance Guide (March, 2016) and the Prescription Drug User Fee Act (PADUFA), Version V for fiscal Years 2013-2017. At times these documents, guidance's and laws are somewhat contradictory and it's up to the Sponsor (when appropriate to engage with the FDA) to determine which 'standard' (of the standards) to adapt, which version(s) to use and when to update versions. This paper will provide an approach to determine the appropriate data standards and versions	cdisc		4
47	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. In this paper, we will present concrete examples to help the readers understand the difference between UTF-8 and wlatin1 encoding and provide practical solutions to address the challenges above	dataset, txt, proc step, excel, subject visits, proc contents, sas enterprise guide, length statement		12
48	The NextGEN of Lumberjacks: Are You Using the Right Tool For the Right Tree in the Forest?	Gen Pan	Presentation			14
49	The Simple Application of SAS DDE in Excel	Danting Guo	Dynamic Data Exchange (DDE) is a method of dynamically exchanging information between Windows applications. The DDE mechanism enable SAS to control `EXCEL` or Word. Basically, what you can do with `EXCEL`/Word using keyboard and mouse can be replicated programmatically in SAS. Although the process of exporting the data from a SAS data set to an `EXCEL` file is relatively simple, but SAS output need to be edited to meet the statistical requirements, someone has to manually create and format an `EXCEL` graph, which can be a time consuming process and the editing process will be also very easy to make mistakes. This paper mainly introduces how to use DDE within SAS to control `EXCEL` and provides some examples, such as how to send data or results generated in SAS to specific cell locations in an `EXCEL` worksheet and so on, which may greatly reduce the amount of work for the following edit process	excel, filename statement, chi-square test, proc freq		6
50	Three Approaches to Estimate Power by SAS	Xiang Liu	To estimate power, there are different ways. This poster will introduce 3 approaches to estimate power by SAS. First of all, also the most common one is to use one SAS procedure - `PROC POWER`. Secondly, given the power estimation formula, translating the formula to SAS code is a good option. However, in situation that lacking support of SAS procedure, and with estimation formula unknown, a solution of simulation is optimum. Examples will be utilized to illustrate the 3 ways. ESTIMATE OF POWER (by `PROC POWER`) First of all, the power estimate of the `PROC POWER` is demonstrated as follows	proc power, proc ttest		1
51	Use Hash to reduce run time	Xiaotian Wu, Yu Zhu	Presentation	hash table, data step, proc sort, view, proc step, proc sql, call missing		17
52	Using SAS to Assemble Output Report Files into One PDF File with Bookmarks	Sam Wang, Kaniz Khalifa	Assembling multiple output report files (such as RTFs or PDFs) into a single PDF file is frequently needed in clinical trial development. The most current practices involve using an adobe product or other third party applications. However these practices are very time consuming for programmers to create or control the bookmarks. Also, default bookmarks by those applications need to use the file names or use certain lines of the titles from the outputs. The use of third party applications will occur additional cost and will require more time to learn and understand the new language syntax. Our approach is to assemble the output report files without using any third party's applications. We only use SAS@ code to assemble all the output files from a specified destination into a single PDF file with bookmarks that are user defined	proc report, proc step, macro variable, ods pdf, by group processing		5
53	Using SAS to Calculate Statistical Properties of Traditional 3+3 Design	Tao Tan	Traditional 3 + 3 design is a popular design thanks to its simplicity for both modeling and execution . Therefore , it is widely used in Phase I dosing escalation studies to help identifying the maximum tolerated dose			7
54	Utilizing SAS? and Groovy to combine multiple RTF/PDF reports to one bookmarked PDF document	Xin Weiquan, Wang John	As part of clinical trial reporting, large numbers of RTF/PDF outputs are created and at the completion of a major milestone in a study, we are often required by medical writers, clients, and regulatory agencies to combine all reports in a user-friendly file format document for easy delivery and review process. One solution is to combine them to a bookmarked PDF document using Adobe Acrobat Software. However, manually generating a PDF document from multiple SAS output files is a time consuming task. This paper presents an alternative efficacy method that use Java script for combining the multiple RTF/PDF documents into one bookmarked PDF document. A SAS built-in mechanism dynamic language `PROC GROOVY` which runs on the Java Virtual Machine (JVM) is used to quickly combine the multiple documents into one cumulative file. We have replaced the manual process by automating the ordering of multiple SAS outputs, and the creation of bookmarks within the PDF document. If needed, this script can be used independently	proc groovy, proc step, sas output		9
55	What's the BIG Deal with Missing Data?	Peter W. Eberhardt, Mina Chen	In clinical trials, a major problem in the data analysis is missing values caused by unrecorded results of measurements at some planned visits, or patients dropping out of the study before completion. Missing values can have a surprising impact on the way that data are dealt with, which may result in biased treatment comparisons as well as impact the overall statistical results of the study. Generally, missing values can be represented by SAS in a number of ways, there are various functions, options and techniques associated with missing values, and procedures will have specific ways of handling them. However, not all SAS programmers are aware of the System options, `DATA STEP` functions, and `DATA STEP` routines that specifically deal with missing values. This paper will include the basics of how to detect missing values, and how to effectively make use of various functions and tools within SAS to utilize missing values			9
56	Why not Picture Format?	Gaoyang Li	The importance and benefits of formats procedure are well-known. Nearly every SAS programmers know how to use SAS built-in FORMATS/INFORMAT or create costumed FORMATS/INFORMAT. However, it seems that picture format was excluded from their toolkit. Why not Picture format? The rationale of PICTURE statement is different from VALUE/INVALUE, at the same time it bring us some wonderful method to display or output data. In this paper, the logic of picture format is parsed step by step to make sure you are confident to use it without worrying error. A few examples with different options are followed to demonstrate its efficiency	sas programmer, proc format, missing value, numeric variable		16

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