PharmaSUG CN 2014 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	A simple way to access the data in EXCEL through SAS v9/ ACCESS libname and Excel engine	Lianbo Zhang	Aiming to summarize `LIBNAME` access and `EXCEL` engines which is useful to read `EXCEL` file into SAS dataset but not familiar to many people who prefer to"`PROC IMPORT`" or"infile input". This paper will discuss the principle and useful techniques about data transform between SAS and `EXCEL`, including named range, spreadsheet, `LIBNAME`, `EXCEL` engines. And also solving some problems we may meet. We use SAS 9.3 64-bit and `EXCEL` 2013 64-bit in win7 x64. `SAS/ACCESS` software for PC files must be available when we use the `EXCEL` engine	excel, libname statement, name range list, sas library	14
2	An implementation of XSL-FO techniques to convert define.pdf from define.xml	Kyle Chang	It's getting more common and important for the electronic submissions of clinical data to include define.xml, which specifies the CDISC standard for providing case report tabulation (CRT) data definitions in XML format. Although define.xml has proven to be a useful mechanism can easily help regulatory reviewers to navigate transmission of CRT metadata, it is not able to provide original features (e.g., hyperlinks, bookmarks…etc.) while they're printed out. One solution is to generate a printable define.pdf document with the same content as the define.xml. The printable define.pdf file accommodates bookmarks and hyperlinks functionality for online review, and it can also be printed out for hardcopy review. Providing define.pdf documents is not only to have an easy way to review clinical data submission package, but also to fulfill submission requirements for sponsor and regulatory authority. This paper provides an approach that using Extensible Stylesheet Language Transformations (XSLT) template converting "define.xml" to formatting objects for easy transformation to "define.pdf"	ods pdf, crf, cdisc	6
3	Annotate your SGPLOT Graphs	Sanjay N. Matange	The SG procedures provide you multiple plot statements to create many different kind of graphs. These plot statements can be used together in creative ways to build your graph. However, even with this ability to customize, there are times when you need more than what you can get using just the plot statements. You need a way to add custom information anywhere on the graph. With SAS 9.3, the SG procedures support th e ability to annotate the graph using data set based information. This annotation functionality is designed in a way similar to the annotate facility available with the `SAS/GRAPH` procedures. There are a few differences and enhancements. If you already k now annotation from `SAS/GRAPH`, or if you are new to it, this paper will show you how to add custom annotations to your graphs	kaplan-meier plot, sg annotation, proc sgplot, proc step	16
4	Automated Log Analyzer Dashboard	Palanisamy Mohan, Amarnath Vijayarangan	The importance of validating a SAS program through the generated log file is inevitable. A successful execution would require an ERROR, WARNING and other system message free log. Though, the severity of NOTE or WARNING might not be very high, but there are chances for multiple NOTES or WARNINGS together in a program that can cause severe problems or incorrect results equal to an error message. This means that one needs to carefully review all the logs that have been generated when a program is executed, but a manual review of 1000 + logs becomes taxing and time consuming. To facilitate this we propose a very simple and a graphical solution which would enable any level programmer not to miss out on any system messages that gets generated when N number of programs is executed	sas log, error message	9
5	Brief Introduction of Oncology Domains in SDTMIG,Version 3.2	Haishan Kadeerbai	The final document of CDISC Study Data Tabulation Model Implementation Guide (SDTMIG) v3.2 was released in the end of 2013. All of previous annotations in SDTMIG 3.1.3, originally published in 2012, have been incorporated in SDTMIG v3.2 that includes many enhancements and improvements from earlier versions. This paper presents a brief introduction on the oncology domains (TU, TR and RS) in Findings Observation Class , which are extremely useful in oncology clinical trials, mainly focus ed on: 1) the purpose and usage of the domains ; 2) relationship among the domains; 3) the domains' cross-data (domains) linkability and relationship	clinical trial, sdtm domain, cdisc	3
6	Building a Partnership: Constructing a global team between a US Sponsor and a Chinese CRO	Jeanina M. Worden	There may be many reasons for a sponsor to decide to work with a CRO but no matter the reason both teams always want to ensure a successful collaboration. Careful planning prior to the study work beginning is an important step towards this goal in any situation, no matter where the companies are located, but when they are in different countries planning becomes imperative. Whose SOPs will be followed? How will issues be communicated? What are the responsibilities of the leads within each company? By discussing, and coming to agreement, on these types of questions before study work starts, the teams avoid slowdowns due to confusion, miscommunications and under realized expectations. To avoid these issues Santen and Rundo teams have worked together to construct a solid plan prior to study work getting underway to clearly set expectations, anticipate challenges and develop processes to mitigate identified risks. This paper is intended to describe the steps taken and the working model for the partnership that resulted from these discussions	data cleaning, missing value	8
7	Calculation of Confidence Intervals for Odds Ratio and Binomial Ratio Using StatXact PROCs	Halimu Haridona	In clinical trials, we will face some issues when making statistical inference using asymptotic and approximate statistical methods for rare events, e.g., calculating the confidence intervals of relative risk/odds ratio for low incidence binomial endpoints. This paper will introduce how to calculate the relative risk and odds ratio and their confidence intervals using StatXact PROCs, which is a more reliable method. StatXact PROCs is a statistical package for Exact Nonparametric Inference, developed for SAS users. It is used to make reliable inferences by exact and Monte Carlo methods when the data are sparse, heavily tied, or skewed, and the accuracy of the corresponding large sample theory is in doubt	odds ratio, confidence interval, proc step, monte carlo, null hypothesis, p-value, proc freq, binomial distribution, relative risk, significance level, sample	7
8	Collapsing Adverse Event Records	Haiqiang Luo	In clinical trials, the way of collecting adverse events (AEs) is diverse and collapsing AEs sometimes becomes necessary. Categorizing AEs into collapsible and non-coll apsible, and collapsing the collapsible AEs are challenging tasks for SAS programmers in the clinical trials. This paper will introduce how to categorize AEs into collapsible and non-collapsible. Three types of collapsible AEs will be discu ssed. 1) Multiple AEs with same onset date; 2) Multiple AEs with a time contiguous sequence; 3) Overlapping AEs. This paper will introduce the implementation of collapsing and sample SAS codes for each type of collapsible AE records	clinical trial, sample, dataset, proc sql, adverse events	7
9	Compartmental Models in SAS: Application to Model Epidemics	Ka Chun Chong, Benny Chung-Ying Zee	Compartment modeling is useful to quantify the spread of elements in a dynamic system. Apart from the Pharmacokinetics-Pharmacodynamics analysis, epidemic modeling is another broad application of compartmental models. In this paper, we demonstrate how to use `PROC MODEL` and arrays in DATA steps to generate and fit the epidemic models such as the Kermack and McKendrick model and SEIR model. Practical application is demonstrated for the 2009 pandemic A/H1N1	proc model, proc step, poisson distribution	9
10	Data Transparency and Sharing: Research Benefits, Risks and the Future	Janet E. Stuelpner, Matthew J. Becker	Whether called data transparency or data sharing, there's a movement to give more researchers greater access to patient-level clinical trial data. The goal is to create an environment for innovation in clinical research. Join this presentation to discuss what is being done, including exploring the value to the overall health care system of creating a multi-sponsor environment that gives researchers access to larger pools of data	clinical trial, clinical data, life sciences	3
11	Design and Construct Efficacy Analysis Datasets in Later Phase Oncology Study	Huadan Li, Changhong Shi	Under the CDISC frames, the Therapeutic Area Standards (TA Standards) have already been the hotspot. The Coalition for Accelerating Standards and Therapies (CFAST) TA Standards Program was launched to accelerate clinical research and medical product development by facilitating the establishment and maintenance of data standards, tools and methods for conducting research in therapeutic areas important to public health. The Oncology TA is the pioneer of this effort. The SDTM Oncology domain models for Tumor Identification (TU), Tumor Results (TR) and Disease Response (RS) are available in SDTMIG v3.1.3. However, the Oncology TA ADaM has not achieved any standardization. This paper will demonstrate how to design and construct standard efficacy analysis datasets in late phase Oncology studies according to the SDTM Oncology domain models. Key word: SDTM Oncology domain models, Oncology TA, ADaM	adam dataset, efficacy summary, sensitivity analysis, primary endpoint, data science, proc step, overall survival, clinical research, survival analysis, proc phreg, cdisc	9
12	Extend SAS by R	Derek Li, Helen Wu	Figures, simulations, and new statistical methodologies have become increasingly important and needed in pharmaceutical industry. Even though SAS is a powerful leading statistical analysis software in pharmaceutical industry, it is still not realistic to depend only on SAS to deal with all figures, simulations and new methodologies. Therefor extend SAS by some other tools are needed and there are several possible ways to extend SAS. One attractive option is to extend SAS by R, a free statistical environment which offers a wide of variety of statistical and computing techniques. This paper will share the knowledge about how to extend SAS by R and illustrate corresponding application using graph programming	pharmaceutical industry, proc step	5
13	From Coal Mining to Data Mining: Advancing Programming Management for Clinical Projects with Text Analytics	Zhouming Sun	Both coal mining and data mining involve the process of extracting valuable materials from raw resources to form economically useful packages. Due to the theoretical similarities behind these two different types of mining, the process of coal mining, as will be described, serves as inspiration for the development of the Simultaneous Monitoring of Analysis and Reporting Toolkit (SMART), a method for data mining based on the techniques of text analytics using Base SAS@ 9'. Established with a general SAS macro, SMART is a versatile toolkit that allows for the reporting of the real-time status of programming activities. By using the techniques of text analytics to find explicit relationships between documents by classifying documents into predefined ordata-driven categories, SMART makes management of clinical project programming more effective and dynamic. This paper will introduce the concepts of SMART, followed by a presentation of its four key processes. It will additionally demonstrate the power of text analytics in extracting useful information while providing a helpful roadmap for project leaders to efficiently manage programming activities independently of a project leader's programming skill or experience	data mining, data cleaning, adam dataset, proc hpsplit, clinical programming, neural network, excel, cdisc	10
14	Marco free automation to identify and drop variables with all values as missing	Vamse Goutam	Missing values do have a vital role to play in handling data either numeric or character. Especially when a variable is completely missing it taxes the execution speed and also the space that the dataset occupies. However, if we are able to identify these missing variables even before the analysis or study and drop them it would significantly increase the execution speed and would also save a good impactful amount of space in the server. There are several solutions to this problem, but this paper introduces a macro free automation to identify and then drop the completely missing variables, as any use of macros or even a simple `PROC FREQ` would run into a risk of either a complete failure or slowdown of the process due to insufficient macro length or numerous levels in the dataset	proc step, missing value, character variable, data step, proc freq, proc format, proc means, numeric variable, user-defined format	5
15	Monitoring Child Growth and Safety Profile using Growth Charts	Rajesh Babu Moorakonda, Mihir Gandhi	In child nutritional trials, graphical presentation of subject-level anthropometric parameters over time is useful to visualize child growth trajectory according to age. The trajectories of selected anthropometric parameters are usually evaluated with reference to a growth chart developed using growth standards, such as World Health Organization (WHO) child growth standards. Growth charts provide expected percentile values of the anthropometric parameters based on children of same gender and age-group from the general population. It is usually important in child nutritional trials to monitor the child growth for potential effect of study product feeding, adverse events and concomitant medications. This paper describes step-by-step procedure for plotting subject-level trajectory of an anthropometric parameter during the trial period on the growth chart developed using WHO child growth standards. The chart also displays details of onset of adverse events as well as intake of concomitant medications. It uses `PROC GPLOT` and SAS Annotate facilities	adverse events, concomitant medications, proc gplot, median, proc step, standard deviation, sample, visit	9
16	Plotting Against Cancer: Creating Oncology Plots Using SAS	Debpriya Sarkar	Graphs in oncology studies are essential for getting more insight about the clinical data. This presentation demonstrates how `ODS` Graphics can be effectively and easily used to create graphs used in oncology studies. We discuss some examples and illustrate how to create plots like drug concentration versus time plots, w aterfall charts, comparative survival plots, and other graphs using Graph Template Language and the `ODS` Graphics p rocedures. These can be easily incorporated into a clinical report	proc sgplot, heatmap, waterfall chart, proc sgpanel, graph template language, correlation, proc step, bar chart, confidence interval, do loop, hazard ratio, clinical trial, odds ratio, proc template	30
17	Quick Graphs with ODS Graphics Designer	Sanjay N. Matange	You just got the results of the study and you want to get some quick graphical views of the data before you begin the analysis. Do you need a crash course in the SG procedures just to get a simple histogram? What to do? The `ODS` Graphics Designer is the answer. With this application you can create many graphs including histograms, scatter plots, scatter plot matrices, classifica tion panels and more using an interactive 'drag -and-drop' process. You can render your graph in batch with new data and output the results to any open destination. You can view the generated GTL code as a leg up to GTL programming. You can do all this w ithout cracking the book or breaking a sweat	box plot, kaplan-meier plot, histogram, scatter plot	15
18	SAS2VBA2SAS: Automated solution to string truncation in PROC IMPORT excel	Amarnath Vijayarangan	SAS `PROC IMPORT` is one of the most commonly and widely used approach to convert flat files that are native to other database engines into SAS datasets. Nevertheless, this procedure also has its own flaws. The most commonly encountered issue is that `PROC IMPORT` might not properly assign the length for character variables as it fixes the length of a particular character variable based on the initial few observations which leads to string truncation. The proposed approach delivers a solution that combines SAS and `EXCEL` VBA to resolve the problem by using the `DATA STEP` code generated in the log while importing flat files without any manual intervention	character variable, proc import, proc step, data step, flat file	6
19	Submission of Pharmacokinetics (PK) Data in a CDISC Compliant Format	Yu Zhu	The legacy Pharmacokinetic (PK) data are usually produced from different sources with different data format: sample collection on CRF, concentration results from Lab findings, and parameters calculated by pharmacokineticist. This brings a lot challenge when preparing SDTM and ADaM datasets for submission. This paper introduces some of the challenges and solutions associated with mapping the concentration data and the PK parameters data into SDTM (e.g. PC and PP) and subsequent creation of ADaM datasets	pk parameter, adam dataset, key, data science, raw data, sample, data cleaning, sdtm domain, cdisc	9
20	The Power of SAS National Language Support - Embrace Multilingual Data	Chao Wang	As clinical trials take place around the globe, data in different languages may need to be handled. The usual solution for this issue is to translate the data to English, after some manipulations, results then will be translated back to the original language. Since version 9.1.2, SAS software can provide a specific function called National Language Support, this issue would be easy to handle. In this paper, an introduction will be made on: 1) how to build the ready SAS session to import and display the mixed data with NLS system option 2) how to use the edge tool - SAS K-Function (including K-macro Function) to do data manipulations and 3) how to display any character in reports with the Unicode support. Here would use a multilingual (Chinese and English) AE dataset as an example on SAS 9.3 PC version. The related applications on the UNIX server and SAS Enterprise Guide would also be discussed	options statement, proc step, raw data	10
21	Using SAS SG Procedures to Create and Enhance Figures in Pharmaceutical Industry	Shuo Chi, Huashan Huo	`SAS/GRAPH` statistical graphics (SG) procedures provide a simple syntax for creatin g graphics commonly used in exploratory data analysis and for creating customized statistical displays. This paper will focus on 1) how to use SAS SG Procedures to create pharmaceutical figures , 2) Introduce the advantages and enhancements of SAS SG Proced ures, 3) New features in SAS SG 9.3 version to enhance both graphic visualization and printer -friendly result, 4) How to prepare statistics for better and flexible graphic visualization using SAS SG	data science, proc sgplot, proc step	12
22	Visualizing Clinical Trial Data	Matthew J. Becker	Today, all employees at health and life science corporations may need access to view operational data. There may be visualization needs for a business analyst to compare clinical trial spend versus similar past trials; for a clinical research associate to easily see what research sites may need more monitoring; for a data scientist to quickly and easily explore adverse event data for outliers; for a medical writer to have graphical patient profiles; for a CEO to immediately have high-level dashboards of operational performance. In this presentation we will discuss SAS Visual Analytics and demonstrate the benefits of visualization for health and life science organizations	adverse events, business intelligence, clinical trial, life sciences, proc freq	10

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