PharmaSUG CN 2012 Papers - Index

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

go 2014

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A SAS Macro Application to Create Mock Tables in Statistical Analysis Plans for Phase I Clinical Studies	Yao Huang	A SAS macro %mock_table application is presented to easily and quickly generate mock tables for Phase I Statistical Analysis Plans (SAP). Mock tables are guides for programming final tables to present summaries of clinical study data. They are most commonly created using a word processor, e.g., Microsoft Word, and are often tedious to create from scratch or modify afterwards when columns and rows are added, deleted, or reformatted. By implementing with a simple `EXCEL` template, macro %mock_table can be used to create almost all mock tables required by Phase I clinical studies efficiently. This paper will use an example Phase I oncology study to show how to prepare `EXCEL` template sheets with minimal work and then use %mock_table to create mock tables immediately	phase i, clinical trial, excel, sample, macro definition, data science		7
2	A Web-based Project Management and Tracking System	Zheng Wang, Yuntian Zhao, Yanhong Li	Presentation
3	An Illustration of Rate Difference Estimation with SAS in Logistic Regression	Yun Guo, Victor Wu, Xinyu Xu, Crystal Cheng	Rate difference is widely used for the binary endpoints in non-inferiority studies. General statistical methods for un- stratified and stratified tests are Z test and CMH type tests respectively. They, however, are not suitable for adjusting covariates, which sometimes are essential in clinical trials. Logistic regression along with Delta method could be used when covariate adjustments are needed. We used an example to show how to program in a SAS environment to get the rate difference along with the confidence interval	logistic regression, logistic distribution, regression analysis, confidence interval, proc step, proc logistic, covariance, normal distribution, sample, clinical trial, z-test, proc iml, binary variable	ppt	7
4	Are you using Proc Report yet?	Priya Ramaswami	Presentation
5	Career Development	Ruotao Wang	Presentation
6	Challenge in Legacy Studies Converting to CDISC - A Case Study from MacroStat	Weijin Gan	Presentation	cdisc, macro definition
7	Clinical Review on iPad with DEFINE.XML	Sy J. Truong	Clinical data review on a tablet computer is a natural step in the evolution of automated information delivery. Clinical Research Associates and Medical Monitors require a unique method of accessing information during the conduct of a clinical trial and also at the pivotal conclusion, or submission of a trial. The challenge remains to overcome the technical complexity of metadata models such as DEFINE.XML. This paper describes these challenges as it elaborates upon methods of using SAS to deliver clinical data in a format that can be easily navigated on an iPad. Some of the methodologies described include: Capturing data with standardized metadata of DEFINE.XML Navigating to specific clinical domains with standardized data structures with multi-touch Searching for subject data such as Adverse Events on iPad Viewing clinical data while mobile and offline * Zooming in on images such as CRF or graphs with pinch gestures These technologies are new and will pave ways for tools in this "post-PC World". Even though data review of clinical information remain as old as the drug development process	clinical data, dataset, clinical research, proc contents, adverse events, data review, proc step, clinical trial, cdisc		10
8	Exploring RETAIN statement	Mingxia Chen	Presentation
9	Fitting PK Models with SAS NLMIXED Procedure (Paper, Appendix)	Halimu Haridona	Pharmacokinetic (PK) models are important for new drug development. Statistical models with both fixed and random effects can be fitted by nonlinear mixed models for PK analysis. `SAS/STAT` `NLMIXED` procedure fits these models using likelihood-based methods. This paper uses a PK data example to demonstrate how to establish PK model and how to implement it using the SAS `PROC NLMIXED`	proc mixed, likelihood function, proc step, proc nlmixed	pdf	6
10	Introduction of an Analysis Table Translation Tool	Jessie Zhao, Mina Chen, Shengying Cheng, Yanyun Shen	Background: Due to the requirement of SFDA, all clinical study report (CSR) will need to be translated into Chinese. This requires that all the in text tables need to be translated into Chinese too. The company used to hire external vendors to perform this task. The translated product needs another round of internal review which is very time consuming and labor intensive. Our Solution: We established a project to instruct two interns to generate a tool using SAS to automatically convert analysis tables from English into Chinese. With the use of this tool, all in text analysis tables will not need to be manually translated but automatically transl ated into Chinese in text tables. This greatly increased the efficiency of translation part of the analysis tables used for CSR and reduced the risk of incidental mistakes which can potentially occur during the manual translation process. The other significance of this tool is that it can be modified easily to translate from English to other languages too as long as SAS software and the operation systems support these other languages. To implement this tool in practice, it requires a close collaboration between SAS programmers and biostatisticians. This paper also proposed a working process to ensure effective application of this tool in project work. In summary, we feel this has been a great project as an internship program as it not only provided a challenging opportunity for our interns to develop sophisticated SAS programming skills but also independent research and problem solving skills. Below is an example of the original analysis in text table and a Chinese translated version of it, which will be include the Chinese CSR to be submitted to sFDA. Why is it necessary to develop this tool? According to sFDA requirement, all CSR including in-text tables should be in Chinese. Before this tool is generated, the whole CSR including the in-text tables are manually translated by vendor companies	adverse events, excel	ppt	4
11	Make your code file driven - Methods to let SAS collect file names in your system	Lu Zhang	Presentation
12	Methods to Generate Unavailable Categories in Interim Analysis	Xiaoran Yin	In most of clinical studies, we have to analyze the reasons for premature discontinuation for each treatment group. There might be 8 or more reasons be displayed on analysis table, even though some of them are unavailable then. We would like to output the unavailable count number as zero. But we can not just hard code them on our SAS programs among interim analysis period since some of these categories might be available at the final analysis. How to make our programs more powerful to handle these uncertain situations? This paper will give you methods you are looking for	proc step, interim analysis, proc means, adam dataset, class variable, adverse events		6
13	ODS TAGSETS - a Powerful Reporting Method	Xinyu Li	Understanding some basic knowledge about `TAGSETS` can enable you to create those special reports you routinely report every day . Especially when combined `TAGSETS` with styles, it can be used to generate good-looking output. In this paper , we take a quick re view about the `TAGSETS` and show how to apply styles and `TAGSETS` together for reporting	proc step, excel, proc template	ppt	8
14	Rising to Excellence - How a China CRO Achieves International Standards	Helen Yin, Minzhi Liu	Presentation	data cleaning, independent qc, quality control, access control, data validation rules, csr, validation documentation		27
15	SAS Data Query and Edit Checks with HTML5	Sy J. Truong	Querying data used to require sophisticated algorithms and programming with SQL expertise or SAS `DATA STEP` techniques. Now, there are many user-friendly graphical tools to build expressions without having to program code to access relational databases. However, there are few tools for creating queries from a web interface upon SAS data. This paper describes a real world project where data managers extract data from a relational database and transfer it to SAS data. Data managers need a user-friendly query tool to verify the cleanliness of the resulting SAS data. Since data managers are not SAS programmers, the HTML5 web interface eases the learning curve allowing them to perform tasks such as: creating compound query expressions, deriving temporary imputed variables, saving and combining saved queries. These are some of the methods described in this paper as it explains how to use SAS `DATA STEP`, HTML5 and XML to accomplish an effective user experience. The initial target audience for this project was to empower data managers to perform tasks only SAS programmers were able to do. However, the user-friendly interface extends usage to a larger audience of non-techie power users	dataset, clinical data, proc step, proc sql, relational database, sas programmer, proc freq, data cleaning	pptx	17
16	Setting up and Managing a Statistical Programming team in China for an US based global CRO company	Shilin Wang	Presentation
17	Systematically reordering Axis in SAS graph	Brian Shen	Once generating SAS graphs, it is a headache to programmers to reorder the axis tick values parameter by parameter, especially for generating big amount of graphs for different parameters or for data changing on daily basis, for example, marketing data. SAS default ordering is sometimes not desirable for procedures like `GPLOT`, GCHART, etc. Although some great enhancements on new 9.3 STAT graph package and the Graph Template Language [GTL], the old GCHART, `GPLOT` procedures are still widely applied by SAS programmers. This paper presents a method on how to systematically replace the default SAS graph ordering for better graph output to simplify programming activities	macro variable, graph template language, missing value, macro definition, %if-%then-%else	ppt	9
18	Taste of SDTM in Real Time	Changhong Shi, Beilei Xu	Presentation	cdisc
19	The Armchair Quarterback: Writing SAS Code for the Perfect Pivot (Table, That Is)	Peter W. Eberhardt, Louanna Kong	"Can I have that in `EXCEL`?" This is a request that makes many of us shudder. Now your boss has discovered `EXCEL` pivot tables. Unfortunately, he has not discovered how to make them. So you get to extract the data, massage the data, put the data into `EXCEL`, and then spend hours rebuilding pivot tables every time the corporate data are refreshed. In this workshop, you learn to be the armchair quarterback and build pivot tables without leaving the comfort of your SAS environment. In this workshop, you learn the basics of `EXCEL` pivot tables and, through a series of exercises, you learn how to augment basic pivot tables first in `EXCEL`, and then using SAS. No prior knowledge of `EXCEL` pivot tables is required	excel, dataset, proc step, proc tabulate, output delivery system, proc means, xls, raw data, macro variable		36
20	The SAS Hash Object: It's Time To .find() Your Way Around	Peter W. Eberhardt	"This is the way I have always done it and it works fine for me." Have you heard yourself or others say this when someone suggests a new technique to help solve a problem? Most of us have a set of tricks and techniques from which we draw when starting a new project. Over time we might overlook newer techniques because our old toolkit works just fine. Sometimes we actively avoid new techniques because our initial foray leaves us daunted by the steep learning curve to mastery. For me, the PRX functions and the SAS hash object fell into this category. In this workshop, we address possible objections to learning to use the SAS hash object. We start with the fundamentals of the setting up the hash object and work through a variety of practical examples to help you master this powerful technique	hash table, dataset, proc sql, hash iterator, out=, lookup, conditional do loop, order by, sysrc, data merging, proc step, sample		17
21	Using CALL EXECUTE to generate different report forms	Danna Chen	In the pharmaceutical industry, according to the actual data, we often need to output different report forms by using `PROC` steps. For example, the output of blinded data and unblinded data are different. Call Execute statement can be used to execute procedure conditionally. This paper will outline this approach to accomplish these tasks and discuss the usage of Call Execute where this subroutine come up with a better alternative to the usual way	call execute, data step, proc step, %if-%then-%else		8
22	Using HASH Table to reduce the use of merge for large datasets	Long Fang	In Dataset programming, it is very common to merge by some variables from other datasets. If we use match-merge, we have to sort the datasets firstly. But for datasets with large amount of records, it will cost lots of I/O time. In a situation like that, a HASH table can be used to provide a lookup table using the key variables without `PROC SORT`. For example, in SDTM programming, we can build a hash table from SV domain using USUBJID and VISIT as a key variable. Then, we can get the variable SVSTDTC/SVENDTC into other domains without `PROC SORT` and `MERGE` statement. In this paper, we will provide examples to show how to use hash table to merge one or several tables with one table. And there is also an example to use macro to create and name the hash object, specify key variables, and look up the data in hash table. We will also provide some debug tips for the hash object	hash table, data step, proc sort, proc step, execution phase, lookup, proc sql, sdtm programming, cdisc		7
23	Using PROC REPORT and ODS for Customized Presentation of Study Results and for Medical Informatics Sparklines	Jim Sattler	SAS software is the instrument we play to produce the music of data called information. New and previously existing features in the SAS Output Delivery System (`ODS`) can communicate text and graphic information clearly, simply, and effectively. These features, if used judiciously and discretely, can facilitate the understanding of clinical information. `ODS` Graphics can produce information of value not only to statisticians but also to physicians. With the release of `ODS` Graphics in SAS Version 9.3, it is now possible to create word-sized micrographs called "Sparklines" for medical informatics reporting entirely in Base SAS	proc step, proc report, output delivery system, sas base, sas output, proc sgplot	pptx	11
24	Why the Bell Tolls 108 times? Stepping Through Time with SAS	Peter W. Eberhardt	For many SAS programmers, new or even advanced, the use of SAS date and `DATETIME` variables is often very confusing. This paper addresses the problems that the most of programmers have. It starts by looking at the basic underlying difference between the data representation and the visual representation of date, `DATETIME` and time variables. From there it discusses how to change data representations into visual representations through the use of SAS formats. Since date manipulation is core to many business process, paper also discusses date arithmetic first by demonstrating the use of simple arithmetic to increment dates; then by moving on to SAS functions which create, extract and manipulate SAS date and `DATETIME` variables. Finally, paper demonstrates the use of the %sysfunc macro function and the `%LET` statement to present date, `DATETIME` and time variables. This paper is introductory and focuses on new SAS programmers, however, some advanced topics also covered. This paper is introductory and is intended for new SAS programmers	sysdate9, date variable, datetime variable, time variable, macro variable, datetime, sas programmer, sas function, time, proc sql, character variable, relational database, format statement, macro function		15

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