PHUSE EU 2011 Papers - Index

111 papers. Source: PHUSE (Pharmaceutical Users Software Exchange) EU Connect conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	"Overnight" Conversion to SDTM Datasets - Ready for SDTM Submission	Niels Mathiesen	This demonstration shows how legacy data (in any fo rmat) is converted to SDTM datasets. Clivax does the job for you - divided in five simple conversion steps: 1) Import of the "raw" data into the system. 2) Assign critical issues with the help of drag and drop. 3) Integrate the assigned data into the system. 4) Activate the SHARE library and supplement your own SDTM Domains as needed. 5) Export the SDTM You get your SDTM submission delivered express "Overnight", fast and simple without any programmers or programming work involved The presentation will be finished with an overview of the built-in power of Clivax. See how the setup of a study can be made within 4 hours - the SDTM submission included. Clivax offers you the ultimate way to take advantage of the compliance with the CDISC / SDTM standard and get SDTM submission for free	sdtm domain, 21 cfr part 11, cdisc	source presentation source	7
2	A Career in the Pharmaceutical Industry	James McDermott		clinical trial, clinical research, pharmaceutical industry, phase iii, phase iv	source presentation source	10
3	A Comparision of SAS Verus Microsoft Excel and Access's Inbuilt VBA Functionality	Jozef Tarrant	There are a great variety of business situations where it is necessary to automatically export data from a large number of similar Microsoft `EXCEL` spreadsheets (perhaps reports, forms etc.) into a central database, without access to a SAS installation. 7KLVSDSHUZLOOVKRZKRZWRXVH0LFURVRIWŠ([FHODQG0LFURVRIWŠ$FFHVV¶VLQEXLOW9%$IXQFWLRQDOLW\ to program this type of export. The export process will run with user-friendly dialogue boxes that gather parameters from the user such as where the source files are located, and the destination database. Finally, a brief comparison of a SAS approach to consolidating data will be presented, allowing the strengths of the alternative approaches to be evaluated. No prior knowledge of VBA is necessary for the programmer or end user. All the code required is supplied within this paper	excel, xls, data step, proc sql	source presentation source	11
4	A is for Acronym!	Alex Hughes	A light hearted account looking through the eyes of a new starter and his induction into the pharmaceutical industry. Covering the first six months of my c areer, coming straight from postgraduate study, I take a look back at my early and naive days of; protocol deciphering, programming debugging and attempts at understanding the endless, ever - changing, list of acronyms that are now part of my everyday life	pharmaceutical industry, sas programming	source presentation source	4
5	A Modern Software Design Principle Applied to SAS Macro Programming: The Inversion of Control Concept	Patrick René Warnat	The inversion of control (IoC) architectural concept is a programming principle commonly used in object oriented programming. The basic concept is to implement high-level (in relation to the level of abstraction) source code more reusable by making it independent from low-level, problem-specific code. Program flow and thus control is inverted by using a callback approach which means that code on a lower abstraction layer calls code defined at a higher abstraction layer. This is an inversion of the program flow direction used in traditional procedural programming. The big advantage of IoC is that you get more reusable code, that you decouple your code and thus have less risk of side-effects when you change your code. The usage of the IoC principle is not limited to object oriented programming. This article points out how to apply IoC to SAS macro programming with an actual example and how to benefit from its advantages	macro definition, sas programming	source presentation source	4
6	A SAS macro to check SDTM domains against controlled terminology	Guido Wendland		sdtm domain, dataset, macro variable, controlled terminology, excel, in=, macro parameter, variable list, macro definition, cdisc	source presentation source	10
7	A simple interface for defining, programming and managing SAS edit checks	Virginie Freytag, Michel Toussaint	If SAS is still widely used for data cleaning, ED C technology has however deeply changed the schedule of data cleaning management. The validation plan should now be defined at an early stage of the project, while programs must be ready to send off-line queries as soon as t he data collection has started, in conformity with first-stage specifications. During the course of the trial, it is not rare to m odify the validation plan, and this along with the subsequent programming changes, should be managed as well. In this context we have designed an optimized SAS w orkflow interfaced with a simple web-based application, allowing the simultaneous definition of edit checks and the automatic generation of the corresponding SAS source codes. The validation plan and underlying programming are then entirely managed through this user friendly web application offering an integrated solution for the fast, efficient and traceable implementation of edit checks	data validation rules, data cleaning	source poster source	7
8	Access Control Execution Systems (ACES)	Shailesh Kulthe, Eric Silva	Adaptive trials have gained increasing acceptance both by the industry as well as regulators. There is growing interest towards the usage of adaptive methodology in clinical trials. There are two main challenges in implementing clinical trial, especially adaptive clinical trials. One is scalability, constrained by the sheer manual overhead and the other, maintaining the trial integrity, constrained by manual procedures. These strongly point to the need for an execution system that can automate the manual efforts and enforce access controls. In this paper the authors will discuss &\WHO¶VACES, a web-based system that specifically addresses these needs ± for both adaptive and conventional trial. ACES supports different types of trials; provides access and security within a controlled environment; manages the interim analysis workflow; notifies users of new trial activity;; and provides secure access to reports and trial documents	interim analysis, clinical trial, access control, audit trail, adaptive design, randomization	source	10
9	ADaM on a diet: preventing wide and heavy analysis datasets	Dirk Van Krunckelsven	2QHRIWKHSUREOHPVZLWK&',6&¶V$QDO\VLV'DWD0RGHO$'D0LVWKHVRFDOOHGDQDO\VLV -enabling " variables. Often all possible subgroups, stratifiers, baseline values, categories of baseline values, etc. are all created in the Subject Level analysis dataset (ADSL). Frequently, these are all merged onto every other dataset. Accommodating the potential need for outputs or exploration. An alternative is proposed that limits the width of the ADSL dataset: storing such additional information in a BDS - type structure. The principle is similar to the Supplemental -feature in SDTM. Provided that parameter value level metadata is available, transposing and merging such supplemental subject -level information onto other ADaM datasets is trivial. This approach enables standardized storage of subject level information not defined LQ&',6&¶V$'D0,WNHHSV ADSL slim and all other datasets focused. However, all information remains readily available for additional analyses on the ADaM ± and potentially the SDTM ± data	adam dataset, bds, cdisc	source presentation source	5
10	ADaM Validation and Integrity Checks	Louise Cross	The release of the CDISC ADaM Validation Checks Version 1.0, dated September 20, 2010 and updated version 1.1, dated 21JAN2011, is a positive step in highlighting sense checks to perform on ADaM data checks to ensure compliance with the model. At ICON, this document has resulted in the creation of a key validation tool, especially useful when working on large conversion projects. We have created a standard SAS macro to use across any ADaM project, which performs these checks and outputs any issues to be addressed. When working on a large conversion project covering over 30 studies, this became a valued tool to ensure consistency and accuracy	data cleaning, adam dataset, excel, cdisc	source presentation source	5
11	Adoption of Agile in Medidata	Tony Hewer, Andrew Smith			source presentation source	8
12	Alternative Approaches to Creating Disposition Flow Diagrams	Brian P. Fairfield-Carter	The description of subject screening, enrollment, randomization, completion and discontinuation is generally referred to as 'subject disposition'. Subject disposition tends to be reported in tabular format, providing frequency counts in various disposition categories, occasionally supplemented by 'time-to-event' graphs for subject discontinuation. However, since disposition basically describes how subjects 'flow' though the life of the project, the clearest and most intuitive means of presenting is with a flow diagram. The problem is that there is no SAS procedure especially well-suited for creating flow diagrams, probably because the layout and content of flow diagrams is highly variable and unpredictable	excel, txt	source presentation source	9
13	An Animated Guide: A program to read information in SAS headers and produce reports for programming	Russell M. Lavery	This paper describes, and provides to the public, a program that can be used to help manage a department of SAS programmers. The program will, if a user specifies one or more, top level director ies on a windows machine, recursively search under those top level directories and find every SAS program and SAS table under those directories. It will then create data sets containing information on every fil e and program. It will also create pdf documentation on programs using the program headers and comments. Compare this functionality to that of the dictionary tables which only contain data about tables in established librefs and no information about programs	data validation rules, dictionary tables	source poster source	19
14	An Animated Guide: An Introduction to Latent Class Clustering in SAS	Russell M. Lavery	This is the first in a planned series of three papers on Latent Class Analysis. Latent Clustering An alysis (LCA) is a method that uses categorical variables to discover hidden, or latent, groups and is used in market s egmentation and medical research. This paper explores `PROC` LCA, a free SAS add-in created by The Methodology Center at Penn State University. Using this free, and easy to install, add-in allows users of SAS to perform Latent Class Clustering using syntax with which they are already familiar. The managerial output of the Latent Cluster Analysis, (sometimes called Latent Class Analysis) is si milar to output from other clustering methods	proc step, latent variable, cluster analysis, proc freq, proc calis, raw data, factor analysis, sample	source	19
15	An Approach to Standard Programming in a Central Data Repository	Robert Ellison		data cleaning, clinical data	source presentation source	6
16	Analysis and Reporting Toolset (A&RT): Lessons learned on how to develop a system with an external partner	David Smith	AstraZeneca needed a global analysis and reporting system. The business knew what it wanted but were inexperienced working in a collaboration with IS and an external IS development partner. The business application development programmers had traditional experience working closely with users to prototype SAS AF and related systems. In the new world, documenting requirements then waiting for the IS development partner to deliver the solution to the business was often frustrating and confusing requiring a number of development iterations. In 2009, the first working version of the Analysis and Reporting Toolset (A&RT) was implemented. The project was a challenging and fascinating orchestration of activities performed by a multi-expert and cross functional team. This presentation is a guide to anyone in a business department planning a similar project and provides information on lessons learned from the experience at AstraZeneca	clinical research, clinical data	source presentation source	9
17	Architect - A Platform for Design and Analysis Software for Clinical Trials	Shashank Maratkar	Design and Analysis of clinical trials involves evaluating several designs, simulating them to assess performance and proceeding with one chosen design, collecting and analyzing trial data, carrying out adaptations as permitted by the protocol, and reporting trial results. Each of these specialized operations is often supported by separate pieces of software that do not necessarily seamlessly share data and maintain a common workflow. &\WHO¶VArchitect software platform addresses this need by providing common infrastructure to integrate the functional components. It provides common functionality such as data handling, comparing designs across different software components, reporting, workflow, and archiving of designs and results. Command line batch language supports scripting and workflow automation	sample, sample size, clinical trial, interim analysis, effect size, group sequential design, confidence interval, dataset, proc calis	source	12
18	Bayesian hypothesis testing for proportions	Antonio Nieto, Javier Gómez	Most clinical trials contain tests on proportions, usually they are answered by means of t he Frequentist approach, nevertheless another valid option could be to solve them using a Bayesian approach. The Bayesian approach has the advantage that it is not rest ricted to only one alternative hypothesis. Mo reover, the hypotheses to be tested do not necessarily overlap. In this paper we show a SAS macro to perform Bayesian hypothesis testing for proportions, that can be also extended to other kinds of endpoints and distributions. For simp licity only the null and one alternative hypothesis are shown. This macro is constructed assuming an improper prior distribution, the uniform (0,1), and a Beta as the posterior conjugate distribut ion. Therefore after calculating the pr oportion of successes in the trial, the probability of being under the null hypothesis or under the alternative hypothesis and a text label indicating the highest probability are shown	beta distribution, clinical trial, alternative hypothesis, prior probability, macro definition, binomial distribution, normal distribution, null hypothesis, hypothesis testing, sample	source presentation source	6
19	Building Block macro principle for Standard Program Library Design	Francis Dsa			source presentation source	8
20	Calling R Functions from SAS	Peter Bewerunge		proc iml, proc step, dataset, error message, macro variable, exception handling, format statement, sas log, sysdate9, sas library, sas enterprise guide	source presentation source	7
21	Challengers Facing the Programmer in Observational Research	Laurence Carpenter	Observational Research (OR) is an increasingly important component in drug development and commercialization. OR contributes evidence regarding the frequency, distribution or clinical burden of disease, the natural history or clinical course of disease, the design of clinical trials, cost and utilization patterns, and the safety and effectiveness of interventions. In the current environment there is increased demand for information from OR studies for regulatory and reimbursement authorities. Regulatory agencies are requiring large-scale observational studies for pharmacovigilance, risk assessment/management, and communication of risk:benefit profiles. Payers are increasingly demanding health technology assessments, the demonstration of real world value of interventions, and comparative effectiveness through OR designs	clinical trial, clinical research	source presentation source	4
22	Chernof face graphs as an efficient way of creating comprehensive Patient Profiles in SAS	Adam Amborski	Patient Profile listings usually contain a large range of data domains and can run for many pages. With such a large volume of information, it may not be simple or quick to draw together a holistic view of a patient's health or group patients together. But it doesn't need to be that way! Instead, imagine a comprehensive graph containing a patient's key data in one place, making interpretation of Patient Profiles much easier. One graph can be used to reflect various health characteristics such as vital signs and laboratory data. Just as the face can reveal one's health, why not present the patient's 'face' on a graph? Chernoff faces can display multivariate data in the shape of a human face. The aim of the proposed paper is to present useful tips on how to create Patient Profile face graphs in SAS	macro definition, adverse events	source presentation source	5
23	Chernoff face graphs as an efficient way of creating comprehensive Patient Profiles in SAS	Adam Amborski	Patient Profile listings usually contain a large range of data domains and can run for many pages. With such a large volume of information, it may not be simple or quick to draw together a holistic view of a patient's health or group patients together. But it doesn't need to be that way! Instead, imagine a comprehensive graph containing a patient's key data in one place, making interpretation of Patient Profiles much easier. One graph can be used to reflect various health characteristics such as vital signs and laboratory data. Just as the face can reveal one's health, why not present the patient's 'face' on a graph? Chernoff faces can display multivariate data in the shape of a human face. The aim of the proposed paper is to present useful tips on how to create Patient Profile face graphs in SAS	macro definition, adverse events	source poster source	5
24	ClinXPORT a software to streamline the analysis and reporting of clinical trials with SAS	Juline Vanwinsberghe, Francois Vandenhende	The reporting process in clinical trials can be schematized in three phases: standardization and derivation of source datasets, statistical analysis and calculations, and finally results reporting by creation of the output tables, listings, and graphs. The method proposed here is to follow a simple, clear, structured streamline for these activities, and to automate the coding of the last phase in order to focus SAS programming (and validation) to only data derivations and calculations. The concept is based on the definition of all levels of information (project parameters, output shells, etc) in an `EXCEL` workbook, and using named ranges to identify them. Then, a set of SAS macros is used to read these meta-data and automate the outputs production. This approach has been implemented and validated on more than 60 studies for several companies in pharmaceutical industry. The estimated time rate gained in reporting activaties is about 25%	excel, xls, name range list, macro definition, macro variable, proc report, sas programmer, proc step, clinical trial, format statement	source	9
25	Collaboration versus conflict: building successful working relationships	Diana Stuart		clinical trial, data governance	source presentation source	5
26	Comparing dataset metadata	Jim Groeneveld	Similarities and differences of structure and data between two datasets at a time can be studied using `PROC COMPARE`. A SAS macro CrossRef has been developed that compares the structure of more datasets at once. It may be useful with similar datasets, i.e. which have approximately the same structure, variables and variable attributes. The information is output as a metadata dataset, a rectangular table with the original dataset names as variables/columns and meta information as records/rows. The metadata presented are library names, dataset names and labels, creation dates, change dates, number of variables, number of records and all variable attributes, like names and labels and optionally types and lengths, format and informats. In the future the information can be extended to contain descriptive statistics. From the resulting tabular dataset it can easily be seen which dataset structures are similar and which variables they have in common	reference data, proc step, proc contents, proc compare, label statement, proc freq	source presentation source	8
27	Considerations for improving program performance	Martin Gregory		macro variable, cpu time, macro facility, data step, character variable, macro definition, index, macro loop	source	7
28	Creating ADaM Friendly Analysis Data from SDTM Using Meta Data	Erik Brun, Rico Schiller	This paper introduces how it is possible to create standardised ADaM friendly analysis data with SDTM (3.1.x) as source using meta-data. The validated system, SADs4, fully incorporates CDISC standards and principles in the dataflow process at Lundbeck. The system requires the Lundbeck SADs data model, the Lundbeck meta-data dictionary, and a number of control tables specifying the study setup. The control tables contain information such as windowing, imputation methods, study specific derivations, and additions to the data model. The system has proven to be flexible, fast, and fairly easy in use. It will work with a wide variaty of indications. An additional gain is that we are able to make data integrations very fast, due to the consistent structure of the output data. The generated datasets can be used by our validated standard package of analysis and reporting programs	imputation, data standard, format statement, data science, adam dataset, cdisc	source presentation source	6
29	Critical Path Learning - How to Develop Top Level SAS Programmers	Paolo Morelli	Critical path learning is a method for ensuring continual learning of subject matter. In order to train statisticians to become top level SAS p r o g r a m m e r s , i t i s i m p o r t a n t t o d e f i n e t h e g u i d e l i n e s o f a q u a l i t y t r a i n i n g p r o g r a m a n d integrate the basic concepts so students can have a solid understanding of the fundamentals moving forward. The concept to be demonstrated in this paper is how to create a set of lessons f o r t e a c h i n g u n i v e r s i t y l e v e l s t a t i s t i c s students the basic concepts of SAS programming in a 20-hour accelerated course. As a Professor of Statistics at the University of Bologna and the CEO of CROS NT Group, I will draw on examples being implemented in both a university atmosphere and in a corporate setting through CROS NT's CROS Academy, a series of training courses and webinars from top statisticians both in the CROS organization as well as external consultants	data governance, pharmaceutical industry	source presentation source	4
30	Customising SAS OQ to Provide Business Specific Testing of SAS Installations and Updates	Steve Huggins	The SAS Installation Qualification and Operational Qualification tools provide a generic inbu ilt method of validating a SAS installation. This paper describes a simple method that allows the Operational Qualification tool to be customised to execute additional SAS code in order to provide a robust method for regression testing a new SAS installation using business specific SAS programs. The process involves selecting one or more indicative SAS programs, benchmarking the expected results and then integrating the SAS programs into the Operational Qualification tool. This paper is aimed at SAS users a nd administrators wishing to upgrade or update their SAS installation whilst comprehensively ensuring the consistency of their results	sysrc, proc compare, proc step, data step, macro variable	source presentation source	7
31	CYA with JMP - reloaded	David J. Garbutt	Presentation	clinical programming, bubble plot, laboratory data, missing value, dataset, box plot, histogram	source	38
32	Data cleaning and spotting outliers with UNIVARIATE	Michael Auld	Timely and strategic cleaning of data is crucial for the success of the analysis of a clinical trial. I will demonstrate 2-step code to identify outlier observations using `PROC UNIVARIATE` and a short `DATA STEP`. This may be useful to anyone attempting to clean systematic data conversion errors in large data sets like Laboratory Test Results	proc univariate, proc step, outlier, proc sql	source presentation source	6
33	Data-driven Software Testing for Agile Development	Priyadarshan Shinde, Ajay Sathe	Automated comprehensive testing of software functionality, especially GUI, conventionally requires extensive scripting for the Testing Tool. Agile development being dynamic with multiple changes in specifications, poses major challenges for the Testing to keep pace, making it arduous and time consuming. For software with complex GUI, comprehensive test coverage is very difficult. This paper presents a Workbench that is Testing-Tool-neutral, and that neatly addresses the demands of re-testing with changing software specs; creating extensive test-cases with mere mock-ups; accommodating new or modified GUI features; and re-using test-cases across similar software products. The Workbench integrates TestCompleteTM with Microsoft `EXCEL` for storing test scenarios ± collections of sophisticated test cases ± in pre-defined structure. This approach accommodates testing of growing or changing feature-combinations with a relatively small effort of defining the test parameters	excel, xls	source presentation source	12
34	Define Your Own SAS Command Line Commands	Duong Tran	SAS provides a number of Command Line commands and some of these are frequently used. For example the commands fsv, lib, var etc. But did you know that you can create your own? This paper will show how you can customize repetitive actions of a particular task into a Command Line command. Keywords: gsubmit, cmdmac	txt, proc step	source poster source	4
35	Defining the Governance and Process of Implementing ADaM across an Organization	Chris Decker	Over five years ago, the CDISC ADaM standard was released as a set of principles and best practices. In reality, the model was not really a µstandard¶ but a general set of guidelines for implementing analysis data; therefore companies avoiding implementing the standard. With the release of the ADaM Implementation Guide at the end of 2009 and the )'$¶VUHQHZHGFRPPLWPHQWWRVWDQGDUGV, companies have realized they have to jump on the ADaM bandwagon or get left behind. This paper presents a case study describing an ADaM development methodology, ADaM implementation lessons learned, and an overall (and ongoing) standards governance process. This case study uses experiences across projects with a various companies to cRPELQHOHVVRQVOHDUQHGEHVWSUDFWLFHVWKLQJVµQRWWRGR¶ and potential future state to support the development and use of analysis data standards	controlled terminology, adamig, standard deviation, data standard, data science, efficacy summary, raw data, cdisc	source presentation source	11
36	Development of SAS Standard Macro Systems	Katja Glass		data cleaning, macro definition, adverse events	source presentation source	5
37	Different Methods of Calculating Body Sway Area	Thomas Wollseifen	Posturography is used to assess the st eadiness of the human body by measuring the movement of the centre of pressure (COP) of a standing subject on a force platform (st abilometry) (Fig. 1). This paper presents three different methods of calculating the c entre of pressure (COP) trajectory (Fig . 2). The first method ('Convex hull') is characterized by the area enclosed by the path of movement (body sway area), approximated by the area of a convex hull. `PROC G3GRID` is applied for the tr iangulation of the data points necessary fo r calculating the convex hull. This approach is compared with the second and most common procedure ('Principal component analysis' (PCA)) which calculates an ellipse enclosing the sample points. `PROC PRINCOMP` is used to calcul ate the eigenvectors that represent the derived ellipse of the PCA	proc princomp, macro definition, proc step, proc sql, create table, sample, macro variable	source presentation source	24
38	Don't Bring a Spreadsheet to a Database Fight: Case for Microsoft Access	Ryan Burns	M i c r o s o f t E x c e l i s a p o p u l a r s p r e a d s h e e t p r o g r a m u s e d by professionals in the pharmaceutical industry from programmers to project managers. In most cases, there is little to no startup time when creating a spreadsheet and the results can be very pretty. These characteristics make `EXCEL` a product that is hard to pass up. However, it is often misused as a database platform. Luckily, Microsoft also makes a database program: Access. Access allows for more structure and easier sharing. It is also far superior if the data will be read or written by SAS. This paper will make a case for using Access inst ead of `EXCEL` and will also present some examples for setting up and using Access databases	excel, lookup, proc sql, clinical data, order by, key	source presentation source	5
39	Effecting Efficiency Effortlessly	Daniel Carden	There are always multiple ways of doing things in SAS to produce required outputs. Often the way that ensures the quickest program run time is the best, as this will save costs in time and money. However, efficient techniques have to be well understood and used correctly to avoid error. It is also crucial that comments explain how a technique has been used so that another programmer can know exactly what is going on. The aim of this paper is to effect efficiency effortle ssly, by using a number of techniques including SAS views, efficient code structuring, WHERE statements, use of a Skip Macro in place of commenting-out code and creating Format Libraries in order to save CPU time. These techniques can save hours of real time, and when properly understood, used and narrated by a programmer they can do so without impacting on the overall "understandability" of the code	view, data step, where statement, proc step, dataset, proc sql	source presentation source	7
40	Effective SAS greplay'ing and how to avoid stretching	David Mottershead	This paper resolves the issue of creat ing SAS greplay figures with common titles and footnotes without stretching the original figures and their text. Figures will be created using `PROC GPLOT`. The creation of t i t l e s a n d f o o t n o t e s u s ing the `GSLIDE` procedure will be explained. Some background of the `GREPLAY` procedure will be explained as well as the creation of custom templates. The importance of setting the goptions which control the si ze of figures will be discussed and how to calculate the required values of the options for the exact size of figure you want to create. The relationship between text size and figure size will be explained, in particular resolving how to have uniform text size within the combined figure. Example code will demonstrate how to create the combined figure using the `GREPLAY` procedure	proc greplay, proc step, proc gplot	source presentation source	11
41	Embedding equivalence - t-test results in Bland Altman Plots visualising rater reliability	Jim Groeneveld		sample, confidence interval	source presentation source	5
42	Ensuring Compliant and Consistent Data Mappings for SDTM-based Studies - an ICON Approach	Jennie McGuirk, Steven Thacker	This paper presents the various tools that are leveraged, in order to ensure compliant and consistent data mappings, across Study Data Tabulation Model (SDTM) based studies within ICON Clinical Research. An increasing number of companies are looking to convert legacy studies of various ages to fully CDISC compliant packages ahead of submission, therefore it is essential to have the means available to ensure that all of the studies are harmonized and compliant in an efficient and automated way to realize what can be tight timelines, utilizing limited resource. In addition to standard checks such as OpenCDISC and WebSDM, ICON Clinical Research's compliance and consistency toolkit also comprises of a series of checks based around a data warehouse structure that captures information from the annotated CRF, metadata and the transformed data itself	dataset, sdtm domain, annotated crf, label statement, sample, excel, data step, proc freq, clinical research, out=, response variable, proc step, cdisc	source presentation source	16
43	Estimation of relative potency in a 4-point parallel line assay	Stefano Vezzoli	For inhaled asthma medications, the current EMA guideli ne requires equivalence in respect of efficacy to be demonstrated also through a comparison bet ween drugs in terms of relative pot ency. Differently from the more common comparison of the magnitude of responses observed following administration of different products, the dose- response relationship is taken into account by this appr oach. In this paper, we discuss the rationale of a two-step analysis to estimate relative potency and its confidence limit s in a 4-point assay, when the same two dose levels of each product being compared are administered. This is the typical design for the comparison of different formulations of the same medication. The analysis is based on the assumption of a parallel line assay, i.e. linear and parallel dose- response curves on log-dose scale. The development of a SAS macro to handle this analysis in both parallel group and cross-over designs will also be described	dose, proc mixed, confidence interval, interaction effect, degrees of freedom, fixed effects, confidence level, significance level, covariance, parallel group	source presentation source	11
44	Extension of the Six Steps Approach: How to go from an SDTM finding domain to an ADaM-compliant AD	Carl Herremans, Qian Wang		bds, adam dataset, change from baseline, sdtm domain, cdisc	source poster source	5
45	FDA CDER Common Data Standards Issues: Evolution of SDTM Submission Standards	Peter Van Reusel, Tina Apers		sdtm domain, clinical database, trial elements, cdisc	source presentation source	3
46	Genericity - New Metadata Concepts Applied to SAS Macro Programming	Wolf-Dieter Batz	Generic Programming is a coding technique that avoi ds or totally eliminates the use of explicit (hardc oded) information in the source code of a program. Instead, the program is equipped with some "search algorithm" or intelligence to make it find the required information itself from the runtime environment. The presentation will shortly introduce the ideas behind generic programming and then show or demonstrate live an application using these concepts. Special emphasis will be made to an implicit metadata structure referred to as RSDS (Report Specific Data Structure), which is highly efficient in reducing the number of parameters to be passed for a macro call	macro variable, macro facility, macro definition	source	6
47	Go Compare: Flagging up some underused options in PROC COMPARE	Michael Auld	A brief overview of the features of `PROC COMPARE`, together with a demonstration of a practical example of this procedure's output data set. Identify observations that have changed during the lifetime of a study when different cuts have been made to varying snapshots of the same database, then create flags from this metadata that can be used in safety update CSR listings	proc compare, proc step, by group processing, numeric variable, create table, pharmaceutical industry	source presentation source	13
48	How do we ensure the programmer we have today will meet the business needs of tomorrow?	Christine Leigh, Karen Rowe		clinical research, pharmaceutical industry	source presentation source	8
49	How personalities and motivations impact IT projects	David Smith	Presentation		source	19
50	How to create a custom style	Sonia Extremera, Antonio Nieto	SAS provide us with a wide range of def ault styles, however if you cannot fi nd the proper style to satisfy your necessities or if you need to make your SAS reports coincide with your corporate style, you can do it easily using the SAS `TEMPLATE` procedure. `PROC TEMPLATE` styles give you the power to customize the l ook of your reports, changing fonts, borders, colors, setting margins, or even adding your corporate logo. In this paper we will walk through the `TEMPLATE` procedure showing, step by step, how to redefine the default style elements and attributes to custom ize fonts, margins or colors to suit your company style. Also, we will show how to save your new style in the sasuser library or how to s h a r e i t u s i n g t h e O D S P A TH statement. All options and statements shown in the paper apply to `ODS` destinations, such as `ODS RTF` or `ODS PDF`	proc template, proc step	source presentation source	7
51	Implementation of a CDISC standards supported by global mapping process and metadata library	Dimitri Kutsenko		cdisc	source presentation source	5
52	Implementing ADaM Using the SAS Clinical Standards Toolkit	Gene Lightfoot	This paper introduces the steps taken to add a new data standard, in this case ADaM 2.1 , into SAS Clinical Standards Toolkit. By following the steps and processes defined here, users of the SAS Clinical Standards Toolkit to will be able to implement their own data standard	data cleaning, sample, data standard, adamig, macro variable, proc sql, proc step, adverse events, create table, macro definition, sas base, cdisc	source presentation source	12
53	Introduction of Pharmaco-economics for non-health economists	Elhem Sbaa	It is becoming increasingly important to examine the relationship between the clinical trial outcomes and the costs of the drugs. Safety, efficacy and quality are no longer enough in a world with scarce resources, where budgets are limited and needs for healthcare are unlimited. Allocate the healthcare budgets to those interventions that offer the most health gain per unit of money became important. The Health Technology Assessment (HTA) helps answer to this question. The HTA approach is a multidisciplinary and comprehensive evaluation methodology that is worldwide increasingly applied in order to support decision makers during t he introduction of new health technologies. Given the differences in health care systems and social services among countries, there is a need for country -specific data. The aim of this communication is to introduce health economics (HE) and HTA to non-economist participants	proc hpsplit, clinical trial	source presentation source	4
54	JMP Clinical for the Expl oration of Legacy studies	Robert Eric Huisden		clinical trial, relative risk	source poster source	4
55	Know Your Sources - Decoding Inputs/Outputs of any SAS Program	Sandeep Juneja	One of the biggest challenges in SAS Pr ogramming is that there is no standard way to gener ate Traceability Matrix for SAS programs with their inputs and outputs. As time progresses programs are updated and there is no way to do impact analysis for these updates on exis ting programs. This paper talks about how to generate Traceability Matrix for any SAS program along with their inputs / output s and their version numbers in SAS Drug Development Environment (SDD). It decodes the JobLog XML files (.jlgxml) and generates report which can help to carry out impact analysis for any updates to existing source code. The Challenge There is no standard way to tell what are the various input / output datasets and files in a given SAS program. The problem arises when one of the source program (for e.g. SAS macro) used as an input to other source programs is updated and it becomes challenge to find out what kind of impact these updates can have on other programs	dataset, clinical research	source presentation source	5
56	Learning from critical values - adverse event identification and classification	Milena Balcerzak	Patient safety plays a crucial role in the health care industry. Information about serious adverse events comes from multiple assessments and randomized clinical trials are not optimal for detecting such rare and unexpected events. Meta-analysis of heterogeneous trial data is quite complex and the medical errors made at this stage can result in disability, decreased quality of life, or even death. Such potential negative outcomes emphasize the need for a medical decision support system, which could detect, classify and even predict adverse events. The semantic and neural-networks methods are becoming standard in most other professional industries. Why not have these artificial intelligence learning methods in place in the clinical laboratory? In one particular study, such methods were used to pilot an early detection system of unexpected patterns of occurrences of laboratory values	adverse events, neural network	source poster source	10
57	Learning to love the SAS LAG function	Herman Ament, Paul Vervuren	The LAG and DIF functions (introduced in the SAS 1979 edition) are often used to get access to the previous value of a variable in a `DATA STEP`. For each defined LAG and DIF function in a `DATA STEP` a separate queue is maintained. Because programmers often don''t realize this, sometimes the result of these functions are unexpected. When the LAG and DIF functions are well understood, they are powerful tools in SAS programming	lag function, dif function	source presentation source	6
58	Managing data issues identified during programming	Shafi Chowdhury, Aminual Islam	Managing data issues identified during programming of analy sis datasets, tables, listings or figures is a major headache for both Programmers and Data Managers. The issue s have to be listed so the Programmer is aware of them, and they have to be sent to the Data Manager so that the issue can be resolved. The problem develops when the program is re-submitted over time. The Programmer is then no longer aware of what the new issues are and which have already been sent to the Data M anager. The Data Manager suffers equally from receiving the same data issues many times in various e-mails, and they then have to check what the new issues are and which have already been queried. Our solution is to simplify and automate the current process so that the issues are not sent to the Data Manager, but they are shared with the Data Manager, and to ensure that the same issues are not raised more than once. This centralization helps to manage the issues raised during programming	excel, adam dataset	source presentation source	5
59	Managing Telecommuters and Global Virtual Teams	Syamala Schoemperlen	With the globalization of m ost industries in recent years, telecommuting has already taken place within m ultiple company offices operating worldwide. The increased popularity of telecommuting is due to a combination of factors such as, the expansion of high-speed and wireless internet access, the desire o f c l i mate-friendly com panies to minimize work com mutes, rising fuel costs, and a growi ng realization among managers that productive employees require a h ealthy wo rk-life b alance	clinical research, problem solving	source presentation source	6
60	Many too many, too many performance tests	Christoph Baumer	There are many known tricks for speeding up the execution of our programs. However due to the complexities of SAS we often get surprising results. Performance testing is often the only way to figure out the fastest way of solving a problem. I will introduce a modular and flexible approach of performance testing. Some programs and algorithms perform well on a small number of observations, but slow down considerably when the number of observations gets higher, for example in megatrials. The performance testing program handles different scenarios, such as running all programs in different orders, running programs in a single batch job or running the program multiple times. The testing program will consolidate the test results in a single dataset, which provides a method to decide a suitable approach before the actual data arrives. An example of different ""many to many"" joins is provided to illustrate the performance testing procedure	create table, batch job, inner join, proc sql, macro variable, proc step	source	13
61	Migration + upgrade = happy users but how many days downtime?	Nigel Montgomery, David J. Garbutt	In 2009, Novartis started the 'GPSII upgrade' project which included SAS migration from v 8.2 to v 9.2, version controlled system (ClearCase ) upgrade and server reloca- tion and virtualization. This affected approximately 900 users from within Statistics, Programming, Data Management, Database Programming and CRF Development. This paper discusses how we determined some key differences between SAS v8.2 and v 9.2 and additionally how we managed the scheduling and migration of approx. 600 projects including approx. 3 TB of SAS data. We will share the SAS v9.2 differences from v 8.2 we found during this project, along with other key findings throughout the project. The way that can be told is not the true way Lao Tzu, Tao Te Ching	proc step, proc lifetest, %if-%then-%else, data step	source presentation source	42
62	Missing Values in SAS	Magnus Mengelbier	Missing values within Life Sciences can be an interesting exercise, not just how they are used in programming and analysis but also how they are used as a tool to code and store data. Whether the value is not applicable, below a level of quantification or just simply missing, is sometimes not trivial. SAS treats missing values as a very valid value and provides powerful functions that enable great control over their processing	missing value, cpu time, data step, character variable, numeric variable, options statement, call missing, sas function	source presentation source	11
63	Modifying Results of proc glm with proc template	Catalina Mejia Herrera, Volker Harm	This paper will present the graphical capabilities of `PROC GLM`, an overview of the `PROC FORMAT` with th e PICTURE statement will be introduced and specific remarks and advice will be given when dealing with the powerful tool of `PROC TEMPLATE`. This is presented by way of an example of an adapted study report	proc step, proc template, proc glm, proc genmod, numeric variable, significance level, anova, output delivery system	source poster source	7
64	Modular Programming - Some Lessons Learned and Benefits Gained	Ross Farrugia	By encouraging programming teams to forward think and plan ahead time savings can be found in the long run. Modular Programming is a programming technique that enables this; beginning from thorough requirement analysis, and the simple idea of breaking programs down into clear separate modules of code. We will then further look into the greater benefits that can be available of re-usability of code at a higher level, as well as the increased ease of validation this can provide. Tips, benefits and considerations will be given on this technique already used widely in software design	adverse events, adam dataset, macro loop, macro parameter	source presentation source	6
65	Much ADaM about Nothing - a PROC Away in a Day	Endri Endri, Rowland Hale	CDISC is rapidly becoming adopted as the data standard for clinical trials and submissions of clinical trial data to the FDA. Within CDISC, ADaM datasets are an integral pa rt of clinical study analysis and require significa nt data derivation to fulfill the needs of TLF provision. Can the creation of ADaM datasets be automated? Yes! This paper proposes an `EXCEL` driven solution which greatly improves the efficiency and accuracy of ADaM dataset creation by defining ADaM dataset structure, complex variable derivation and data checks within `EXCEL`. Using a library of SAS macros, the definitions then drive the automated production of SAS scripts which produce analysis ready datasets that meet the ADaM specification and which can be v alidated in accordance with regulatory requirements . Additionally, data check reports are created for data management to speed up the data cleaning process	adam dataset, adamig, clinical trial, excel, clinical data, data cleaning, key, macro definition, cdisc	source presentation source	8
66	New Starter Models for Pharmaceutical companies and Clinical Research Organisations (CROs)	Lovemore Gakava	The paper compares and contrasts New Starter Models used by Pharmaceutical (Pharma) companies and Clinical Research Organisations (CROs). New starter refers to recent graduates, students on internship or someone who has changed careers and joined the pharmaceutical indus try as a `SAS/STATISTICAL` programmer from a differe nt environment such as finance or marketing. The advantages and disadvantages of the models in both environments are discussed. Finally the paper ends by identifyin g improvements and alignment of the models in both environments	clinical research, clinical trial, pharmaceutical industry, sas programmer	source presentation source	4
67	Numeric precision and representation issues. Why 4.8 - 4.6 is not always equal to 0.2.	Nicola Tambascia	Numeric precision and representation issues are well-known topics in computer science. Non computer scientists sometimes are not aware of the problems that occur with these issues. This paper provides a brief introduction to problems based on numeric precision and the presentation of numbers on computerized systems. Numbers are stored as binary numbers in computerized systems. However, not all floating-point numbers can be represented properly in the binary system. When calculating with numbers that cannot be stored exactly, the result may not be as expected. In our case 4.8 and 4.6 can not be stored exactly and therefore the result of 4.8 minus 4.6 will not be exactly 0.2. The conversion of floating-point numbers to a storable IEEE-754 binary number is illustrated. Finally some SAS functions and options are introduced that may help to deal with the issue	proc compare, proc step, numeric functions	source poster source	11
68	Preparing an eSubmission based on multiple trials, some of which are ongoing - challenges for statistical programmers	Asa Carlsheimer	Preparing for an eSubmission based on multiple trials, some of which are ongoing is, a challenging task for statistical programmers. Having ADaM standards in place and maintaining a pooled data repository will facilitate the submission work on the data side. However, generating more than 1,100 tables and figures for the Integrated Summary of Safety and Efficacy, involving a large team of biostatisticians and statistical programmers (in-house and off-shore), requires careful planning that includes preliminary discussions on consistency across programs. The purpose of this presentation is to share experiences and provide some advice for statistical programmers to consider in future submission work	data cleaning, adverse events	source presentation source	7
69	Programming for Early and Late Phase - really that different?	Katherine Macey	This presentation will compare and contrast the statistical programming needs of phase I/II studies (early development) versus phase II/III studies (late development). I will start by comparing typical features of early and late SKDVHVWXGLHVDQGWKHUHVXOWLQJWHFKQLFDODQGµVRIW¶VNLOOVUHTXLUHGE\SURJUDPPHUVZRUNLQJRQWKRVHVWXGLHV)RU example, juggling multiple exploratory healthy volunteer studies with a fast turn-around vs. long-term planning and programming of large confirmatory phase III studies, may require a different mind - and skill-set. I will describe some examples of typical clinical trials as illu strations. I will then discuss the advantages and disadvantages of various ways of organising your programming teams; for example, having separate teams supporting early and late phase studies, having a single project team supporting all phases. I will illustrate with examples from Roche of how programming teams are structured	phase i, phase iii, clinical programming, adam dataset, adaptive design, phase ii, dose, efficacy summary, exploratory endpoint, statistical programmer, data cleaning, cdisc	source presentation source	6
70	Queries management: Interfacing SAS with in-house EDC application	Virginie Freytag, Michel Toussaint	Despite the growing use of EDC by pharmaceutical companies in clinical trials and the confirmed leverage of this technology, off-line edit checks for data validation are still widely used. SAS, mainly due to its flexibility, is therefore a n appealing alternative for their implementation. T he management of generated queries is, however, usually a high hurdl e to overcome, particularly in large multicenter st udies. Most EDC applications on the other hand offer full user friendly possibilities for manual queries management by the monitor. In this paper we will show how we successfully interfaced our SASworkflow for automatic queries generation with our in- house EDC application, thus enabling to combine the a d v a n t a g e s o f b o t h s i d e s f o r t h e e f f i c i e n t m a n a g e ment of off-line queries	data validation rules, clinical trial, edc	source poster source	5
71	Rapidly Retrieve Clinical Information across immense Data Sources: Introducing Reveal paper	John Leveille, Chris Decker	The vast and disparate nature of clinical data challenges our ability to search and analyze. Using Reveal, you can now answer precise questions gleaning data from spanning disparate sources in seconds rather than hours. Questions like: Where is GI Bleeding? What hypertension events were seen across this compound? Reveal's search technology eliminates the need for multiple manual, time-intensive searches through various clinical data systems and uniquely reaches sources previously impossible to search. It bridges the gap between data and analytic tools so users avoid spending time hunting for the information and, instead, focus on the analysis to answer key scientific questions		source poster source	4
72	Rebuilding define.pdf by reading Word and XFDF files	David J. Garbutt	Although the industry is transitioning to the CDISC define.xml to document SAS transport files submitted to the FDA there are still many studies to be submitted that were prepared with the old methods and for which documentation has already been created. Continuing with the old method is not viable because FDA now expects the define.pdf content to include derivation information for variables. This information is typically stored in the Statistical Analysis Plan. This presentation shows how SAS XML features were used to add this vital functionality to an existing tool building define.pdf. We use SAS XML maps to read CRF page location information for each variable from an annotated CRF (saved as XFDF). We also use an XML map to extract variable/dataset metadata from MS Word tables contained in pre-existing .doc files. The advantages of using LATEX to generate the define.pdf are also briefly discussed. *Todo list	sample, data science, txt, dataset, data step, cdisc	source poster source	23
73	Rectifying Irregular Text Data: a case for using Regular Expressions in SAS	Jayshree Garade, Manjusha Gode		clinical data, char.	source presentation source	2
74	Remapping of Codes (and of course Decodes) in Analysis Data Sets for Electronic Submissions	Joerg Guettner	For electronic submissions, the FDA requests, according to the Study Data Specifications (Version 1.5.1, January 2010), that variable names and codes in analysis data sets should be consistent across studies and, where feasible, the NCI CDISC Vocabulary shoul d be used. For integrated analysis it is also necessary to harmonize codes across all studies. Un fortunately during the life cycle of a product, codes may change either due to project needs or due to exte rnal circumstances, e.g. CDISC introduces new controlled terminology. For analysis data sets following the Analysis Data Model (ADaM) there are often pairs of corresponding variables containing a c ode and a decode which both have to be updated. This paper describes a workflow to remap codes and also decodes for a study or an integrated data base with a macro using metadata and standard SAS format data sets with a few additional variables	controlled terminology, format statement, cdisc	source presentation source	5
75	Research Project on Metadata Extraction, Exploration and Pooling: Challenges and Achievements	Ronald Steinhau, Dimitri Kutsenko			source presentation source	7
76	SAS Clinical Data Integration	Bill J. Gibson		clinical data, clinical trial	source	2
77	SAS Data Management Studio	David Smith		data cleaning, pharmaceutical industry, adverse events	source	4
78	SAS Data Set Utility with Excel Output	Rob Horton	There may be a requirement to check data sets creation dates are later than a certain point in time. A quick utility that allows for checking of file creation dates or to check that previously empty data sets are now populated may be of use in many situations. One such utility can be run by the programmer, an XML file is generated and a non-programmer can then check output without having to use SAS. There are options to run over multiple study areas, exclude a particular list of files and to change cell	excel, proc report, proc step	source presentation source	5
79	SAS Drug Development as a platform governing data standards specification and clinical data exchange between pharmaceutical organizations	Tina Apers		clinical research, data standard, data cleaning, csv, adverse events, clinical data, clinical trial	source presentation source	56
80	SAS Goes Spreadsheet	Raimund Strob	Since Dan Bricklin and Bob Frankston implemented the first WYSIWIG spreadsheet program with VisiCalc in 1979, spreadsheets have become a common tool for End-user development. Programmers face the challenges of implementing algorithms defined in spreadsheets and handling problems defined within them. One of the major issues programmers face is that both the data and the algorithms in a spreadsheet are not sequentially defined. Also the definition within a spreadsheet may apply to special parts, like different BY groups, within a SAS data set. One possible solution to these issues might be to organize the data in a DoW-Loop (Whitlock DO Loop) and define the algorithm within this structure. This paper explores how to define certain operations within a spreadsheet, for example how to copy a formula or how to define the dimension of the array(s) defining the spreadsheet and which of these tasks can be predefined with a SAS macro	macro definition, macro variable, by group processing, proc sql	source presentation source	14
81	Scalable Vector Graphics in SAS 9.2	Nicola Tambascia, Sven Greiner	For more than ten years, Scalable Vector Graphics (SVG) has been a standard for displaying vector graphics on the internet. For the first time, the SAS 9.2 software supports the creation of graphics in SVG format. This document provides a brief introduction to the XML-based SVG standard and to the creation of SVG graphs using `SAS/GRAPH` software. Firstly, the concept of vector graphics and its technical implementation as scalable vector graphics are explained. Secondly, a number of examples are presented to illustrate the specific features of SVG using the SAS software. Thirdly, the advantages and disadvantages of SVG as a graphics format in the analysis and reporting of clinical trial data are discussed	ods html, ods pdf, proc gchart, bar chart, proc step	source presentation source	9
82	SDTM Domain QS: Challenges and Pitfalls - an Interpretation Guide to the Implementation Guide	Knut Mueller	In Clinical trials, patient reported outcomes (PROs) are widely used to support label claims or even to be part of the primary endpoint. According to the SDTM implementation guide, PROs are destined to be stored in the Questionnaires (QS) domain. The QS domain is one of the most complex domains as it has to capture a huge variety of different questionnaires. It is defined in the respective section of the SDTM implementation guide. However there is room for interpretation especially with regard to terminology and the way coded or derived variables are to be stored. A sponsor defined SDTM interpretation guide helps to ensure consistency across projects and studies. This paper describes the challenges and pitfalls of setting up the QS domain for SDTM and the implications for setting up respective ADaM datasets for PRO data	adam dataset, sdtmig, sdtm domain, data science, imputation, clinical trial, raw data, adamig, cdisc	source presentation source	6
83	SDTM Implementation Guide - Clear as Mud: Strategies for Developing Consistent Company Standards	Brian Mabe	Many pharmaceutical companies are now entrenched in various CDISC models with SDTM being the most defined. There is even a very detailed implem entation guide. One may think, "How hard can it be implementing this?" However, there are many references to sponsor defined fields for many important variables within SDTM. Additionally, how can one report in standard domains using different study models (Doubl e Blind vs. Open Label, Phase 1 vs. Phase 4, etc.). The more one gains experience with SDTM the more apparent t h a t t h e r e a r e m a n y d i f f e r e n t philosophies for defining the same type of variable. The aim of this paper is to explain the difficulties in defining these vague sponsor-defined terms while being compliant and ensuring consistency across all studies. To achieve this, one of the most efficient methods is to have a company interpretation guide for SDTM that offers solutions to these issues in a consistent manner	sdtmig, sdtm domain, raw data, controlled terminology, cdisc	source presentation source	5
84	Seven sharp surgery tips for Clinical Programmers	Rohit Banga, David J. Garbutt	Clinical programmers face a variety of challenging tasks daily. For many of these tasks we know that there are numerous ways to achieve desired results using SAS. This paper highlights some of the useful techniques that we have learned while working with SAS and illustrates each snippet with real examples and code. We aim to make a collection use- ful to beginners and experts and hope everyone will learn something useful or at the very least be reminded of a technique they have forgotten. The sharp tips we will reveal include using formats to replace left joins, using hash ob- jects to perform table lookups and joins, displaying dates differently with Picture Formats, an unexpected behavior when using a `MERGE` statement, a simpler to set up and less error prone DO OVER loop, a `FCMP` Procedure example and importing XML files using SAS Unicode server	hash table, proc step, proc fcmp, lookup, data step, proc sql, do over	source presentation source	15
85	Sharing SAS programs between PC Server and SAS Drug Development	Magnus Mengelbier		clinical research, macro variable, macro call, libname statement, sas library	source presentation source	7
86	Simple Version Control of SAS Programs and SAS Data Sets	Magnus Mengelbier	Poster: SAS data sets and programs that reside on the local network are most often stored using a simple file system with no capability of version control, audit trail of changes and all the benefits. We consider the possibility to capitalise on the capabilities of Subversion and other simple straightforward conventions to provide version control and an audit trail for SAS data sets, standard macro libraries and programs without changing the SAS environment		source	1
87	Some New Features in SAS 9.3 (Overview)	Daniel Christen	Presentation	sas base, proc step, graph template language, web service, sas management console, sas grid, macro put	source	43
88	Sponsor-FSP collaboration: an adaptive design for enduring success	Manjusha Gode, Claudia Hammer	Presentation	service provider	source	15
89	Stacking Rich Text Format (RTF) %SRiT	Duong Tran	Reporting in Rich Text Format (RTF) is gaining more and more popular and as programmers gain more experience on this topic, RTF is set to be a mainstream reporting format. In the paper %RiTEN[1], the author discussed the techniques for framing cells, merging cells and tables stacking and how the implementation of these techniques gives it an immense powerful tool for reporting. %SRiT (Stack Rich Text) in addition to these techniques gives another level of flexibility to reporting in RTF. %SRiT is a SAS macro that stacks RTF files. Its key objective is to stack the input RTF files but keep the originality of each individual file and have an option to reset the page counting. Therefore one can either collate the RTF files into a single report or a deliverable document, typically for review. Keywords: RTF (Rich Text Format)	ods rtf, macro definition	source poster source	4
90	State of the union: what does the future hold for statistical programming in Europe	Leo Valmas	Presentation	sas programming, clinical trial, clinical research, data science, data transformation, adaptive design, pharma programming	source	28
91	Study Composer: a CRF design tool enabling the re-use of CDISC define.xml metadata	Philippe Verplancke		data cleaning, edc, excel, data validation rules, clinical data, clinical data management, annotated crf, clinical trial, data governance, sdtm domain, cdisc	source presentation source	5
92	The Evolution of Coding Technology	Richard Davies			source presentation source	4
93	The Evolution of Coding Technology - Demonstration	Richard Davies			source presentation source	2
94	The Nycomed outsourcing model - Data standardisation experience after three years	Berthold Traub	Presentation	clinical data, clinical trial	source	25
95	Three Unique Case Studies - On the Trail of the Holy Grail of SDTM Implementation	Kenneth Stoltzfus	While it's clear that a critical mass has been reached with a high percentage of pharmaceutical companies engaged in the implementation of SDTM standards into their organizations, what is not so clear is the best path to follow to get there. From my standards experience on both sides of the pharmaceutical industry - pharmaceutical companies and contract research organizations - it is apparent that there is no "golden egg" or "magic bullet" that ensures a successful SDTM implementation. But it is my objective here to share that experience and insight into 3 situations that have been productive. Included here are 3 unique case studies of individual companies working hard to implement SDTM, described briefly at a strategic level and then in more depth at an operational level. Not only the mapping result will be shown here, but also the decision-making process that ultimately determined the right mapping for that organization	medical history, sdtm domain, protocol deviations, concomitant medications, adverse events, clinical trial, cdisc	source presentation source	6
96	Tracing data elements through a standard data flow	Alistair Dootson	Ever wondered where a data point came from? Ever been looking through some TFLs and thought "I wonder how this number came to be here?" Chances are you are wondering about the traceability through your data collection, management and reporting process. Where do you start tracing the data? Where do you finish? How am I going to trace this data effectively? This paper looks into the "breadcrumb trail" through the data management and reporting cycle of clinical trials. Is there a simple solution out there to manage the traceability of the data in your trial? What will you do when the FDA come knocking and want to see the links through the steps in your process? These questions will soon be answered	sas data integration studio, raw data, proc sql	source presentation source	13
97	Training Statistical Programmers on SAP Review Skills	Sascha Ahrweiler	A statistical analysis plan (SAP) describes the planned analysis for a clinical trial. SAPs must be carefully reviewed by statistical programmers working on the project for clarity and comprehension in order to construct analysis data sets and prepare planned Tables, Figures, and Listings. SAP review is a challenging task that requires both statistical expertise and abstract thinking skills, and is often times completed in the absence of available trial data. Statistical programmers must also develop critical evaluation and communications skills to prospectively identify issues and seek clarity with statistical colleagues on planned analyses on a trial or a development program. Statistical programmer education and training is highly variable and is derived from multiple disciplines, suggesting that comprehensive training on SAP review is an essential skill to ensure consistency and quality within the project	statistical programmer, data science, clinical trial, clinical programming, proc means, anova, imputation, analysis set, proc freq, interim analysis	source presentation source	7
98	Translating eClinical Data - The Art and Science of Data	Nikki Dowlman	With the increasing complexity of studies in their design, technologies employed and sheer size, it is more critical than ever to quickly assimilate large amounts of data to facilitate the conduct of a study, identify action areas, and share best practices. As a consequence, many sponsors are seeking to amalgamate data in a warehouse to minimise the hunt for data in disparate places. This paper will explore best practice methodologies that can be employed to ensure intuitive and informative display of data, as well as processes for ensuring useful m etrics are identified. We will review the Gestalt P rinciples of visual perception and the importance of User Interface (UI) design for data display. Case studies of both good and bad display of information from other industries will be used to provide illustrative discussion points. These principles will then be applied to the data in the clinical trial industry	clinical trial, bar chart	source presentation source	6
99	Unit Testing as a Cornerstone of SAS Application Development	Dante Di Tommaso		macro definition, data cleaning, macro variable	source presentation source	6
100	Use of early longitudinal viral load as a surrogate to the virologic endpoint in Hepatitis C	Igwebuike Enweonye	In the management of Hepatitis C Virus (HCV) infection, there is a great interest in early monitoring of viral load since early emergence of resistant strains can jeopardize the treatment outcome. In this paper we present an ongoing research to address the question whether early longitudinal viral loads can be used as a surrogate in discriminating responders / non-responders at the end of the trial. The data analyzed in this paper are from a completed, randomized, phase II, clinical study, exploring the effect of a certain protease inhibitor (PI) therapy in combination with the standard of care on efficacy, safety, tolerability, PK and PK/PD of the therapy. We propose a semi-parametric mixed effect model which is used to model the longitudinal sequence up to week 12 of the subjects. Based on subject specific parameters o btained from the semi-parametric model we estimated the probability to be a responder at the end of the trial	proc mixed, covariance, random intercept, covariance structure, irregular component, sas output, treatment period, logistic distribution, logistic regression	source presentation source	12
101	Use of ODS tagsets.excelxp to create Excel type files	Douglas Staddon	`EXCEL` files are widely understood in many departments within an organization so if you can create them easily this should aid communication and data quality. This paper will show how to use `ODS` `TAGSETS`.excelxp to create XML files that can be opened in `EXCEL` just like a normal spreadsheet. Case study data from the CDISC pilot study has been used to demonstrate the benefits and problems encountered. An XML file was created for each patient in the study with separate tabs per raw dataset and also a file of the whole database	excel, macro variable, proc step, cdisc	source presentation source	9
102	Use Your Favourite Program Editor	Duong Tran	The SAS Enhanced Editor (EE) and the Editor in Enterprise Guide (EG) 4.3 are significant improvements over the SAS Traditional Editor (TE). But for programmers, who work with Non-Windows Operating System (OS) like UNIX or do not have EG, then TE, NEDIT, EMACS etc are the typical choices of editors. This paper discusses a simple technique that allows you to use any Alternative Editors (AEs) as if they were integrated into the SAS Application and how to submit SAS codes interactively from these AEs. Keywords: Gsubmit, UltraEdit	sas enterprise guide, sample	source poster source	3
103	Using Dynamic Lists in SAS Stored Processes for Genetic Toxicity Historical Control Data	Volker Harm	In standard tests for genetic toxicity historical control data are used to assess the validity of the experiments as well as additional information for the statistical analysis of the tests. Thus compilation, management and reporting of this data gain ever more importance. Existing SAS Stored Processes can be significantly enhanced by the use of dynamic lists to cope with an increasing number of test types, types of controls, and laboratories. The paper will introduce the data that is handled, show how the application is built up, describe the use of dynamic lists in SAS Stored Processes and explain how the users benefit from this new flexibility	dataset, sas management console	source presentation source	11
104	Using Microsoft Excel to Write SAS Code	Andrew Boyd		attrib statement, macro call, excel, macro definition	source presentation source	7
105	Using Microsoft Excel to Write SAS Code	Andrew Boyd		attrib statement, macro call, excel, macro definition	source poster source	7
106	Using R to Validate Results of Other Programming Environments	Nikhil Abhyankar, Vidyagouri Prayag	During the course of statistical software development, validation of algorithms is a crucial activity which ascertains the accuracy and quality of the numbers they produce. The open source R software can be used extensively in achieving this goal. The set of validation tests provided by the Comprehensive R Archive Network (CRAN)1 and the growing community of R enthusiast statisticians and computer scientists make it possible to use R for this purpose. The open source nature and the various contributed packages facilitate its use for testing advanced theories, especially when there are no other products available for comparison of first-to-market products. In the clinical data analysis industry, SAS is the preferred software for statistical analyses. However, R can be used for independent and reliable validation of outputs ranging from subject listings to advanced analyses	data cleaning, data step, data science, clinical trial, statistical software, missing value, proc sort, proc step, proc means, excel, clinical data, proc mixed	source presentation source	8
107	Using the SAS Clinical Standards Toolkit to work with the CDISC ODM model	Lex Jansen	The CDISC Operational Data Model (ODM) is a vendor neutral, platform independent format for the i nterchange and archival of clinical study data. The model represents study metadata, administrative metadata, reference data and subject data associated with a clinical trial. The ODM format is defined by an XML schema and a specification. The SAS Clinical Standards Toolkit (CST) is a framework of SAS macros, metadata and configuration files to support clinical research activities. The CDISC ODM standard is one of the standards that are supported by CST. To enable ODM support, SAS has defined a relational data model that represents the ODM model as SAS data sets. This paper shows how the SAS Clinical Standards Toolkit supports: x Reading and writing of ODM files	dataset, data cleaning, sample, clinical research, format statement, macro variable, imputation, proc step, clinical trial, cdisc	source presentation source	20
108	What makes a strong programming team?	Beate Hientzsch	Presentation	clinical programming	source	22
109	Why Should You Be Using the New SG (Statistical Graphics) Procedures in SAS 9.2?	Philip R. Holland	!"# $%&' $(&$%&'$!)($%&''!%$%&'%)+%	proc step, proc sgpanel	source presentation source	10
110	Writing Parallel Processing Compatible Engines Using OpenMP	Aniruddha Deshmukh	Software for sophisticated statistical procedures usually involves compute -intensive algorithms and large amounts of data. Numerical methods, optimization and simulation requirements all involve massive amount of computing. One way to dramatically improve runtime performance is to exploit multiple CPUs now commonplace even on desktop computers. OpenMP (Open Multi-Processing) is an open standard for portable and scalable parallel programming with shared memory and distributed shared memory multiprocessors. It provides an API for parallel programming in C/C++ and	parallel processing, random number generation	source presentation source	8
111	Writing standard programs your people will use and love!	Jean-Marc Ferran		data cleaning, clinical research	source presentation source	8

Back to Conf go 2012 go 2010

This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.

The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.

SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.