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PHUSE EU 2007 Papers - Index

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

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1 %RiTEN Duong Tran For years SAS programmers within the Pharmaceutical industry have been searching for answers to produce tables and listings for clinical study reports to MS Word format. There have been numerous techniques[3] but all these, though innovative, either less convincing or requires considerable programming that they have not caught on. The advent of the ODS RTF (Rich Text Format) destination beginning SAS v8.2, in my opinion has and will make these techniques and alike obsolete. The purposes of this paper are to: 1) Introduce the %RiTEN (Rich Text Enhancer) utility and thus hope to inspire you to fully move forward to the next generation of reporting formats with the ODS. 2) Highlight the three KEY functions (cells framing, cells merging and tables stacking) that the PROC REPORT ODS RTF destination still lacks proc report, proc step source 6
2 A dedicated programming group in a pharma M&S organization Vincent Buchheit clinical research, data science source 8
3 A gentle introduction to Meta-analysis Angelo Tinazzi,
Jayne Tierney		 Meta-analysis (MA) to combine the results of rela ted randomised controlled tria ls, has become a common and widely accepted tool in the evaluation of health care interventions. Regulat ory agencies, such as the FDA, have started considering it as key supporting evidence. MA can be based on aggregate data extrac ted from the reports of published trials only, aggregate data collected from all trials or centrally collected updated individual patient data (IPD) from all randomized trials. This paper is designed to introduce the main aspects of planning and conducting a MA ; the main statistical measures will be introduced for the di fferent kind of outcomes (dichotomous , continuous and time-to-event). An overview of the main features of the statistical software supporting MA will be also reported proc mixed, treatment effect, confidence interval, forest plot, proc means, statistical software source 8
4 A SAS based solution for define.xml Monika Kawohl When s ubmitting data t o t he F DA, a data definition f i le, describing th e s t r u c t u re an d contents of the data, is a mandatory deliverable. For data submitted in t h e C D ISC SDTM format the defi ne.xml as published by t h e CDISC define.xml team is the preferred type of a data definition file. Compared to define.pdf, the previous data definition file format, define.xml is more suitable for providing the different types of metadata required to adequately describe data in the SDTM format. An additional benefit of define.xml is its machine-readability. After Accovion successfully implem ented an automated process for defi ne.pdf in S AS, building a SAS base d automation process for define.xml was the logical consequence. This paper illustrates the process chosen by Accovion to compile all the necessary metadata and build a define.xml as described in the CDISC Case Report Tabulation Data Definition Specification (define.xml) V1.0 Standard [1] sample, excel, annotated crf, data dictionary, sas base, sdtm domain, controlled terminology, proc step, dictionary tables, cdisc source 18
5 A SAS Macro for breaking pages Michael Knoessl Statistical analysis of clinical data is reported primarily as summary tables and listings. Many tables are specified as to display more rows than do fit on a single page. Such tables must be divided across two or more pages. However, breaking pages automatically is not a trivial task. The main problem is to identify the breakpoints, because it is the row (or group of rows) that shall be displayed on the page following the break, which identifies the row that shall be displayed last on the page preceding the page break. Determining the two rows between which a page break is to be introduced has to occur in the SAS dat aset that contains the result data. This paper describes the macro %PAGEBREAKFLAG, which creates a counter variable that consecutively counts the prospective pages of the output table, and adds this variable to result data containing SAS dataset in=, proc report, macro parameter, out=, options statement, proc step source 4
6 A systematic review of PHUSE Angelo Tinazzi,
Anna Compagnoni		 So far PhUSE has been an exciting initiative. European Phar maceutical Software Users now have a place where they can present and share their experience. In the first two PhUSE conferences (2005 and 2006) and in the current one (2007), 273 papers were presented (89 in the 2005, 85 in the 2006 and 99 in 2007) in the following areas: ƒ 51 posters ƒ 36 application and development ƒ 31 technical solutions ƒ 22 coder corners ƒ 18 coding solutions (2006 and 2007 stream) ƒ 17 Statistics & Pharmacokinetics ƒ 17 Tutorials ƒ 13 CDISC and XML (2005 stream) ƒ 13 Data Management ƒ 11 CDISC and Regulatory (2007 stream) ƒ 10 Professional Development & Management (2005 and 2006 stream) ƒ 10 Regulatory Compliance, Electronic Submission & Validation (2005 and 2007 stream) ƒ 7 SAS Starters (2005 and 2006 stream) ƒ 5 Management (2007 stream) ƒ 4 Industry Starters (2007 stream) ƒ 4 Professional Development and Training (2007 stream) ƒ 2 Microsoft Tool (2007 stream) ƒ 2 Oracle Clinical (2007 stream) The ult pharmaceutical industry, sas 9, cdisc source 5
7 Advanced RTF layout with SAS Katja Glass The Output Delivery System (ODS) from SAS 9 with ODS RTF offers a lot of possibilities to design rich text format (RTF) documents. With additional included RTF commands the layout possibility for designing RTF is nearly unlimited. The purpose of this paper is to point out the different ways of styling RTF with SAS. The focus is put on the advantages of direct RTF commands to create for example auto numbe ring and a different kind of table of contents. Furthermore, general methods are described how new RTF commands can be found to enrich the output proc report, proc step source 9
8 All roads lead to SDTM - which one shall we take? Ian Fisher,
Donna Thompson		 sdtm domain, raw data, data standard, phase i, sdtm programming, clinical trial, data cleaning, cdisc source 9
9 An animated guide: The Amazing SQL Optimizer... Russell M. Lavery proc sql, where statement, hash table, proc step, having source 12
10 Anticipating user issues with macros Lawrence Heaton-Wright How can you stop users asking you questions like: "What macros are available?" "Why doesn't this macro work?" "How do I stop this macro doing that?" There are lots of techniques available to SAS macro developers such as providing a help option, checking that the input values of a macro parameter are correct, supplying useful warning and error messages and creating, maintaining and documenting a code library. The aim of this poster is to provide some ideas to help macro developers to help their users help themselves macro variable, error message, macro parameter, sas log, macro loop, macro call, %macro parameters, macro definition, macro put, macro function source 7
11 Application of the ARRAY statement to create ESTIMATE and CONTRAST statements in statistical PROCEDURES: a case study Roel Straetemans CONTRAST and ESTIMATE statements are important tools when performing statistical analyses. For a statistician the creation of the correct code can however be cumbersome when dealing with many variables and/or different levels within variables. This paper will illustrate how the ARRAY statement can be used to circumvent these difficulties by letting SAS create the correct and necessary code array, data science, main effect, proc genmod source 10
12 Architecting SASin a Modern World.. Gregory S. Barnes Nelson For decades, SAS has been relegated to the world of the mystical, the unknown and perhaps even superstitious. Configuration options, switches and toggles all in conspicuous or perhaps precarious positions, waiting for the day when an expert can take a closer look-or worse, when something breaks. This paper will show how SAS can be architected with modern Windows and UNIX systems solutions. While current users of SAS 9 can continue to use the software as they (or their fathers) always have, SAS 9 now offers new possibilities for how to use its technology more efficiently as well as how to better manage infrastructure. We will explore the hardware side of SAS by examining disk options, I/O configurations, backup as well as understanding how powerful multi-way (and multi-core) systems can ease your SAS processing with optimal configurations between processors and memory sas 9, sas programmer source 15
13 ARROW statistical graphical system Ann Cuijpers,
Raphael Noirfalise		 The ARROW Statistical Graphic System is an in house system used for the creation of graphical displays of clinical study data. The strength of the system is that through these graphs the results can be communicated in a clear and efficient way while the system itself is very flexible and user-friendly. Depending on users input, the system can produce different types of displays, including mean plots, vertical/horizontal bar charts, box plots, line plots and dot plots . Statistical descriptive like means and standard deviation are derived directly from SAS procedures and then implemented for the graphic display. There are keyword parameters available as user interface for controlling titles, head notes, footnotes, layout, colours and axes layouts. At the same time, the output can be produced in 5 different file formats allowing the graphs to be created in the most appropriate format for different applications (printing, incorporation in Word document ) proc sgplot, bar chart, clinical trial, data visualization source 6
14 Automating GREPLAY to display multiple graphs per page KarolAnne Fitzpatrick,
Erika Daly		 proc greplay, proc step, phase i source 9
15 Bayesian Modelling with SPLUS and the S+flex Bayes library Andrew Jack,
Dawn Woodard		 Bayesian methods are used increasingly across all phases of the pharmaceutical research and development cycle. Examples include early-phase adaptive clinical trial design, population-pk modeling and analysis of safety data in all phases. These applications are driving the need for robust, flexible and novel Bayesian analytics, graphics and reporting software. This article and presentation reviews Insightful Corporation's approach to providing Bayes analysis software tools and solutions to support the needs of pharmaceutical development. In particular, the Insightful S-PLUS flexBayes library (S+flexBayes), in conjunction with the collaborative S-PLUS framework, provide statisticians and statistical programmers with a flexible and productive solution for Bayes modeling. Results from these analyses are readily deployed as interactive clinical review graphics or publication reports as part of the Insightful Clinical Review and Reporting Solutions adverse events, relative risk, sample, posterior probability, prior probability, system organ class, preferred term, clinical trial, bayesian statistics, markov chain, monte carlo, multiplicity, sample size, clinical data, clinical research source 11
16 Bootstrapping in a replicative bioequivalence study Feryal Tanriverdio,
Arne Ring		 For comparing the pharmacokinetic parameters of two form ulations of the same substance, bioequivalence studies are used. For these studies, the FDA recommends to analyse them using the Average Bioequivalence-method. For highly variable drugs, it is difficult to apply this met hod, as a large population size is needed to show that the confidence interval of the ratio of the average lies comple tely within the range of 80%- 125%. In one of our trials, we used the Scaled average bioequivalence-method. This method is technically more demanding, as the analy sed statistics cannot anymore be assumed to be normally distributed. Therefore we applied a bootstrap method. The aim of this paper is to show how the programming of th is study was implemented, focusing specifically on the bootstrap methodology. Problems faced while programming, concerning the processing time and data structure will be shown and the solutions that were found for these problems bootstrap, sample, confidence interval, proc sql, proc step, sample size, standard deviation, geomean, pk parameter, adpp source 11
17 Check for SAS files consistency Arnaud Dauchy 21 cfr part 11, adam dataset, sas log, raw data, txt, data cleaning source 29
18 Clinical data standards and automation Giri Balasubramanian data cleaning, analysis set source 11
19 Clinical Research Information Management... David Handelsman The clinical trials process generates mountains of information across users, trials, departments, divisions and companies. While various point solutions are deployed across research companies, there is growing recognition that a living clinical research repository that spans organizational boundaries provides significant benefit in bringing new therapies to market. Companies of all sizes can derive substantial business value across their organization, whether they rely on the growing repository as a collaborative workspace for their internal and external business units, or whether they build an accessible institutional memory to intelligently drive their business process forward. This paper will describe the issues and opportunities associated with deploying a clinical research information management system that not only tames, but leverages, the clinical information mountain clinical research, master data management, adverse events, data governance, clinical trial, access control, clinical database source 8
20 Coding Standards for the Un-Standardizable Richard Pugh,
John James		 source 7
21 Comparison of different ways using table lookups on huge tables Ralf Minkenberg In many application areas the problem to extract specific data records out of a huge master table based on key values and stored in a (smaller) lookup table is very common. A master table could for example contain common customer information in a bank data warehouse, or could contain basic demographic data about patients in a clinical study or project. Especially for mega trials or meta analysis these master tables could become really large. The classical solutions by way of sorting or indexing the data records are compared with other more efficient methods, which lead to the benefit of savings in time and memory. The methods presented alternatively on the one hand are using well-known SAS statements, on the other hand the new hash sort in SAS version 9 is explained as well which provides new powerful possibilities for a quick and efficient lookup lookup, proc sql, proc step, set statement, call execute, cpu time, proc format, hash table, put statement, inner join source 6
22 Compute; Your future with Proc Report Ian J Dixon,
Suzanne E Johnes		 PROC REPORT is widely used within the pharmaceutical industry to summarise and list clinical trial data. This paper will focus on the COMPUTE statement available within this SAS procedure and will highlight the wide- ranging use of this facility proc report, proc step, macro variable, adverse events source 11
23 Consideration of endogenous backgrounds in PK analyses Fritz Schindel Objective: The pharmacokinetic analysis of biologic compounds is frequently disturbed by the pres- ence of endogenous levels, which cannot be discerned from exogenous levels. The frequently used method of subtracting baseline levels from subsequent measurements was compared to a fully ad- justed regression model in a simulation study. Methods: Simulations (5,000 each) were carried out for a standard one-compartment model with rich (n=10) and poor (n=6) post dose sampling, using unweighted as well as two weighted types of non-linear regression. Results: Whereas the fully adjusted model performed properly across various scenarios, the sub- traction method showed a noteworthy bias (up to 14%) for AUC and elimination half-life with weighted non-linear regression. For estimation of the parameter Cmax using any weighting scheme, and of any parameter using unweighted non-linear regression, the two methods performed equally well regression analysis, proc reg, sample, numeric functions, homoscedasticity source 10
24 Considerations for CDISC Implementation Robert T. Stemplinger,
Jackie Lane		 Implementation of the CDISC standards presents a number of challenges that can have a deep and far reaching impact on an organization. To mitigate that impact and avoid wasted effort and exorbitant costs, careful consideration should be given to a handful of fundamental issues. Once these issues have been embraced a proper direction can be taken to meet the needs of the organization as they have been defined. This paper will detail some of the various challenges, propose possible solutions based on real life examples, and discuss technological options from SAS and other providers that can be applied to facilitate implementation sas programmer, sdtm domain, data cleaning, cdisc source 4
25 Creating Bar charts using the proc gplot and annotate function Berber Snoeijer The SAS system has a special procedure to produce bar charts. The PROC GCHART is an easy to u se procedure but it has certain limits in freedom. The position of the x -labels, standard deviation -bar setup and the bar width often cannot be specified precisely enough. Difficulties in formatting arise when the bar chart contains both negative and positive values. Rita Tsang (2004) gave a method to make bar plots by using PROC GPLOT and an annotate dataset. We have elaborated her method to be able to make more complicated box plots with finely tuned axes and standard deviation bars. In this paper, we wil l show how to setup the dataset, produce the annotate dataset and show the possibilities with our method proc step, bar chart, proc gplot, proc gchart, proc univariate source 4
26 Data mining in the clinical research environment David Smith Data mining has had wide adoption in recent years in many industries, largely because of the ability of mining techniques to rapidly yield answers to business questions in a short time and the availability of large quantities of data to exploit. This paper will discuss the topic of data and text mining in general, before focusing on applications in the clinical research field. Of particular interest is the application of mining techniques to signal detection for adverse events. The value of these techniques is discussed, along with the context in which data and text mining appear in the overall architecture of a SAS solution for pharmacovigilance data mining, neural network, sample, adverse events, clinical research, feature selection, regression analysis, life sciences source 7
27 Debugging Standard Macros Anthony Cooper HARP (Harmonisation of Analysis & Reporting Program) is GlaxoSmithKline's in-house developed global standard reporting system. The two main components of HARP are the web-based HARP Application and the suite of standard SAS macros, the HARP Reporting Tools. Recent reviews of both components highlighted the need for enhancements to help users when debugging the macros. The purpose of this paper is to describe some of the design principles of the HARP Application and Reporting Tools, to discuss the feedback from the reviews, and finally to cover the planned changes. It will also touch upon some of the debugging functionality available in SAS macro definition, sas log, macro debugging, macro variable, error source 5
28 Designing a Global SAS9 Architecture for analysis and reporting Andrew York,
Gregory S. Barnes Nelson		 sas 9, sas management console, clinical research, constraint, phase i, production run, sas enterprise guide source 26
29 Document stapling with C# Mark Vanderstay When multiple study outputs (Tables, Listings or Graphs) need to be reviewed by a study team, it is often b eneficial to collate those files into one Microsoft Word document. At Amgen this process is known as 'Stapling' and is an extremely laborious process when done manually. This paper looks at the development of an application that simplifies the stapling pr ocess. The requirements of the application will be outlined together with the adopted solution source 5
30 DQC checker Ryan Finch Before starting to create reporting objects it is important to be confident that the data has reached the quality level required. Years of experience have taught us that whatever the number of Data Management checks and Science data review cycles, the standardization of your CRF/eCRF and database, you will always find specific scenarios that may cause your programs to fail. This is why Roche has implemented a process to review key data before database lock to ensure that they match our expectations. This process is called the DQC "Data Quality Checker". This process is even more critical for Roche, than perhaps for other companies, due to the fact that the data is extracted by Clinical Programming (Data Management) based on a specification document written by Statistical Programming (derived data as well as raw CRF data). The DQC tool is used to perform the user acceptance tests of the extracted datasets to ensure they match the specifications and that derivations are correct clinical programming, data review, where statement, data validation rules, statistical programmer, study day, data step, adverse events, cdisc source 7
31 Electronic record validation Erik Leijskens,
Sini Marant		 According to the 21CFR Part 11 guidel ines of the FDA, a computer -generated, time -stamped audit trail should be provided for the electronic records of a clinical trial. Within our company the analysis data sets and output files were identified as electronic records and therefore all changes to these files after database lock need to be recorded. The Electronic Record Validation (ERV) process is a SAS -based solution that is established to track these changes and to create documented evidence about What was changed , Who made the change and When the change took place. In this way regulatory authorities or internal auditors can easily access the required documented evidence for quality assurance database lock, audit trail, statistical programmer, data cleaning source 2
32 Electronic submissions - the role of the programmer Simon Dundas It is essential that programmers and system developer s working on the generation of study tables understand how their work impacts the compilation of an Electronic Submission (ESUB) to FDA. Datasets are a major part of an NDA or supplemental filing - as important as study t ables. The datasets and the associated documentation must be accurate and user friendly. Processes for study table production should equally be aimed at producing suitable ESUB components as it is inefficient and difficult to retrospectively adjust datasets and documentation. Programmers need to be aware of the current FDA guidances and be involved with discussions as to what is to be provided in each Electronic Submission (ESUB). They should im agine what it''s like for the FDA review team, with tight timelines, trying to use the dataset s to reproduce analyses or perform new ones adam dataset, raw data, crf, cdisc source 8
33 First time in human reporting Dylan Browne source 4
34 From local lab to standards and beyond Angelo Tinazzi,
Irene Corradino		 Data Management of Laboratory Data, such as haematology and biochemistry, can be very time consuming, especially when data are collected in different local laboratories, all of them with differ ent normal ranges and units. This is the standard situation in oncology, where patients usually have their blood samples drawn outside the hospital, after being discharged or when therapy is administered at home. The present paper will review the full process of dealing with such data, and the application of a common grading system, the CTCAE grading system, will be also described laboratory data, clinical trial, phase i, adverse events, data governance, dose, sample source 10
35 Graph automation using integrated SAS/S-PLUS solution Qian Wang,
Carl Herremans		 Graphical display of clinical data and statistical analysis results is often required for clinical study reports, journal publications, and presentations to the regulatory agencies. Graphs can be created using "point-and-click" software packages, without programming efforts from the end-users. However, each time the graphic requirement changes, the plot has to be re-generated or revised manually. Besides, such process is too labor-intensive and time-consuming to handle large scale graphical needs for some studies. Alternatively, the plot generation can be automated through programming. But the traditional approaches demand substantial efforts and require considerable technical knowledge from the end-users. Ideally, a graphical tool should combine the ease-of-use from the "point-and-click" graphical software packages with the automated scalability from the programming approaches. This paper discusses one possible implementation of such a tool, the SPGRAPH SAS macro dataset, data science, clinical trial source 7
36 Having your cake & eating it: extracting data and attrib from OC John Hendrickx Extracting data from Oracle Clinical into SAS is quite straightforward but getting the variable attributes - labels, formats, etc. - is not. This paper discusses two SAS macros for extracting the data together with the variable attributes macro loop, create table, macro definition, proc sql, %if-%then-%else, proc step, dataset, user-defined format, clinical database, date variable, char., error message, oracle, macro variable source 17
37 How to write and track additional requests using Excel/Word Xavier Passera Over the life of a project, numerous output and data requests , which are not part of the SAP process, are made from people outside the statistics department. A process that defines and allows statisticians to track these additional requests is essential for quality assurance, project management and documentation. Roche Products Ltd has a defined PROC ess for Additional Requests and this paper highlights how a tool, using EXCEL and Word, manages the different aspects of the process excel, xls source 12
38 Improving maintenance and performance of SQL queries Bas van Bakel,
Rick Pagie		 Almost all programmers and a lot of end users are familiar with the term SQL or even with writing SQL queries. The challenge comes with the querying and joining of numerous large tables; together with the performance, which drops rapidly, the maintainability decreases and debugging can become very complicated. This paper will introduce ground rules for a method of programming your SQL queries in a way that they are easy to maintain and debug. It will also show that a query developed in the proposed way will improve the performance enormously compared to other queries proc sql, proc step, cpu time, create table source 5
39 In-line formatting Duong Tran With the existing of the Output Delivery System (ODS) from SAS v8.2 onwards, report can now be much more elaborated. This paper discusses how this can be achieved partly with inline formatting. In my opinion there are still confusions on this topic within the SAS users' community and I believe is due to the lack of documentation and demonstrated examples from the SAS Institute. This paper will explain 'All' regarding inline formatting under the ODS RTF (Rich Text Format) destination proc step, output delivery system source 8
40 Integrating data transformations using DI Studio and SAS DD Alistair Dootson DI Studio is a SAS tool that lets the user visually define executable processes, which until now had to be created and deployed using traditional SAS programming techniques such as using the program editor window within Foundation sas data integration studio, clinical research, proc sql, data merging, sas base, clinical trial, sas enterprise guide, proc step source 15
41 Integrating information - from char strings to SAS data libs. Kamila Duniec proc sql, proc step source 1
42 Intelligent outputs - Telling the story Claire Hoggins There has been a heavy focus on informative graphics in the past few years, but not all data types can be easily graphed. So for adverse events are we actually providing clinicians with the information they require to make appropriate decisions for the drug's development? There has been a large overhaul in standard safety outputs at GSK in 2007 for Phase I trials, revisiting the ICH guidelines and current practice. The choice of output produced is imperative to prevent clinicians misinterpreting the output or missing something of importance phase i, concomitant medications source 4
43 Introduction to the SAS9 Business Intelligence platform Gregory S. Barnes Nelson business intelligence, data mining source 12
44 Liberate, a SAS reporting system David Royle proc sql, excel, dataset, connection string, oracle, web service source 12
45 LSLV to Final CSR Tables in 24 hours - Dream or Reality? Stinder Fowler Reality! The production of final clinical study report tables in 24 hours from Last Subject Last Visit (LSLV) is a reality for Phase I healthy volunteer studies in Pfizer. Today, the m ajority of studies performed at any one of Pfizer''s four Clinical Research Units globally will have final tables issued within 24 hours. This is no longer a challenge for Pfizer but is standard practice. This paper focuses on the technology, standardisation and processes involved in enabling 24 hour reporting to be a reality, including an overview of the changes required to Pfizer systems, and the challenges faced clinical research, clinical trial, csr, database lock, phase i, adverse events, clinical database, data science, vital signs source 10
46 MAKS: A validated SAS Macro Library for data in SDTM structure Rolf Meinert,
Katrin Bhalla-Muller		 MAKS ("Makros zur Auswertung Klinischer Studien") is a CDISC SDTM based library of validated SAS macros for reporting data from clinical studies. The macros will be made available to a wide community. Robustness therefore was an important requirement and all macros have only few parameters. In order to make the SDTM utilizable for the generation of tables and listings it was necessary to amend the SDTM by a so- called variable attribute table (VAT) that contains a set of layout descriptors for SDTM variables. A further file with study- specific layout descriptors like sponsor name and page size can be delivered too. By end of September 2007, 35 main macros and 80 utility macros have been programmed and validated. Some macros (%AE, %DS, %CM, %LB, %MH, ) are domain-specific, others (%LBASE, %TIMENUM, %LISTTIME, ) can display data from several domains. Conclusion: MAKS is a powerful and validated tool for reporting data from clinical studies using very simple statements data cleaning, clinical trial, csr, adverse events, cdisc source 3
47 MDMAP: An application utilized in the mgmt of trial metadata Herve Guimard,
Eric Sorel		 The primary goal of the MDMAP (Meta Data Management and Publishing) application is the standardization and automation of the processes that Biostatistics used worldwide to capture, manage and maintain the clinical trial metadata when producing an electronic submission clinical trial, excel source 5
48 Medical safety review Marleen Nijs,
Ilse Augustyns		 Medical Safety Review (MSR) is a uniform process to facilitate safety review at an early stage. The information will be provided to clinicians and statisticians by a single word document with hyperlinks to predefined components adverse events, concomitant medications source 7
49 Merging by formats James McGiffen Merging multiple small datasets to one large one can have performance issues and merging datasets where there is a set of ranges involves can lead to inaccurate merging. This paper presents and discusses one possible solution to both these issues by using SAS formats. This paper will detail the advantages and disadvantages of using this method and provides examples of where it can be useful proc step, format statement, proc sql, proc format, put statement source 3
50 Method Validation in a PAT Environment using a SAS EG Addin Robert Eric Huisden In this paper analytical method validation with the aid of SAS Enterprise Guide add-in s is discussed. Examples are presented of add-ins that address assay validation issues in a PAT (Process Analytical Technology) environment. Streamlining method validation, robustness tests with experimental designs, multivariate analysis, real-time data and control charts are topics that will be demonstrated in these applications data cleaning, multivariate analysis, sas enterprise guide, proc princomp, regression analysis, outlier source 6
51 My Word, it's a table of contents John Woods,
Esther Bayon		 macro call, order by, macro definition source 7
52 OSR - Output macro system with Proc report and central storage.. Peter Gerber OSR is an Output macro system in SAS code, which enables you to create tables and listing output in a standardized way using PROC REPORT and ODS (Output Delivery System). Figures can be done with OSR and graphic procedures like PROC GPLOT. An important part of the OSR tool is a central repository of titles and footnotes in an EXCEL spreadsheet. From here, title and footnote information can be transformed into a SAS dataset and printed automatically into the output. Additionally, page numbering, date and time information, information about the datasets used for programming of the output and standard lines can be printed into the output. The output produced is landscape, pagesize=66, linesize=132 and the font is SAS monospace. Keywords: PROC REPORT, output, EXCEL, Macro system, ODS proc step, proc report, excel, macro variable, dataset, create table, sas output, permanent dataset, macro definition source 13
53 PK data file(s) creation process Ann Cuijpers,
Hilde Wuyts		 In order to start a pharmacokinetic(PK)(/pharmacodynamic(PD)) analysis, the Clinical Pharmacology(CP) department needs to obtain data from different sources, different departments: Clinical database: raw- and analysis data (sampling/dosing information, demographics, adverse events, co-medication, age…) Bioanalytical database (analyte concentrations) * Other: e.g. Protein binding information, Drug content information These source pharmacokinetic/pharmacodynamic data are combined in one or more data files by the Biometrics and Clinical Informatics(BCI) Statistical programmer analyst (SPA). The required derived variables will be added by this SPA. For all clinical studies that have PK/PD data, these data files will be generated by BCI for CP database lock, clinical database, dataset, clinical data, clinical trial source 3
54 Preliminary data process Antoine Brisacier Have you ever experienced difficulties when producing a SAS PROC REPORT summary or listing having a gap between results from a same block of ID/BY variables, using FLOW option, as below: Fig. 1 But would it be nicer to have the results gathered within the same block of ID/BY variables, even if the output from PROC REPORT is correct? Fig. 2 This paper will present SAS techniques along to the PROC REPORT''s use to overcome this issue and to get the above output proc report, proc step, character variable source 6
55 Preparing real world data in Excel sheets for stats analysis Volker Harm This paper collects a set of techniques of importing EXCEL sheets and creating keys to prepare the data for statistical analysis using PROC IMPORT and macro variables. The data stem from a real world problem that arose due to a possible production or transport problem. Main topics that came up were: How to name lists of variables in a set of sheets? How to preserve contents of labels in a readable form? How to import character columns with some numerical values? How to eliminate superfluous blanks and line feeds? How to rename variables in automated way? excel, data science, character variable, proc import, proc step, proc princomp source 6
56 Presentation of the demo of standardization of the ADS/SDS Claude Guyot The standardization of the SDS & ADS creation process involves a validated tool by following a computerized methodology. This validated tool will ensure quality and gain/improve efficiency, and will permit programmers to produce easier and quickly their standard datasets. They will mainly focus on the specificities of their study and they will have more time to include and perform the programming of it. This validated tool must be sufficiently flexible to take into account different study designs and different structures of database in=, macro call source 5
57 Proc format - tricks and traps Christof Binder The FORMAT procedure is one of the basic procedures that you can hardly avoid, even as a SAS beginner. However, there are a few subtle features and traps worth noting. This paper presents some of these features that come with formats. Care is taken to highlight traps along the way that might result in incorrect or unexpected results decimal, proc step, proc format source 5
58 Quoting macro variable references Shan Lee The aims of this paper are: 1. To give examples of situations in which quoted SAS macro variable references do not resolve as one might expect. 2. To explain the behaviour of the macro variables in these examples macro variable, &variable, macro quoting function source 6
59 Relationships between CDISC variables concerning day and date Susan Fehrer,
Russell M. Lavery		 study day, elapsed time, cdisc source 10
60 Relationships between CDISC variables linking domains (relrec) Susan Fehrer,
Russell M. Lavery		 study day, concomitant medications, adverse events, unique key, verbatim term, proc freq, cdisc source 12
61 Relationships between ORRES and other CDISC variables Susan Fehrer,
Russell M. Lavery		 numeric variable, char., cdisc source 9
62 Reporting by Elements - A New Way of Looking at Clinical Reports Magnus Mengelbier source 9
63 Reporting of NONMEM Models using S-PLUS Richard Pugh fixed effects, proc mixed source 7
64 Running clinical trials programs with Enterprise Guide Philip R. Holland source 5
65 Runtime funtime: Vertical data reporting using runtime formats Michael Auld With the industry-wide move towards standardisation using the CDISC SDTM, the perceived wisdom is to create more vertical data structures. When it comes to creating Derived data sets, many argue that this approach is incompatible with parameter-based data, for example Vital Signs, where the precision and number of decimal places varies from parameter to parameter. An approach adopted by many is to create separate variables for each parameter - thus creating a wide horizontal data set. This transposition of the data is a backwards step in the ultimate goal of the CDISC consortium, which is to harmonise and integrate the SDTM and the ADAM standards. This paper identifies a solution to this problem by retaining the SDTM vertical structure and using the under-utilised and often forgotten SAS runtime format facility. In th is way, parameter-based data like Laboratory results can be analysed and displayed to the correct level of precision in which they were collected time., data step, vital signs, proc means, clinical data, laboratory data, proc step, format statement, cdisc source 11
66 SAS Indexes: Benefits, Limits and Applications Carole Beaugendre SAS indexes are not widely used although they may considerably improve performance in specific cases. It is useful for programmers to know about SAS indexes and how to use them. However they should be used with caution as a badly designed index or an index used inappropriately may be less efficient than using no index. An interesting application is the creation of patient profiles, especially in large clinical studies raw data, clinical trial, index, dataset, proc step, proc freq, proc sql source 5
67 SASMAX - Maximise potential of reusing SAS code Beate Hientzsch In statistical programming units of pha rmaceutical companies and contract re search organizations (CROs), it is common practice to reuse SAS " c o d e a c r o s s p r o j e c t s . For complex standard programs and macros designed, validated and maintained by a standards group, this is typically a well-defined process. However during daily r o u t i n e , a l l k inds of SAS programs initially w ritten for single use are copied, modified and adapted from one project to the next to reduce time for development and validation, potentially leading to a bunch of slightly different versions. It's a challenging question for each SAS programming unit: How can one ease and standardize the reuse of SAS code, i.e sas programmer, sample, macro catalog, macro definition, sas programming, data cleaning, stored macro, libname statement source 9
68 Simple %str(ER)ROR Checking in Macros Magnus Mengelbier error message, macro put, %if-%then-%else, macro definition, macro flow control, options statement, work library, macro variable, macro quoting function, put statement, data step, flag variable, goto, libname statement, return, proc sql, proc step, macro loop source 15
69 Standard Graph Templates Philip R. Holland source 8
70 Standard Management Approved Report Templates Gary S. Stevens In the modern pharmaceutical business arena it is of paramount importance that any new drug or compound is brought to the market as soon as possible. Physicians and patients clamor for additional, more efficacious and safe treatments to meet the needs across a wide range of therapeutic areas. In response, pharmaceutical and biotechnology companies invest vast sums of money into research and development of new therapeutics. To ensure the success of a drug or compound this investment must be recouped and profit margins met in an extremely competitive market before therapeutic patents expire. Therefore it is essential, for the success of a pharmaceutical product to reach patients, that the clinical trial process is as quick and efficient as possible adverse events, adam dataset, in=, macro variable, concomitant medications, cdisc source 7
71 Standardization of submission data sets and analysis data sets Yvane Boudraa In Biostatistics & Programming (B&P) department, programmers and statisticians must create SAS datasets to perform the analysis and the reporting of clinical data. In case of submission, these datasets called Analysis Data Sets (ADS) will be provided to the health authorities. In addition, the health authorities require other datasets that contain all the collected information on subjects. These datasets are called Submission Data Standards (SDS) that will be mainly used for the medical review. To facilitate the review, the Food and Drug Administration (FDA) has endorsed (July 21, 2004) a standard submission data format, called the Study Data Tabulation Model (SDTM) developed by the Clinical Data Interchange Standards Consortium (CDISC). In terms of analysis data standards, CDISC Analysis Data Model (ADaM) team has provided metadata models and examples for analysis datasets used to generate the statistical dataset, adam dataset, cdisc source 4
72 Standardizing FDA data to improve success in pediatric drug development Julie A. Maddox,
Chris Decker		 The United States Food and Drug Administration (FDA) have accumulated the largest number of regulatory submissions of pediatric clinical trial data in the world. In line with the Critical Opportunities list initiated by the FDA in 2006, topic 6, opportunity 72 describes the development of a pediatrics database with a key goal of converting existing electronic datasets from pediatric studies to a standardized format so that the data can be imported into a data repository that can be queried. In order to facilitate analysis of data across various pediatric trials, the FDA needed the data transformed into a uniform standard structure. The FDA and SAS Institute initiated a pilot project to implement a standardized data repository of pediatric hypertensive data that can be queried, analyzed and reported on to promote the analysis of data across pediatric studies clinical trial, clinical data source 12
73 The Good, the Bad and the Ugly of programming standard Rebecca Tootell,
Natalie Collier		 missing value, macro variable, proc step, temporary dataset, proc sort source 1
74 The perfect reporting system - theoretically Laura Phelan,
Magnus Mengelbier		 clinical research, api, data cleaning, macro definition, macro variable source 7
75 The Pros and Cons of PROC DOCUMENT Philip R. Holland source 2
76 The value of metadata Bas van Bakel The SAS Metadata server is used to store metadata in a repository on a centralised location to support the enterprise intelligence platform. Although this repository contains data that can be of value to an end user, not all metadata is directly accessible. This paper will demonstrate how this informatio n can be retrieved and presented to an end user. The examples are extracted from a version of the SAS Information Delivery Portal which OCS Consulting has set up to function as a proof of concept environment to illustrate our vision of how web technology can be u s e d t o a l l o w a n o r g a n i s a t i o n t o i m p r o v e c o l l a b o r a t i o n between people and across departments sas management console, proc step source 10
77 They SQL Here, They SQL There Ian Amaranayake,
David Shannon		 The SQL procedure has come to play a significant role in many of the SAS 9 solutions. Not only is it still a language within a language for the Base SAS programmer, but it is a key procedure in tools such as SAS Data Integration Studio, Enterprise Guide and behind the scenes of Information Maps. A brief history of the SQL origins will be presented to understand why it is used and understood across several software packages and languages. Common uses of SQL and how it is set apart from the DATA STEP will be shown, including consideration to the new joins and set notations added in the last major release of SAS. A presentation of some SQL utilities will be given that are invaluable to a SAS programmer aiming to make frequent reuse of their code. Finally the internal workings of SQL will be discussed including some little known options that visualise what processing PROC SQL does internally, hence helping programmers to write more efficient code proc sql, proc step, dictionary tables, sas base, macro variable, sas programmer, sas enterprise guide, view, data step source 12
78 Using SAS stored processes and the SAS portal... Volker Harm Using SAS Stored Processes and the SAS Information Delivery Portal our department Global Drug Discovery Statistics is building up a statistics portal delivering SAS applications to scientists in Global Drug Discovery. We use this to convert newly developed statistical solutions into standard routine methods as well as to provide applications implementing established standard evaluations previously done by our department. This will be exemplified by a little application that uses control charts to determine control ranges for IC50-values used in cytotoxicology. Given the infrastructure of SAS Business Intelligence this is a really effective way to have applications centrally developed and maintained and to deploy them enterprise-wide without local installation efforts serving our mission "Do more statistics!" sas enterprise guide, proc step, macro variable source 9
79 Using SAS to perform a stratified t-test John Salter This paper examines the stratified t-test approach and provides some SAS code for provision of simple outputs as well as detailed datasets for output into tables and statistical appendices ancova, proc calis, covariance source 7
80 Using tagsets MSOffice2k to customize your Excel report Dominique Pinet The purpose of this paper is to show how SAS9 can be use to customise EXCEL listings. This method minimises the programming in SAS with the use of templates and ODS TAGSETS MSOFFICE2K. The format management on the EXCEL file is performed by XML code that is insert ed in the template. This method does not use Access to PC File excel, proc report source 10
81 Validation of programs developed using SAS Nikos Tsokanas A validation approach on programs developed us ing SAS, that satisfies the regulat ory requirements, is beneficial to drug development businesses. The Food and Drug Admi nistration (FDA) agency clearly states, within 21 CFR part 11, that drug organizations must ensure the ability of their com puterized systems to meet the applicable predicate rules. This is achieved by having in place a specif ic, verifying and controlled procedure, which needs to be documented, for all the SAS generated programs. In conjunction with a risk based approach, validation can be less time consuming, more effective and add more value to the overall process. High, Medium and Low Risk outcomes determine the relevant validation method that is then applied for the completion of the procedure. This paper will demonstrate the keys to an effective validation process and expand with a high level overview of such an efficient approach used within the Bios tatistics department in Roche data cleaning, pharmaceutical industry, sas risk engine source 7
82 Web techniques for SAS ODS output and more Jules Van der Zalm SAS programs can be defined as stored processes to output information to the SAS Information Delivery Portal. They most commonly output static tables, listings or figures. When using HTML techniques within the stored process, dynamic content can be generated, for instance allowing the end-user to interactively browse through multiple dimensions of tables and listing. The approach for the development of such a program will be discussed in this paper. The approach consists of the following steps: Step 1: The objective; Step 2: The base program; Step 3: A dynamic program; Step 4: Creating a user interface; Step 5: Embedding the user interface; Step 6: Retaining the user selection macro variable, vital signs, proc tabulate, proc step, class variable, macro definition, %if-%then-%else source 8
83 Web-enabling the Pharmaceutical sector Raymond Ebben,
Jules Van der Zalm		 The paper will discuss how pharmaceutical companies can take advantage of web technology when integrating information from departments that contribute to the clinical trial process. It is based upon a version of the SAS Information Delivery Portal which has been set up to function as a proof of concept environment to illustrate our vision of how web technology can be used to allow an organisation to improve collaboration between people and across departments. Supported by the examples within this portal environment, possibilities for integration will be explored thus creating an understanding of how to fully utilise the functionality the portal has to offer. The topics that will be covered are: xObjectivesofthispaper; xIntroductiontoportals; oPortals; oTheSASInformationDeliveryPortal; oPortalenvironment. xOurportalenvironment; oMotivation; oUsersoftheportal; oIntegratingdepartmentalinformation; oTakingadvantageofSASontheweb; oThevalueofmetadata statistical programmer, sas management console, data cleaning, adverse events source 14
84 WEB-TRIAGE: Application for patient registration in ph I studies Angelo Tinazzi,
Lucio Ferraresi		 Phase I clinical trials conducted to find recommended doses f o r u s e i n l a t e r p h a s e s t u d i e s , e m p l o y p r e - s p e c i f i e d guidelines. These determine what dose to administer to the nex t subject or group of subjects. "Standard" Phase I trials (in oncology) use what is often called the '3+3' design, where at least 3 patients are treated at each dose level (dose L), by applying the following rules: I f 0 p a t i e n t s e x p e r i e n c e d o se-limiting toxicity (DLT), escalate to dose L+1; I f 2 o r m o r e p a t i e n t s e x p e r i e n c e DLT, de-escalate to level L-1; I f 1 p a t i e n t e x p e r i e n c e s D L T , t r e a t 3 more patients at dose level L; ƒIf1of6experiencesDLT,escalatetodoselevelL+1; ƒ I f 2 o r m o r e o f 6 e x p e r i e n c e s DLT, de-escalate to level L-1; Most phase I trials in oncology are mu lti-institutional (MI) phase i, clinical trial, dose, dose escalation, stored procedure, data validation rules, phase ii, sql server, proc sql, clinical research source 10

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