PHUSE EU 2014 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A Day in the Life of a Data Sharing Specialist	Kelly Mewes	Presentation	pii, annotated crf, clinical trial, clinical research, regulatory submission, study day, cdisc	source	29
2	A Dynamic Approach to File Organisation using DOS within SAS	Michael Tang	An ability to execute DOS commands and operate Windows applications within SAS can provide a dynamic file organisation solution. Files external to the SAS environment can be arranged to make retrieval and review more efficient; using SAS as a data processor can automate this process and make it dynamic. This paper outlines a process to organise a Windows directory contain ing Tables, Listings and Figures (TLF) into a pre-defined structure such as defined in a Table of Contents or Tracker document, e.g. Batch numbers assigned to outputs depending on delivery timeframes. The process selects the required outputs, copying them into folders created using DOS commands and packages the new folder structure into a zipped file for easy delivery and review. Command options are discussed to give the user more control with process execution and steps are included to remove temporary files and folders that are created to free up disk space	staging area, macro variable, filename statement, options statement	source presentation source	7
3	A New Era: Open Access to Clinical Trial Data	Guillaume Breton	Presentation	clinical trial, clinical data, subject-level data, verbatim term, missing value, label statement, annotated crf, cdisc	source	21
4	A New Trend in the Industry - Partnership between CROs and Pharma. Do we Know How to Work Together?	Kevin Lee	There is a new trend in the working relationships between CROs and Drug companies. The relationships evolve from the traditional transactional model to a partnership in the recent years. The partnership in the pharmaceutical industry is a new adventure to both CROs and Drug companies, and it also impacts programmers. The partnership will bring the new challenges and opportunities to programmers. We will work with not only our colleagues, but also employees from other vendors and clients. The partnership working environments will require the new set of rules and behaviors. The paper will discuss what kind of challenges and opportunities we will encounter and how we will resolve and succeed in the partnership working environments. The paper will specifically discuss the role of a project leader in CRO who are working directly with the clients and other vendors in the partnership environments	service provider, sas programming	source presentation source	4
5	A Vision for the Future: Core Competencies for Statisticians and Programmers	James R. Johnson	Presentation	statistical programmer, clinical research, problem solving, phase i, phase ii, phase iii, sample	source	26
6	Accessing Study Results Online	Shafi Chowdhury, Mokhfur Alam Chowdhury	Presentation		source	1
7	ADaM Conversions: The Good, The Bad and The Ugly	Carla Santillán, Jessica Minuke	A good, bad and ugly face were identified from three types of ADaM conversions experiences. We will share our experiences, strategies and solutions from: x conversion of a pooled database; x dictionary updates; x conversion with SAP and TFLs as the only sources The Good: creation of final ADaM datasets in which SDTM / ADaM traceability was accomplished. SDTM and ADaM metadata modified accordingly to each other's structure requirements. The Bad: Source derived datasets and SDTM metadata didn't match up due to differences in formats, SDTM codelist, dictionary versions, etc. The Ugly: incomplete and unclear source and specifications can lead to mistakes or inconsistencies that could not be found until the validation process, causing re work and delay. In addition source datasets derived values may need to be re-derived to fit within the ADaM structure. All three conversions had challenges and led to process improvements which will help in future conversion projects	adam dataset, data cleaning, sdtm domain, data science, cdisc	source presentation source	11
8	ADaM Standards: What's New?	Gavin Winpenny		adam dataset, data standard, where statement, medical history, cdisc	source presentation source	9
9	Agile Project Management in a Regulated Environment	Alistair Dootson, Amol Waykar	Scrum is an agile approach to project management for software development or implementation, which if implemented correctly is able to satisfy regulatory requirements effectively and efficiently with high productivity and high quality. The classical waterfall approach has dominated the regulated industry as it meets the regulation requirement on Quality Assurance, Safety, Security, Effectiveness, Traceability, Verification and Validation, but this has resulted in challenges, such as vast amounts of unnecessary paperwork, error prone manual processes, lower productivity, slow response time to market, slow to implement change and adopt industry standards. In this paper we outline how "Scrum as an agile" approach can be used to address some of the key challenges we come across in the regulated environment. We will also discuss the key components of Scrum - values, roles, activities and artefacts - and how they are used during the process	data cleaning, data migration	source	5
10	An Introduction to SAS Visual Analytics for the Health Care Sector	Rhian Pilling	SAS Visual Analytics software is an in-memory analytic tool offering an intuitive, drag and drop interface enabling users of all abilities to explore their data effortlessly. Insights are shared easily leading to increased collaboration and improvements in the decision making process. This paper presents an introduction into the SAS Visual Analytics software. Topics include, managing the Visual Analytics environment, the SAS Visual Analytics Data Builder, the SAS Visual Analytics Explorer and the SAS Visual Analytics Reporter. Readers will learn how to load and unload LASR tables, build queries to manipulate tables, create data items, apply filters to data, build interactive charts and tables and learn how to apply chosen properties and styles to their output. This paper is aimed at all individuals who wish to gain a broad understanding of the SAS Visual Analytics software	nominal variable, numeric variable	source	19
11	Analysis of Self-Reported Health Outcomes Data from Web-Based Media Sources	Mark Wolff, Kenneth Lopiano	The process of obtaining valuable information from free text fields and narratives is an important part of any analysis related to health care. Often important clinical outcomes and covariates are embedded within clinician narrative and need to be extracted using text mining tools. These data offer additional information to that captured in electronic health records. An ever-increasing number of individuals routinely contribute and consume solicited and unsolicited information about a variety of health related issues using a wide range of internet and social media channels. Personal web based reports on health status, symptoms, treatments and associated interventions (whether drug- related or behavioral-related) provide a rich source of data that has the potential to complement and inform structured (planned) and unstructured (spontaneous) observations surrounding a range of potential treatments, compounds, approaches and various environmental delivery mechanisms	clinical endpoint, data integrity	source presentation source	2
12	Analysis of Time to FDA Medwatch Safety Alert for Therapeutic Monoclonal Antibodies	Pavla Kadlecová, Vid Stanulovic	Previous analysis of US FDA Medwatch safety alerts for monoclonal antibody therapeutics demonstrated that pre - marketing clinical trials can predict more than half of safety concerns. We expanded thi s analysis to assess whether predictable safety alerts, i.e. reactions for which there was a hint based on clinical trials, are detected sooner after drug approval than unpredictable safety alerts. Up to 31-Dec-2013 inclusive, 76 Medwatch alerts for therapeutic monoclonal antibodies were reported; 43 predictable vs. 33 unpredictable. Predictable alerts were reported at a median (IQR) of 41 (19 -77) months after approval vs. 53 (23 -73) months for the unpredictable alerts. The mean (SE) was 52.07 (6.69) months and 55.91 (7.06) months for the predictable and unpredictable, respectively. Time to alerts for predictable vs	cumulative frequency, proc freq, normality test, clinical trial, adverse drug reaction	source presentation source	5
13	Analysis of Time to FDA Medwatch Safety Alert for Therapeutic Monoclonal Antibodies	Pava Kadlecova, Vid Stanulovic	Previous analysis of US FDA Medwatch safety alerts for monoclonal antibody therapeutics demonstrated that pre - marketing clinical trials can predict more than half of safety concerns. We expanded thi s analysis to assess whether predictable safety alerts, i.e. reactions for which there was a hint based on clinical trials, are detected sooner after drug approval than unpredictable safety alerts. Up to 31-Dec-2013 inclusive, 76 Medwatch alerts for therapeutic monoclonal antibodies were reported; 43 predictable vs. 33 unpredictable. Predictable alerts were reported at a median (IQR) of 41 (19 -77) months after approval vs. 53 (23 -73) months for the unpredictable alerts. The mean (SE) was 52.07 (6.69) months and 55.91 (7.06) months for the predictable and unpredictable, respectively. Time to alerts for predictable vs	cumulative frequency, proc freq, normality test, clinical trial, adverse drug reaction	source presentation source	5
14	Automated Generation of Program Documentation by Means of Tagged SAS Comments and Metadata for Integrated Analysis	Thomas Wollseifen	It is sometimes difficult to maintain documentation and keep it in sync with the programs. In integrated analysis, pooling of study data is dependent on the study design and the metadata of the pooled database. The pooled data is then used for integrated summary reports of safety and efficacy. When preparing documentation files for a submission to an authority it is required to document all derivations of the different variables for each analysis domain (ADSL, ADAE, etc.). Preparing the documentation for the transfer files is often a tedious manual process. I will present a method of automating this process using predefined tags within SAS programs. The system interprets these tags and combines the metadata with the comments that describe the programmatic derivations to create the documentation. Finally, documentation for the individual domains and studies is consolidated into a single deliverable	clinical trial, comment, macro loop, macro definition	source presentation source	9
15	Automated SDTM Creation and Discrepancy Detection Jobs: The Numbers Tell the Tale	Joris De Bondt	The FDA is advising use of data standards as early as possible in the study lifecycle. As a result, Data Management centers are using the Study Data Tabulation Model (SDTM) to drive operations from First Patient In till Database Lock. Many tools on the market allow for the creation of SDTM datasets via intuitive user interface s. However, targeted tools are needed to manage nightly jobs taking care of data source downloads (eCRF, ePRO, Lab, etc), data uploads in a staging database, converting to SDTM and running edit checks before th e Clinical Data Manager arrives in the morning. Each step requires complete error handling (missing data sources, network connectivity issues) to guarantee a database ready for use by the Data Manager. Visualising t he performance metrics of the nightly jobs in intuitive graphs is key for spotting and resolving glitches , allowing for a well -tuned database environment that hosts a large set of small phase I to large phase III trials	clinical research, key performance indicator, cdisc	source presentation source	2
16	Automating Production of the blankcrf.pdf	Walter H. Hufford Jr.		crf, controlled terminology, cdisc	source presentation source	5
17	Backward Selection: Immunity Test of Covariates	Prathamesh Athavale	Backward Selection Method: Elimination round of variables using Mixed model repeated mea sure. This method is used to eliminate covariates in the model based on their significance. It follows step by step approach to eliminate variables one by one	backward selection, covariance structure, proc mixed, covariance, macro variable	source presentation source	10
18	Big Data - Step towards High Performance Analytics	Dhiraj Dabhi	The objective of Phase IV (post-marketing) trial is to test the safety/efficacy of the treatment on larger population. Such trials generally yield massive data. Even some Phase II/III trials which have large number of enrolled subjects or have larger study duration contain huge databases. It has been observed that SAS programmers experience problems while working with large SAS datasets. Even basic SAS programming steps become difficult to run on such huge datasets. The problems include high compilation and execution time, SAS I/O errors, memory/disk full, unexpected errors, session time out and so on. Also as the complexity level of study derivations increases, working with huge data becomes more difficult and time consuming and inefficient programming only adds more to it. It becomes more difficult when one needs to validate such a huge data	proc step, proc sql, options statement, cpu time, data step, proc means, clinical trial, dataset, sample	source presentation source	8
19	Biomarker as Essential Part of Clinical Development	Renuka Chinthapally	Biomarkers play an important role in the clinical development due to their ability to predict efficacy of drug earlier and accurately. Biomarkers are used as surrogate endpoints as they are cheaper than the true endpoints. Biomarkers are used as biological indicators for disease progression, effect of therapeutic intervention and drug induced toxicity. They reduce the overall cost of drug development. FDA estimates that just a 10% improvement in the ability to predict drug failures earlier in clinical trials could save US$100 million in development costs per drug. Biomarkers also offer the potential to inform treatment decisions, whether to move forward to the next phase of clinical development or not. The paper also emphasizes on prognostic and predictive biomarkers with examples. The growing need for biomarker evaluation in medical practice, has implemented the statistical methods to evaluate diagnostic accuracy	clinical trial, roc curve, change from baseline, clinical research, confidence interval, proc freq, binomial distribution, treatment period, proc logistic, logistic distribution, proc step, adverse events	source presentation source	7
20	Building the Next Generation	Michael Hall	Within our programming and statistics departments, we have successfully ran a student recruitment program for a number of years. Students approaching their third year of University will take a year away from their studies to gain experience in a professional environment. The benefits work both ways; we as a company offer bright young individuals a route into the industry, which they may othe rwise find difficult to obtain as a graduate. In return, what do we get? Enthusiasm, fresh ideas and an enormous talent pool of potential for highly sought after positions to name but a few. Many students upon completion of their final year of studies have jumped at the opportunity to apply for permanent positions at the company. Due to the students' time spent working here we know what we're taking on, with instances of returning graduates climbing their way up to management positions and even beyond	clinical programming, statistical programmer	source presentation source	3
21	Call Execute: Let Your Program Run Your Macro	Artur Usov	The SAS Macro language is an extremely flexible tool which facilitates SAS programmers with the ability to re -run the same code for a set of different parameters. As the number of required re -runs increases one could use a do- loop to rerun the same macro, passing macro parameters from a list of parameter values stored in a global macro variable. That can, however, become cumbersome as the list of parameters and macro calls becomes large. Instead, Call Execute allows storing all macro parameter values in a SAS dataset and us ing those values in a `DATA STEP` to execute the macro. Or, the other way around, it allows using the values in a SAS dataset as parameters to a macro. It will also allow you to create programs that are completely controlled by the data that it processes. This paper will demonstrate how the SAS Macro language combined with Call Execute statements in a `DATA STEP` can help the execution of macros with macro parameter values obtained from a SAS dataset	call execute, macro parameter, macro variable, dataset, macro definition, macro facility, data step, set statement, excel, proc step, in=, proc sql, macro put, macro call, macro quoting function, character functions	source presentation source	9
22	CDISC Dataset-XML - A Dataset Structure for Clinical Trial Data Transport for Future Drug Submissions	Joerg Dillert		data standard, adverse events, cdisc	source presentation source	6
23	Challenges of Different MedDRA Versions for Integrated Data and Databases	Peter Bonata	For the creation of an initial integrated adverse event analyses dataset it is a common situation that the underlying studies are coded in different MedDRA versions. Therefore a recoding and update to a single version is necessary for a harmonized dataset that assures reliable results and the traceability across versions. So far "neither the current version of the ADaMIG nor this document fully covers the integration of multiple studies" (CDISC ADaM Data Structure for Adverse Event Analysis Version 1.0a). This paper shows a workflow how in cooperation with a standard team these tasks can be fulfilled as well as how former dictionary versions can be handled. In contrast to the CDISC proposal to keep the original coded terms on record level in the data set, it is suggested here to create an analyses dataset history domain containing original and later versions	adverse events, clinical trial, adam dataset, concomitant medications, cdisc	source presentation source	8
24	Challenges with the Interpretation of CDIOSC - Who Can We Trust?	Linda Palm Simonsson	Many smaller companies have none or very little knowledge of biometrics and turns to one or several CROs to help them conduct their trials or a submission. A small company expects the CRO to be the expert and since many small companies lacks a biometric department they also lack internal resources, routines and expertise to do a qualified QC of the SDTM and the ADaM packages received from a CRO. Sometimes the difficulties for the sponsor begins already at understanding what's included in the proposal vs what's needed according to FDA or other agency. Other difficulties may be inconsistency between e.g. SDTM IG and FDAs requirements. This presentation will address some errors, misunderstandings and difficulties seen in the interpretation of different documents and showing some inconsistencies between different sources	medical history, treatment period, imputation, adam dataset, adverse events, concomitant medications, cdisc	source presentation source	10
25	Change Management - An ICON Approach and Case Study	Anagha Pradhan	In the pharmaceutical industry, reporting of tables, listings and graphs is done using various platforms and systems. Most pharmaceutical companies have their own reporting system for analysis and reporting. This paper describes the change management steps required to ensure a successful transition from one reporting system to another. The paper will cover authors' own experiences, while transitioning from one reporting system to another, describing the challenges and successes along the way, as well as steps taken to avoid potential failures. I will also speak about the effect of the decision to transition on other factors e.g. timelines, documentation, data structures, personnel and so on	clinical research, data standard	source presentation source	4
26	Checking Symmetry in Predefined Time Assessments	Antonio Nieto, José Luis Jiménez Moro	Overall survival (OS) is usually the gold standard endpoint in oncology phase III clinic al trials comparing a new treatment with the current standard of care. In several situations, OS can be substituted by progression-free survival (PFS), which has to be measured in pre- specified time points. These assessments must be done consistently at the same intervals in both treatment arms to avoid imbalance that would result in a biased global comparison. Consequently, a crucial check (if not the first) would be to ensure that symmetry cannot be rejected. Different ways to conduct this check using SAS are described herein, from Box -Plots graphs to Wilcoxon test to compare time to disease assessments. In addition, Kaplan-Meier curves of the time from randomization to the first and second disease assessments are also shown	treatment arm, randomization, clinical trial, overall survival	source presentation source	9
27	Clinical Site Dataset in eSubmissions to the FDA	Stefanie Sturm	To verify the integrity of the data, submitted to the FDA, is the cause for CDER to perform on -site inspections of clinical trial sites. For an early planning of on-site inspections, CDER recommends the applicants to submit data on clinical site level. In December 2012 the draft guidance "Providing Submissions in Electronic Format - Summary Level Clinical Site Data for CDER's Inspection Planning" was published by the FDA, describing the requirements for the clinical site dataset. The poster will provide information about the regulatory background and an overview on how to prepare and submit the clinical site dataset. This covers the information in cluded in the dataset, as well as the clinical site submission package that is send to the FDA	clinical trial, treatment arm	source presentation source	3
28	Clinical Study Data Processes @ LEO Pharma	Joergen Iversen	Clinical data flows through a surprisingly complex set of transformations and processes from the point where it is collected in a Case Report Form and until it is presented in a Clinical Study Report. At LEO Pharma, several different data standards from the CDISC organisation and others are used underway, some supported by standard tools, others less so. Data is validated, transformed, reported and QC'd by both standard tools and systems as well as many thousands lines of custom code, none of which may in any way alter the contents of the data, but all of which must act to ensure that the same data remains correct and well documented, while being used for many diverse purposes. Covered here are details of which data standard is used and for what purpose, which standard system controls the flow of data, and the amount of custom code	clinical trial, csv, 21 cfr part 11, data standard, clinical data, cdisc	source presentation source	4
29	Collaborative Development of an Open Source Repository for Standardised Analysis Using Cloud Services	Michael Carniello, Hamming TU	Presentation	clinical trial, data transformation	source	1
30	Communication - Building the Invisible Bridge	Anindita Bhattacharjee	"You can have brilliant ideas, but if you can't get them across, your ideas won't get you anywhere" - This quote by Lee Iacocca, sums up the importance of communication. Being able to communicate effectively is a key aspect everywhere. Apart from programming capabilities, communication skills play a significant role in the success of statistical programmers in the clinical research domain. One big reason for good communication being critical is the remote ly distributed and culturally or linguistically diverse global teams. However, the observed trend is that it is quite uncommon to have programmers with the right blend of technical and communication skills. The usual recourse of outsourcing trainings to external faculty or institutions does not seem to help to the desired extent. This paper share s ideas of focused communication training and describe s the implementation experience on a sample of appropriately chosen statistical programmers	statistical programmer, sample	source presentation source	9
31	Concept for a Clinical Trial Reporting System	Daniil Shliakhov	During the process of clinical trial analysis, information is presented in tables, listings, and graphs and most pharmaceutical companies and outsourced service providers use SAS to prepare the outputs. Oftentimes, statistical programmers spend a lot of time creating standard outputs like demography or adverse event tables, rather than spending their time and efforts working on more complex statistical outputs. The reporting system concept presented in this paper would produce validated statistical outputs in one click - thereby increasing efficiency and saving companies time and money if implemented in the future. This paper focuses on the concept of the reporting system, describes all main features that should be present in the system, and explains some technical issues that programmers can face while using the system	statistical programmer, clinical trial, adverse events, data science	source presentation source	2
32	Confessions of an obsessive compulsive programmer	Diana Stuart		data step, drop statement	source presentation source	4
33	Conjoint Analysis of Treatment Preference	Milena Balcerzak	Treatments are often examined for their efficacy, t olerability, effects on quality of life and cost ef fectiveness. No regimen is likely to have all the attributes that p atients would ideally like. Therefore, it is crucia l for health care industry to evaluate the patient's relative importance for each of the treatment features and their pr eferences for alternative treatment concepts. Conjoint analysis is a popular marketing research t echnique used to analyse product preference data an d to simulate consumer choice. Application of this metho d in health care industry has increased rapidly in the past decade. Among other things it is used in trading off desirable attributes (e.g.: high effectiveness) ag ainst undesirable ones (e.g.: cost) and describing which ones are the most important in determining the pati ent preferences for one regimen over another. In this p aper, conjoint technique was applied to analyse pat ient preferences for various treatment alternatives	conjoint analysis, main effect, proc step, predictor variable, response variable, treatment-regimen strategy, proc freq	source presentation source	12
34	Considerations in Creating Transparent SDTM-Based Datasets	Jerry Salyers, Richard Lewis	One of the definitions that one finds for "transparency" emphasizes that anything judged to be transparent is "characterized by visibility or accessibility of information" and that it is "free from pretense or deceit". As one looks at the real issues of data transparency, and in much the same way as looking at data "traceability", the methods for the preparation of the SDTM-based submission datasets play an important role. The methods employed will impact a reviewer's level of confidence in how "transparent" the final datasets are and how well they reflect the overall clinical data flow from the point of collection to the point of submission. The correct mapping of stored operational data to SDTM-based datasets represents an all-important layer in the overall quest to provide transparent and traceable data. This paper will look at a number of specific areas where incorrect or misleading mapping may compromise this goal	medical history, clinical trial, data governance, data cleaning, clinical data, missing value, concomitant medications, cdisc	source presentation source	8
35	Consistent Data Delivery	Thevaki Mahedevan	Often data is delivered to Biostatistics with inconsistencies in the repeated extract of the raw data from one delivery to another, while programming is ongoing for interim deliveries. Some are true data issues that need to be queried by Data Management (DM). However, occasionally these are issues that occur during a mapping (e.g. to SDTM) or during the extraction/transfer process, and are not true data issues. These need to be highlighted to DM immediately for action and redelivery. How does Biostatistics best approach these raw data sanity/acceptance checks between each data delivery? One approach is to manually check subject counts in key datasets, another is to write SAS code to check data programmatically. The advantages of doing checks programmatically are that it is more reliable, minimizes human error and less time consuming in the long run. A program can be written to cross check all raw datasets, variables and observations that are delivered each time	proc compare, proc step, by group processing, proc contents, cdisc	source presentation source	7
36	Cost-Effectiveness in Hospitals Using SAS	Ilias Pyrnokokis	Economic crisis imposes fiscal policies in the health care sector. However, the provision of hea lth care and the improvement of health will always be of top priority regardless the source of funding, private or public. As a result, the costs of health care must be minimized, while at the same time a high quality has to be maintained in the delivery of health care to achieve the optimal allocation of given resource s. Therefore, cost analysis standards must be set. Hospitals are the field where analysis is performed to compare their effectiveness versus hospital care at home. However, little research has been done using SAS software as an analytical tool. In this paper, we conduct such an analysis with the assistance of SAS to show its effectiveness when the analysis demands the usage of big data and the transparency in the results for the decision maker s	confidence interval, sensitivity analysis, proc step, proc univariate	source presentation source	8
37	Creating Graph Collections with Consistent Colours Using ODC Graphics	Philip R. Holland	Collections of line graphs or bar charts, where the graph data is grouped by the same value, are frequently used to identify differences and similarities in behaviour. Unfortunately, by default, the colours used for each line can change across the graph collection if some group values are not present in every graph. In `SAS/GRAPHTM` this problem has been solved by generating SYMBOL or PATTERN statements based on the data, or using annotation to create all of the graph lines, bars and legends. Neither of these solutions is readily available in `ODS` Graphics, so this paper will explore how to solve this problem using macros to implement the graphic "layer" approach in `PROC SGPLOT` and `PROC TEMPLATE`, so that the user has complete control over the data colours and styles applied to every graph	proc step, proc sgplot, macro loop, %if-%then-%else, macro variable, macro definition, sample	source	14
38	Creating Standard Analyses and Displays for Common Safety Assessments in Clinical Study Reports	Mary Nilsson, Ann Croft		phase ii, adverse events, clinical trial	source presentation source	3
39	Data Challenges in Adaptive Trials	Claudio Garutti	Adaptive trials are clinical studies where one or more parameters (e.g. sample size) can change as the study progresses, in a pre-specified manner, without compromising its scientific validity. Across the industry, simple adaptive designs are used on approximately 20% of clinical trials. Adaptive trial designs may save substantial time and resources by early study terminations due to futility; sample size re-estimation; and reduction of the number of protocol amendments. One of the main barriers to wider adoption of adaptive trials is operational concern, in particular delays and disruptions in trial execution. The objective of this work is to introduce adaptive trials and their data challenges, and to propose a possible data strategy	interim analysis, adam dataset, sample size, data governance, sample, treatment effect, sample size re-estimation, randomization, adaptive design, data integration, type i error, data science, clinical trial, data cleaning, dose finding	source presentation source	5
40	Data Transparency through Metatada Management	Judith Goud, Julie Smiley	In response to public pressure for data transparency, regulatory authorities are requiring that clinical trial sponsors make more trial data available, and industry organizations are adopting principles to publish 'all sponsored trials irrespective of the results'. While publication of clinical trial data is a challenge, data transparency also requires assurance that reported data are accurate and are coming from the official source. Currently however, the data resides in a myriad of systems and formats, making it difficult to maintain the lineage from data collection through analysis and reporting. By managing data about the data or metadata across the enterprise, organizations can provide full data lineage for regulatory compliance and improve business efficiency at the same time	clinical trial, clinical data, data standard	source presentation source	4
41	Data Transparency: Data De-identification and the Role of the Programmer	Jean-Marc Ferran	Presentation	data masking, adverse events, clinical data, data standard, privacy, medical history	source	20
42	Data Transparency: Moving from Bad Pharma to Good Science	Gary Hearfield, Katherine Tucker	Presentation	clinical trial, csr	source	1
43	Define your Own SAS Functions for Date and Numeric Validation using PROC FCMP	Jason Smith	Every clinical trials programmer is at some point going to encounter invalid numeric or date values collected in a character field. There are many different methods of checking for valid numbers or dates in a character variable; from writing a validation macro to just using an input statement and hoping for the best . Wouldn't it be useful if there were a n easy to use SAS function that would check if a character variable holds a valid numeric value, or a valid date? With the introduction of the SAS Function Compiler (FCMP) procedure in SAS version 9.2 we can now define these SAS functions that we always wanted to have . This paper will present some simple validation functions and some more complex date functions as examples of what can be achieved with `PROC FCMP`	sas function, character variable, input statement, date variable, out=, proc fcmp, proc step, data cleaning	source presentation source	5
44	Dequote Me On That: Using the Dequote Functions to Add some Friendliness to SAS Macros	John Hendrickx	SAS macros need to be user-friendly (certainly if you're going to use the macro yourself). This paper shows how to use the dequote function to make macros easier to use. Macro calls can use single or double quotes to mask text that would otherwise require macro quoting. The macro itself is written to use dequote to remove quotation marks and unmask special characters. The macro becomes easier to explain, easier to use, easier to read	macro variable, macro quoting function, macro function, macro parameter, macro put, error message, macro definition	source presentation source	8
45	Development of Exploratory Statistical Programmers Based in India	Jan Stefanek	Interest in cost reduction motivates pharmaceutical companies to collaborate with programmers located in emerging countries like India. However, cultural differences and differences in work habits have to be bridged in order to successfully team up with such countries. Additionally, effective exploratory and post study analyses impose special requirements on statistical programmers. The process of exploratory work at UCB for one of its products is briefly outlined, and a training concept is introduced that has successfully been applied at UCB, to integrate programmers from Accenture, based in Mumbai, India, into an exploratory statistical programming team. Finally, a train the trainer approach is presented, which in the future will be used to develop programmers for exploratory work on site at Accenture	clinical programming, clinical trial, statistical programmer	source presentation source	4
46	Discover Define-XML	Mark Wheeldon, Kevin Burges	Historically, Define.pdf was used as a submission deliverable to enable FDA reviewers to understand the content of submitted proprietary datasets. Define-XML was introduced by CDISC as a machine-readable alternative, and this has replaced Define.pdf as a mandatory part of an eCTD-based FDA submission. Define-XML is still often seen as an afterthought, only produced when datasets are ready to be submitted	proc contents, sdtm domain, clinical trial, where statement, data cleaning, annotated crf, cdisc	source presentation source	12
47	Do You Like What You See? - Understanding Data in Magnetic Resonance Imaging Studies	Oliver Wirtz	As diagnostic methods evolve and new measures find their way into clinical trials statistical programmers are faced with more complex data. Magnetic resonance imaging (MRI) data can become difficult to interpret for non-specialists: Results may be calculated from the actual electronic image using sophisticated computational algorithms. These procedures often involve external vendors and therefore create logistical challenges. Programmers also need a basic understanding of underlying physical principles in MRI to be able to understand the meaning of the results used in the final statistical analysis. This paper will cover the overall workflow from MRI data generation and assessment, the further processing procedures at an external reader site to final analysis	data science, missing value, raw data, statistical programmer, clinical trial, proc freq, final analysis	source presentation source	7
48	Don't Miss the Hidden Gems Among the New Features of SAS ODS for Report Creation	Patrick René Warnat		output delivery system, file statement, proc step	source presentation source	5
49	Drug Exposure Plot: Tailored Approach to Display Ongoing Data	Mathias Lebreton	The 80/20 rule is driving business needs within Clinical Reporting. A major challenge we are faced to is the following: how can we minimize the 20% non-standard requests, that currently demand around 80% of our resources. In early phase studies, where we are just starting to learn about the compound, this problem is particularly pronounced. We have found that we can best tackle this issue by providing tailored graphs that aim to address the scientific question. Such graphs are key to increase the data quality and facilitate decision making. The drug exposure plot is a good example of programming insight to the study status for clinical te ams. Several layers of information can be displayed (onset of particular events such as AE or tumor evaluations) on top of patient dosage administration history (dose change, interruption). It allows to display the combination of diverse collected or derived information and serves several purposes such as data transparency	dose, adverse events, exposure, study day, proc sgplot, phase i, protocol amendment, proc step, phase ii	source presentation source	12
50	Dynamic Oceans and Static Poolings of Clinical Trial Data in NVx	Panteleo Nacci	Business drivers to merge legacy and current clinical trials data using common, non-proprietary data structures are increasing, often giving a competitive advantage to companies which can query all data available from clinical trials. Novartis Vaccines has based the decision to develop its Clinical Data Repository on this aspect, as well as other important ones. This paper gives some insights on how data from multiple trials are assembled and utilized to support regulatory submissions, PSUR/DSUR/IB preparation, etc. Special attention is paid to the mechanism devised to allow the identification of what has been administered to each subject	clinical trial, adverse events, lookup	source presentation source	9
51	Evaluating the Tradeoff between Quality of Life through Quality-Adjusted Survival Analysis	Lawrence Rasouliyan	Clinical trials often employ many different endpoints to evalua te the benefits and risks associated with treatment. These endpoints, however, may not always align with one another. In oncology studies, for instance, a more efficacious treatment, which prolongs survival, may also be associated with greater toxicity, which decreases quality of life. Quality-adjusted survival analysis (QASA) provides a useful tool to calculate summary meas ures that incorporate both survival and overall quality of life so that the trade-off can be compared across treatments. T opics to be covered include construction of quality-adjusted Kaplan-Meier curves, calculation of quality-adjusted life years (QALYs), and examination of an exploratory method to evaluate quality-adjusted median survival	median survival, survival function, survival analysis, clinical trial, overall survival, confidence interval, kaplan-meier plot, proc means, hazard ratio, auc, bootstrap, likelihood function, sample, log-rank test, p-value, rank test, weighted mean, proc step	source presentation source	12
52	Every Study is Special! - Governing Data Standards	Chris Price, Elizabeth Nicol	The definition of data standards aligned with CDISC is just the start of a journey. O nce standards have been defined, they need to be implemented on each study according to the protocol in data collection, tabulation and analysis. Roche governs the Global Data Standards for all of these areas with a cross -functional committee managed by the Data Standards Office ( DSO). The committee is made up of five core teams each supporting a different area of the standards. Each study team, where necessary, submits requests for approval for deviations from the current standards to be implemented at a s tudy level. In addition to deviations for individual studies requests are also received for the development of new standards. All requests are evaluated by the DSO and then brought before the Governance Committee for a decision and actioned as appropriate	controlled terminology, adam dataset, data science, clinical trial, data standard, edc, cdisc	source presentation source	12
53	Exploring Safety and Diagnostic Data using Self-Organising Maps	Colin de Klerk	Using self-organising map (SOM) neural networks can reveal features and clusters of similar characteristics in the underlying data. In a SOM-based visualisation, multidimensional data are mapped onto a 2D grid and can help to identify initial regions of interest. Subsequently these can be examined more closely by conventional means. While SOM techniques are already integrated into SAS Enterprise Miner, we apply them by first principles to sampled safety and diagnostic data using Base SAS and `SAS/GRAPH`. Nominal, categorical input data present a particular challenge, since SOM models typically assume simple, continuous input data. The results reveal certain predictable relationships, but also less intuitive patterns that provide potentially interesting kernels on which to concentrate further analysis	neural network, system organ class, adverse events, training set	source presentation source	9
54	FDA Guidance on Standardised Study Data for Electronics Submissions	Lauren L. Shinaberry, Éanna Kiely	In February 2014 the FDA released several draft guidance documents on standardized study data for electronic submissions with a 90-day window for public comment. The final guidance is expected to be released 2015. Two of the three guidance documents will be considered binding once they are released as final. From the time the final guidance documents are released sponsors will have between 24 and 36 months (depending on the submission type) to comply with the binding guidance documents. This paper will highlight what impact the two binding guidance documents will have on sponsors as they prepare their submissions in the period following the finalization of the binding guidance. We will also discuss some of the content of the non-binding guidance that may require a change in how sponsors are implementing CDISC standards within their organization	data standard, data standardization, cdisc	source presentation source	7
55	Findings About: "Findings About"	Madhura Khare	Study Data Tabulation Model (SDTM), defines standard structure for study data tabulations for regulatory submissions . This not only saves time and money but also furnishes a choice for sponsors to automate the process of standard data creation. SDTM defines general observation class which is further divided among three classes Interventions, Events and Findings. The SDTM does not allow the addition of new var iable; In addition the s upplemental Qualifiers special purpose model is used to capture non-standard variables. In some situations it is observed that collected data from the case report form can neither be represented within an Event no r in Intervention also it is not always easy to map information in supplemental qualifiers. In such situations find ings about domain models are utilized. This paper discusses advantages of findings about model in certain situations. Case studies from the real life studies will b e discussed to get familiar with Findings About	adverse events, crf, cdisc	source presentation source	8
56	FLIPFLOP the Data: To Check Sustained Timepoint Occurrences	Rucha Landge	Frequently in dataset creation as well as in reporting there is always a need to restructure data from colum ns into rows and rows to column . What other method than the conventional `PROC TRANSPOSE` wil l be used to flip -flop the data . `PROC TRANSPOSE` is a simple method and very precise to obtain the desired results. Keeping aside the conventional usage of `PROC TRANSPOSE` to obtain single observa tion from multiple observations , let us know some more benefits of this procedure. We frequently come across instances in CLINICAL REPORTING to check consecutive occurrences of events across time- points like days, visits. These checks are very significant while creating summary reports. This paper will elaborate on different perspective to use `PROC TRANSPOSE`	proc transpose, proc step, data step, baseline, trial elements	source presentation source	8
57	Frequency Tables - Aspects to Consider	Katja Glass		proc freq, adverse events, independent qc, proc step, proc means, by group processing, adam dataset	source presentation source	10
58	From Gene Expression to Expression Cartography, Grade Correspondence Analysis Application in Class Comparison Studies	Edyta Winciorek	DNA microarrays and gene expression profile analysis give new possibilities in clinical trials analysis. An important goal of microarray studies is the reliable detection of genes that show s significant changes in observed expressions of gens between treatment and control group of patients in clinical trials. Taking into account tha t DNA microarrays can simultaneously measure the expression level of thousands of genes within a particular mRNA sample a specialized statistical techniques are required for the analysis of high -dimensional microarray data. In this article the overview of the applications of Correspondence Analysis (CA) to microarray data will be presented. CA methods will be used to detect the differences and associations in gene expression among treatments and controls. The results will be discussed using measures of the "representativness" of the trends, as well as measures of their " regularity"	proc corresp, sample, hypothesis testing, clinical trial, proc freq, anova, data science	source presentation source	6
59	From ODM to SDTM: An End-to-End Approach Applied to Phase I Clinical Trials	Alexandre Mathis	Clinical Data Management (CDM) activities performed during phase I clinical trials are generally conducted over a shorter period of time compared to later clinical phases. Typically, participants are healthy subjects, study sizes are smaller, measurements are more numerous and more frequent, and additional measurements and protocol adaptations occur frequently due to the fact that the compo und is not as well characterized as in later stages of development. This paper presents the data management model we developed to tackle such challenges. Our model considers that the responsibilities of the data managers include the design of the electroni c Case Report Form (eCRF) and the oversight of the clinical data collection at the clinical site including data cleaning and tabulation of the clinical data according to the Standard Data Tabulation Model (SDTM). Thus, the data manager is responsible for collection and delivery of the raw clinical data for the statistical analysis	clinical data, statistical programmer, clinical database, data governance, clinical trial, phase i, sdtm programming, adverse events, data science, vital signs, sdtm domain, data cleaning, cdisc	source presentation source	5
60	From SAS Programmer to Manager - Facilitating the Transition	Russell Gibson	Today's programmer typically has a mathematical and statistical background; they are well versed in procedures, algorithms and specifications. However when asked to take on management responsibilities, knowledge of SAS may be less helpful. It suddenly becomes apparent that a vast array of soft skills is required in order to develop another person, to guide their professional development and to lead them to success. This paper suggests ideas and tools to help smooth the transition from programmer to manager. Its focus will be management of people, rather than the already well documented subject of project management. The author discusses personal experiences of accepting people management responsibilities for the first time and compares these with management and leadership theories found in established literature		source presentation source	7
61	Handling Non-Standardised Questionnaires	Anne Sophie Beckx, Éanna Kiely	The CDISC QS sub -team is currently creating questionnaire (QS) supplements to the SDTM implementation guide (SDTMIG) and appropriate controlled terminology, to facilitate their usage and to ensure consistency between different SDTM submissions. However, this can only be done for questionnaires which are in the public domain or for which permission to publish controlled terminology was granted to CDISC. Nonetheless, the unavailability of a QS supplement to the SDTMIG for a particular questionnaire should not be an obstacle to creating a QS domain according to the SDTM philosophy. Applying the same simple and straightforward rules that are used to create QS supplements to the SDTMIG will ensure that the resulting QS domain: is of high quality is consistent with SDTM * will be consistent with the supplements to the SDTMIG if/when they are created	controlled terminology, sdtmig, cdisc	source presentation source	5
62	How the Release of the Therapeutic Area Asthma User Guide Affects an Ongoing Submission	Nick De Donder	In December 2013, the Therapeutic Area Data Standards User Guide for Asthma was released in order to improve standardization of Asthma studies. This paper will present the way we at Business & Decision Life Sciences handled the conversion of several Asthma cl inical trials for a FDA submission using SDTMIG V3.1.2 Amendment 1 before the release of the TA Asthma User Guide. At the start of the project the Respiratory Physiology (RE) domain was under discussion in the draft version of Asthma User. For Questionnair es findings, the Asthma User Guide makes a difference between Symptom Assessment (in Findings about CE), and other questionnaires (in QS). Some differences are present in the way to collect Healthcare Resource findings. Regarding Asthma history, management of the date of diagnosis could be subject to multiple interpretation	clinical trial, sdtmig, cdisc	source presentation source	6
63	How to Improve Performance on SDD 3.5	Rob Wartenhorst	Novartis Vaccines moved from a legacy SAS environment to SAS Drug Development (SDD 3.5). With the move the data format was converted from the legacy data structure to SDTM data structure and the reporting macros updated to use the SDTM data. To get a head start on the conversion, the reporting macros were updated on the legacy SAS environment. Not realizing how the repository moves its data around we ended up with a very inefficient use of the system. This paper addresses a technique we applied to make system performance more acceptable	macro variable, libname statement, cdisc	source presentation source	3
64	How to Improve Your Figure. An Overview of Annotation Techniques in Graph Template Language	Konrad Zywno, Bartosz Kutyła	Presentation	graph template language, proc template, sg annotation, sample, proc step	source	42
65	How to Validate a SAS Macro - A Case Study	Edwin Ponraj Thangarajan, Giri Balasubramanian	Validate SAS Macro in Agile Our SAS Macros \| The phases in agile are iterative and the process may return to a previous phase at any point as required. Each phase has one or more outputs that are natural outcome of following a validation process that consistently produces high-quality system solutions that meet the needs of the business. The outputs that provide evidence of this process, for the purposes of audit, inspection & regulatory compliance, are compiled into a validation package. Edwin Ponraj Thangarajan \| Giri Balasubramanian \| PRA Health Sciences, Chennai, India. Validation is a process of establishing documentary evidence demonstrating that a procedure, process, or activity carried out in production or testing maintains the desired level of compliance at all stages. This process is extended into all aspects of Clinical Trials including Data Management, Data Conversion and Analysis	macro definition, error message, data cleaning, macro variable	source presentation source	1
66	Implementation of CDISC ADaM in the Pharmacokinetics Department	Joanna Magielse, Tineke Callant	SGS Life Science Services as a leading CRO, is one of the pioneers in the implementation of CDISC standards. Given the positive experiences in the SGS Data Management and Biostatistics departments (implementation of SDTM and ADaM respectively), the Pharmacokinetics (PK) Department recently decided to adopt the CDISC standards as well. In an SDTM database, pharmacokinetic data is stored as one record per subject, per time point (PC domain) or per pharmacokinetic parameter (PP domain). For the PK analysis, the generation of Tables, Listings and Figure s, and the statistical analysis on PK parameters, 'analysis ready' datasets are created. The structure of these 'analysis ready' datasets is based upon the Basic Data Structure (BDS) of CDISC ADaM. This paper will highlight a practical approach in convert ing PC into ADPC and PP into ADPP, with the focus on data handling, criteria evaluation and sub-analysis selection, important aspects of PK analysis	key, data science, pk parameter, adam dataset, life sciences, bds, sample, cdisc	source presentation source	6
67	Implementation of Oncology Specific SDTM Domains	Jacintha Eben	Information regarding tumor lesions and disease response is ke y in oncology clinical trials to evaluate if the primary or secondary endpoint, usually being defined as a time to event endpoint, has been achieved. Specific oncology domains described in Study Data Tabulation Model Implem entation Guide (SDTMIG) 3.2, are representing the data collection of tumor lesions and the evaluation of response(s). Identification information of the lesion is collected in the tumor identification domain (TU). Each identified lesion is repeatedly measured or assessed at subsequent time points. The follow up of each of these lesions, lesions that are split up or merged, and new lesions is captured in the tumor results domain (TR). Using all of this information the investigator eval uates the disease response, captured in the disease response domain (RS)	clinical trial, disease response, sdtm domain, baseline, clinical database, pd, tumor identification, tumor results, cdisc	source presentation source	8
68	Interactive Data Visualisation of Adverse Event Clinical Trial Data with the D3.js. Script Library	Johann Laurent, Hugh McDevitt	Interactive data visualization of data can provide a clear and straight forward way of quickly communicating information. For example adverse events clinical trial data represented with force directed graph. Each node is represented by a pie chart and its size is proportional to adverse events frequency. Seriousness of adverse events is identified with the outer radius length of the pie chart. Information is broken down from MedDRA System Organ Class (SOC) to Adverse Reaction Terms	adverse events, data visualization, clinical trial	source presentation source	5
69	Internship - First Step in Personal Development	Jaroslaw Parzych	Personal development usually follows recruitment. What would you say if you would have a chance to start this process earlier and make sure you recruit right person at the same time? In case you think this sounds like a good idea this presentation might give you an opportunity to get some information on internship programmes run by Quanticate. Although this presentation is focused on progr amming, representatives from other departments might also find it useful. Interns are usually students or graduates full of energy and readiness to change the world. It provides additional challenges and opportunities at the same time. Some companies use Interns to perform simple tasks or use them as cheap resource to review existing processes and support teams working on improvement initiatives. It means they learn something and help company but not necessarily develop skills they would need in a day to d ay work. Within Quanticate we use approach focused on training and personal development		source presentation source	3
70	Inverse Probability of Censoring Weighting for Selective Crossover in Oncology Clinical Trials	José Luis Jiménez Moro, Javier Gómez Garcia	In Randomized Clinical Trials, selective crossover occurs when a patient randomized to one treatment arm changes to the alternative treatment during the study. This situation disturbs the integrity of the randomized comparison and creates a scenario of informative missing data where the estimated treatment difference for long-term endpoints, like overall survival or progression free survival, will be biased. The inverse probability of censoring weighting technique (IPCW) was designed to recreate an unbiased scenario where nobody switched to other treatment, and allows us to assess the real clinical benefit of the experimental arm compared with the control arm. In this paper we show how to perform this technique using SAS software and how it behaves compared with the traditional censored analysis	overall survival, treatment arm, logistic regression, regression analysis, logistic distribution	source presentation source	5
71	Is Your Clinical Data Standards Library Ready for Change? Adapting to TA Specific Standards	Lauren L. Shinaberry	The release of Therapeutic Area User Guides (TAUG) based on priorities provided by CFAST is an ongoing project; among the first user guides released are Asthma , Diabetes and Alzheimer's. The TAUG are critical as they give guidance on mapping therapeutic specific key data to SDTM. It is impor tant to understand the compatibility of your current standard library with the new TAUGs. Implementing standard libraries in an integrated metadata management tool enables you to have an overview of your libraries and manage maintenance. This paper provides an overview of the current, ongoing and planned TAUGs from CFAST. A description of the process of the TA standards development is presented as this supports the discussion of the approaches to organizing your global and TA standards libraries. A brief d iscussion of the historical progression of methods for storing metadata is given as well	relational database, data standard, cdisc	source presentation source	6
72	Japanese Submission/Approval from Programming Perspective	Ryan Hara	Japan is one of the world's biggest pharmaceutical markets and as such, development and approval of new drugs in Japan is one of the top priorities for pharmaceutical companies. The intent of this paper is to present Japan-specific submission requirements and also the approval process of the Japanese health authority PMDA (Pharmaceuticals and Medical Devices Agency) to a non-Japanese audience, especially programmers. Thus, the main focus of this paper is the specific programming requirements for a submission/approval in Japan that may differ from global processes. This knowledge may help global teams to better understand the specificities of Japanese pharmaceutical market and in turn, strengthen collaboration of global groups with Japanese teams and colleagues	data science, adverse events	source presentation source	5
73	Learnings and Evolutions of Risk Assessment Quality Control within Statistical Programming Group	Timothy Barnett	At the PhUSE conference of 2007, a Roche colleague presented on the "Validat ion of Programs developed using SAS", focusing on the implementation of a new Risk -assessment approach and highlighting the effectiveness and efficiencies an approach can have. This paper will look to review how the Risk-assessment approach evolved over the last seven years, with changes in industry and programming landscape as well as our understanding of risk, to its current format being used in Roche today. Such areas to review will be the incorporation of smart risk within the business, the requests for transparency and closer inspection from the outside world, learnings we took from an idealized risk approach to the physical implementation within the programming group, and where risk assessment is heading for future iterations	data cleaning, quality control, clinical programming	source presentation source	5
74	Lessons Learned by Comparing "using SAS" as and Epidemiological Researcher and "using SAS" as a Statistical Programmer	Joris Derks	In 2013, I made a career switch. From being an epidemiological researcher I switched to a career as a statistical programmer for OCS Consulting, a consultancy firm. Although there are many similarities between the two, the major one being the use of SAS software for research analyses, I soon noticed that there are many differences between the two. Several aspects of my new job can be really useful for my former colleagues, epidemiologists, and my background in epidemiology helps me do my work as a statistical programmer. In this paper I will share some background information on both jobs, then zooming in on the differences and the lessons that can be learned	statistical programmer, life sciences, survival analysis, data science, clinical trial	source presentation source	6
75	Leveraging R And Shiny for Point and Click ADaM Analysis	Ian Fleming	Presentation	data visualization, edc, data cleaning, cdisc	source	10
76	Life as a New Programmer	Jeremy Gallien	Starting as a programmer in the pharmaceutical industry can be challenging. Whether it is your first job, you are a trainee or you come from another field; the difficulties are numerous and varied. This can lead to a lot of frustration, nervousness, and make your start a nightmare. I started work as a trainee, and I have now taken a look back at my time as a novice statistical programmer . I have summarized what problems I encountered and how I solved them. The aim of this paper is to help new starters in order to facilitate their beginning as a statistical programmer, to give tips and tricks and highlight common mistakes . To achieve this, I will present scenarios to illustrate problems encountered in the everyday life of a beginner	statistical programmer, pharmaceutical industry, data cleaning	source presentation source	3
77	Looking for SDTM Migration Specialist	Angelo Tinazzi	Despite CDISC became a widely accepted standard for electronic data submission with US -FDA soon no longer accepting non-CDISC submission and other regulatory agencies considering the same (e.g. the Japan PMDA initiated a pilot phase to assess CDISC submission requirements), many studies, not only the legacy ones, are still designed without having in mind the full CDISC-path (at least CDASH-SDTM from the eCRF design step). As a consequence migrating non-SDTM studies raw datasets to SDTM -compliant datasets is still absorbing a lot of sponsor resources. This paper discusses the implications/challenges of migrating studies to SDTM with practical and detailed examples from our experience. The goal is also to demonstrate that SDTM migration could be extremely challenging (and risky) especially with studies with complicated design either from the protocol or database design point of view (e.g	liquidity gap, data cleaning, sdtm domain, length statement, annotated crf, medical coding, reviewer guide, raw data, sample, cdisc	source presentation source	12
78	Managing the Change - Evolving Statistical Programmers to Clinical Data Scientists	Sascha Ahrweiler		data science, statistical programmer, clinical trial, clinical research, clinical programming, clinical data	source presentation source	10
79	MedDRA - Beyond that Basic AE Report. How SMQs and MedDRA Structure can Enhance Reporting	Pamela J. Giese		adverse events, system organ class, sample, clinical trial, proc freq	source presentation source	9
80	Medical Data Review and Exploratory Data Analysis using Data Visualisation	Joel Allen, Vinod Kerai		data visualization, clinical trial	source presentation source	4
81	Metadata Design for Analysis and Reporting	Gopinath Viswanathan, Giri Balasubramanian	PRA Health Sciences, based on its understanding of implementation guide published by CDISC for trial data, was able to convert them into metadata form in its flagship system EXACT. The metadata captured in EXACT was then organized to meet the needs of PRA's internal standards and also able to import sponsor specific metadata information as part of its process within EXACT or extend the PRA 's metadata too based on business needs. Using it's metadata repository named Global Metadata Repository' PRA was able to generate reports with consistent quality in less time	mock shell, cdisc	source presentation source	2
82	Metadata, Study Standards, Interoperability: Key Concepts Toward Compliance to FDA Guidance	Isabelle de Zegher	Management of metadata and master data is key for system and data integration. However these are ill defined concepts and there is a lot of confusion in our industry on what they mean, how to manage and implement them. As part of the Emerging Technologies working group with FDA/PhUSE, we started a project around "metadata definition". Its goal is to develop common industry definitions, supported by operational examples, around metadata, master data, controlled terminology and interoperability…. This talk will present the results of the project by focusing on questions such as: what is a study specific data standard versus enterprise metadata, what a metadata repository (MDR) is, how does metadata related with master data, what is controlled terminology and how to manage it more efficiently. The second part of the talk will focus the importance of these concepts while ensuring compliance with the FDA February 2014 guidance on electronic submission	master data management, controlled terminology, data standard, metadata management, clinical research, data integration, data governance, cdisc	source presentation source	3
83	Migrating Between SAS Clinical Standards Toolkit Versions 1.5 and 1.6. Is that an African or a European Swallow*?	Gene Lightfoot		autocall macro, dataset	source presentation source	6
84	Models, Metadata and Messaging: Exploring Standards Governance in the CDISC Era	Kenneth Stoltzfus	The number of sponsors establishing internal standards governance continues to grow as CDISC (Clinical Data Interchange Standards Consortium) standards evolve from regulatory guidance to requirement, prompting an emerging uncertainty about how to achieve standards governance that is tailored for each sponsor's situation. How does an organization find the proper balance between efficiency & innovation, facilitation & bureaucracy, and compliance & leniency? What are the standards management issues that prevent sponsors from determining scope, instituting processes, or leveraging the standards metadata? Having been immersed in standards on both sides of the pharmaceutical industry - sponsor companies and contract research organizations - I will represent 6 case studies in standards governance in order to explore the challenges and potential solutions for sponsors as they implement and subsequently manage these new standards as part of their clinical data processes	clinical trial, clinical data, data standard, regulatory submission, cdisc	source presentation source	6
85	Monitoring Clinical Trials with a SAS Risk-Based Approach	Laurie Rose	With global regulatory encouragement, the life sciences industry is gaining momentum to embrace a risk-based approach to the monitoring of clinical trials. Typical to pharmaceutical organizations, there is wide variation regarding the level and speed of adoption, methodologies used, and technology employed to enable the process. The industry has a rich history of leveraging SAS to ensure safety and efficacy of clinical trial data used for regulatory submissions, and SAS is well suited to extend its analytical approach to the operations side of clinical trials as well. This paper addresses the challenges related to aggregating critical data sources from both biometric and operational systems in order to assess study risk related to patient safety, data quality and the overall integrity and performance of the clinical trial	clinical trial, data cleaning, clinical data, bar chart, proc freq, data validation rules, outlier	source presentation source	9
86	Multiple Imputation: Better than Single Imputation in Pain Studies	Ashik Chowdhury	Missing data is one of the genuine challenges we face during analysis of clinical trials. The main impact of missing data is that it can infuse bias in results which reduces the chance of getting the appropriate interpretation. Hence proper knowledge of techniques for handling missing data is crucial. A c ommon method of handling this problem is by imputing missing values. There can be single or multiple imputations. While single imputation methods like LOCF or BOCF have been widely used, multiple imputation is of a comparatively recent origin but is g aining popularity. The main reason behind its popularity is it overcomes some of the drawbacks of single imputation. The current paper presents comparative effectiveness of each with the help of real life cases involving pain studies	imputation, missing value, multiple imputation, change from baseline, missing completely at random, proc step, missing not at random, proc mi, primary endpoint, clinical trial, least squares mean, treatment effect, sample, last observation carried forward	source presentation source	12
87	My Transition From Placement Student to Permanent Employee Within the Same Company	Robyn Rosser	Sharing my experience of returning to the industry after my Industrial Placement year and completing the final year of my BSc, the benefits of returning to the same company and therapeutic area. What had my placement year within the programming department at Amgen prepared me for? Additional training sessions I have benefitted from to increase my industry knowledge and an overview of the new challenges faced in my role during my year since returning	data cleaning, adam dataset, cdisc	source presentation source	4
88	NONMEM - A Programmers Point of View	Raghu Kishor Koukuntla	Non Linear Mixed Effects Modeling software called as NONMEM is a leading tool used for population PK/PD analysis. It is widely used to predict the effect of drug on the target patient population. The NONMEM software requires data in a very specific pre-defined format and includes a mixture of data components like dosing records, PK and/or PD observations, time independent covariates, time dependent covariates, lab covariates and may include covariates such as concomitant therapies. This paper will provide clear understanding of a NONMEM input dataset and helps as a reference document for NONMEM programming concepts	population pk, in=, imputation, elapsed time, pd, response variable, sample	source presentation source	12
89	Oncology Efficacy Endpoints	Sarah Berenbrick	I am going to give an overview of efficacy endpoints in Oncology studies. I will focus on RECIST (Response Evaluation Criteria In Solid Tumours), which is a method of assessing how solid tumours change over the course of a study. I will cover the measurements taken for RECIST assessments (Target lesions, Non target lesions, New lesions) and the possible outcomes, (complete response, partial response, stable disease, progressive disease) and what they mean - patient recovered, stayed the same or got worse. I will also cover how visit windows and censoring can be handled. How and why measurement methods are important. I will cover other endpoints relevant to oncology such as Overall survival, Objective Response Rate, Best overall response, Disease control at X weeks and quality of life	pd, procedures, complete response, clinical trial, standard deviation	source presentation source	3
90	Outsourcing Version 4.0a - The Changing Landscape of Sponsor's Expectation	Pooja Shinde, Sangeet Kumar			source presentation source	1
91	Overview of Hash Object	Swarnalatha Gaddam	This topic is intended to provide more exposure to beginner or experienced SAS programmers who are looking for alternative to data steps in SAS. The concept of HASH programming is similar to the definition of an array in SAS. Several SAS users have benefitted from HASH programming by considerably reducing the processing time for compound data merging tasks. We are going to discuss introduction to HASH programming, syntax, and a few examples which are more relevant to clinical industry. Also benefits on using HASH programming versus regular `DATA STEP` would be discussed	hash table, data step	source presentation source	8
92	Overview of SAS Proc Format and Code for Combining Formats Catalogs	Douglas Staddon		proc format, proc step, temporary dataset	source presentation source	14
93	Patchwork - A Creative Approach to Revise Existing Validation Methods	Anja Feuerbacher	The validation of generic scripts and macros can be a slow and laborious process, frequently seen as a hindrance to the smooth flow of implementing or changing software. With the knowledge that this is an essential part of the drug development process, the key step is the risk assessment of the impact the changes will have and this risk decision in turn leads into the depth of validation that is required. Over the lifetime of a script or macro, many regenerations of the code are likely to occur, each with their own validation requirements. This constant updating can lead to the validation packages containing a lot of unclear, inconsistent, duplicated, or even erroneous tests, so that the execution is very cumbersome and can take several days to complete	data cleaning, regression analysis	source presentation source	5
94	Population Segmentation in a Healthcare Environment	MaryAnne DePesquo	In this new era of Healthcare Reform (HCR) in the United States, there was a heightened emphasis on profiling individuals in the health insurance industry. The importance of understanding the characteristics of members for population analytics, healthcare economics, and marketing was a necessity prior to the enactment of the Affordable Care Act (ACA) and became more essential in the post ACA environment due to HCR mandates . The goal to provide an integrated 360 view of the individual would be beneficial for disease and care management programs, balancing population risk, increasing market share, providing wellness programs to improve member's needs and assisting the consumer to make informed choices about their care through quality and cost transparency. By profiling the individuals, an approach for care can be built for each member segment classification as predefined by specific attributes	proc step, cluster analysis, regression analysis, health insurance, sample, proc varclus, outlier, logistic regression, proc fastclus, correlation, proc varreduce, logistic distribution	source presentation source	5
95	Presenting SAS Data Sets on the Web Using SAS ODS, JavaScript and jQuery	Volker Harm	Inspired by the SAS `ODS` tableeditor tagset we look further how JavaScript and the jQuery library can be used to dynamically present SAS data sets on the web. Emphasis will be laid in sorting, filtering and editing data	ods html, dataset, sample, macro definition	source presentation source	13
96	PROC EASTMONITOR - A SAS Add-on for Monitoring Adaptive Clinical Trials	Sheetal Solanki, Ajay Sathe	Typical clinical trial workflow involves creation of trial design by sponsor statisticians, statistical analysis by statisticians at the Independent Statistical Center (ISC) and decision making by the DMC. In a group sequential / adaptive trial, statisticians typically use custom SAS macros to analyze patient data at interim analyses and produce required reports. The product East PROCs consists of `PROC` EASTMONITOR which can be used by ISC statisticians to perform interim analyses of subject data based on group sequential / adaptive methodology identified in the protocol in a statistically safe manner. It takes as input the design produced by the East system and summarized subject data. It computes test statistic, performs a hypothesis test and produces a recommendation for early stopping or continuation of the trial. It also produces statistical inference about the treatment effect	proc step, sample, sample size, interim analysis, clinical trial, effect size, confidence interval, proc calis, group sequential design, treatment arm, adaptive design, treatment effect, out=, two-sample test, type i error, conditional power, standard deviation, dataset	source presentation source	15
97	Programming and Statistics in the Academics Environment	Arne Ring	Presentation	data wrangling, clinical trial, data science, statistical programmer, regulatory reporting, clinical programming, clinical data	source	21
98	QC: Manual vs Program - A Personal View	Helen Nicholson	When producing any output we need to know that it is right. It does not matter if the output is to be used by another department, or are the final tables, figures and listings for the clinical study report. Unless the output is produced from a fully automated system, the outputs need checking in some form every time they are produced to ensure that they are correct. Normally this will involve a method of programming which will re-produce the data from the output in some form which can then be compared against the original. This poster will look at some of the most common ways of doing this comparison, either by eye or programmatically and present the pros and cons of each method	out=, proc step, permanent dataset, proc report	source presentation source	6
99	R and Revolution R Enterprise in Regulatory Environments	Eike Nicklas, Neil Miller	R, the open source environment for statistics, has been adopted as a data analysis tool by many pharmaceutical companies. Its free license, noncommercial background and fast growth offer many advantages, but also pose challenges to the use in business IT and regulatory environments in particular. Revolution R Enterprise, an R-based platform by Revolution Analytics, offers validated revisions, commercial support and scalable high-speed computations on multiple servers and thus facilitates the use of R in clinical programming and generally in the pharmaceutical industry. In this contribution, Revolution Analytics and HMS as a service and solution provider introduce R, discuss options for its use in regulatory environments and compare open source R and Revolution R Enterprise in the clinical context. In addition, we discuss tools that support the validation of custom scripts as well as common approaches to quality assurance topics such as unit testing	data cleaning, clinical programming	source presentation source	4
100	R for SAS Programmers: It's Different, but Friendly	Friedrich Schuster	In recent years, a large number of pharmaceutical companies have adopted R as a data analysis tool. The main reasons for the increasing importance of R are the availability and ever -growing number of high -quality statistical methods, very good graphics capabilities and a wide range of useful programming extensions. This presentation addresses SAS users who are n ew to R and want to learn more about R. It gives a brief overview of the characteristics of R as a programming language and as an environment for statistical computing. Some particularly important differences between R and SAS will be highlighted. Small co de examples illustrate solutions for common SAS data management and analysis tasks. It also points to further sources of information, such that participants will be able to continue learning R on their own by the end of this presentation	proc step, missing value, dataset, anova, homoscedasticity, proc glm, data governance, box plot, data step, proc genmod	source presentation source	10
101	Reengineering a Standard Process form Single to Environment Macro Management	Guiseppe Di Monaco	Statistical programming departments in pharmaceutical companies often have to manage huge macro libraries in an efficient and controlled way. On the one hand, it is important to deploy the macros in a flexible and user friendly way. On the other hand, macros and related documentation require an efficient method of validation and updates. With that standard, organizations in the pharmaceutical industry face the exact same challenges as any other software development house. In my presentation, I will demonstrate the benefits of adopting the Environment Macro Management approach and related reengineering steps that are needed	regression analysis, data cleaning	source presentation source	10
102	Representation of CDISC Foundational Standards Using RDF Authors	Scott A. Bahlavooni, Frederik Malfait	Over the past two years the PhUSE CSS Semantic Technology (ST) project has made considerable progress to represent existing CDISC standards in RDF, the established web standard for semantic technology, based on a ISO 11179 type meta-model. RDF representations are available for CDASH, SDTM, SEND, ADaM, and their controlled terminologies; a CDISC public review for these deliverables is in progress for the fourth quarter of 2014. The presentation will give an overview of the different models, how they relate to each other, and how they can be used to manage data standards in an ISO 11179 type Metadata Registry	controlled terminology, data standard, cdisc	source presentation source	3
103	Risk based monitoring using integrated clinical development platform Authors	Sita Rama Swamy Peddiboyina, Shailesh Vijay Joshi		clinical trial, clinical research, raw data, missing value, data integrity, data cleaning, data standardization, outlier, adverse events	source presentation source	9
104	SAS in Clinical Trial - A Relook at Project Management, Tools and Software Engineering	Sameera Nandigama	Traditionally, SAS has been used as a standalone application for data analysis and projects are managed using spreadsheets and e- mails. Recent technological and software developments have now moved from a standalone application to an integrated statistical computing environment fitting into the workflow of a develo per. However, there is a wide gap between the available tools for project management and those that are currently used. This gap is also visible in the design, development, testing and documentation of code in statistical programming. Hence, with the shift of the industry from a local application to a highly integrated and regulated software environment, the need for better use of available tools and refined processes is called for. In this paper, the author discusses tools such as JIRA and Doxygen and also processes such as Agile and Waterfall. All of which can improve control, efficiency and focused visibility at all levels	clinical trial, sas programmer, macro definition, data science	source presentation source	12
105	SAS Visual Statistics	Andy Bayliss	Presentation	regression analysis, logistic distribution	source	18
106	SDTM in Business Intelligence	Mike Collinson	The SDTM standard is widely used for data storage and pooling. This paper looks at the use of SDTM data for cross study analysis using Business Intelligence tools, starting with an introduction to warehousing principles. It covers the steps required to use SDTM domains as dimensions in a star schema, allowing the data to be used for user-driven creation of powerful ad-hoc reports and provision of interactive dashboards	star schema, fact table, sdtm domain, business intelligence, data warehouse, cdisc	source presentation source	3
107	SDTM Mapping Becomes more EASY with SAS Clinical Data Integration	Saumilkumar Tripathi	CDISC SDTM (Study Data Tabulation Model) is recommended as the standard data structure to be used for submitting tabulation data to the US FDA for clinical trials. Approach for implementing such data standards should be quick and effective with less resour ce utilization. SAS Clinical Data Integration is the solution from SAS which helps user to organize, standardize and manage clinical research data and metadata in compliance with CDISC SDTM or company specific internal standards. This paper will present the Graphical User Interface (GUI) approach compared to the conventional programming for SDTM mapping, performing CDISC Compliance checks and preparation of CRT -DDS define.xml which highlights the usage and unique features of SAS CDI. It also includes automation of such data integration tasks to gain speed and efficiency, different ways to check domain completion status, comparing metadata properties of domains across the studies and many more	sdtm domain, sas base, sas programming, proc sql, clinical data, data cleaning, data transformation, clinical trial, data integration, raw data, sas expression, sdtm programming, data merging, proc step, excel, cdisc	source presentation source	8
108	Signal Detection of Misconduct in Clinical Trials	Geoffrey A. Mann, Richard C. Zink		clinical trial, volcano plot, multiplicity, vital signs, data cleaning, demographics domain	source presentation source	7
109	Simple and Efficient Matching Algorithms for Case-Control Matching	Berber Snoeijer, Edith Heintjes	Epidemiological studies comparing outcomes between patient groups with or without certain exposures to treatments or risk factors are prone to bias, caused by differences in patient characteristics. To allow drawing reliable conclusions from such studies, patient groups should be exchangeable. To achieve this, matching exposed patients (here called cases) to reference patients (here called controls) based on important characteristics may help to ensure exchangeability. In studies with rare outcomes a case control design may be more opportune, where patients with the outcome (cases) will be matched to patients without the outcome (controls) based on predefined criteria. Matching criteria can be either exact class variables (e.g. gender, specific comorbidity) or continuous variables which should be matched wit hin a certain range (e.g. age, propensity score)	proc sql, propensity score, proc step	source presentation source	6
110	Small Sample Bayesian Factor Analysis	Dirk Heerweghm	Factor analysis (FA) is a statistical technique to explain the correlation structure among observed variables. It can e.g. be useful in the analysis of Patient Reported Outcomes, which typically use multiple questions to measure a single concept. Maximum likelihood factor analysis (ML-FA) relies on large sample theory, and it is co nsequently often recommended to use it only in large samples ( e.g. N=200 or more). In smaller samples, ML-FA can run into problems (e.g. model non-convergence, negative resi dual variances). Bayesian statistics typically perf orm better in small samples, and may therefore be useful in (clinical) studies that rely on smaller sample sizes. This paper provides a gentle introduction to Bayesian FA and shows how it can be implemented in Mplus 7.0. The paper then shows the relative performance of Bayesi an FA as compared to ML-FA for two CFA models: a on e- factor model with four factor indicators, and a two-factor model with 3 indicators for each factor	factor loading, sample, sample size, correlation, factor analysis, power analysis, normal distribution, monte carlo, covariance, prior probability, maximum likelihood estimation, bayesian statistics, likelihood function, model training, estimate, proc calis, missing value	source presentation source	18
111	Spotfire for Clinical and Translational Medicine	Gerard Conway	Learn about the TIBCO Spotfire Clinical software and how a focus on standards allows us to go from STDM to analysis and review in minutes, leveraging out-of-the-box templates. In addition hear how PerkinElmer, Inc. is broadening data access to include omics and related data to enable translational analysis in a single environment. Note: Safe Harbor. This document shows current intentions regarding product features, behaviour, schedules and support; these intentions may change without notice as we respond to customer requirements. Any unreleased products, services or features referenced herein are not currently available and may not be delivered on time or at all. Customers who purchase PerkinElmer's Informatics applications should make their purchase decisions based upon products, features and services that are currently available	clinical data, clinical research, data science, data standard	source	3
112	Standards Driven Innovation	Frederik Malfait	Presentation	data validation rules, data cleaning, adverse events, data standard, clinical trial, data science, medical history	source	36
113	Statistics and Computing for Health A 10-year Bachelor of Science in South Brittany	Jean-Francoius Petiot, Cedric Frambour	Presentation	clinical research, survival analysis, health insurance, clinical programming, discriminant analysis, logistic regression, relational database	source	17
114	Study Data Handling: A CRO Experience	Paul Crean	As a CRO ICON receives data from multiple systems (internal and external) and vendors, processes it, and delivers it in a variety of formats. In recent years, ICON is delivering outputs to data consumers earlier in the study, more frequently, and in a greater variety of formats. This paper will describe how the process of data handling at ICON has changed in light of these requirements, and the tools implemented to enable study teams to have a clearer picture of data processing in a study and to alert them of any potential data processing issues	raw data, data wrangling	source presentation source	6
115	Submitting Summary Level Site Data: Overview, Implementation and Case Studies	Robert F. Woolson, Jeffrey M. Abolafia, Ben Vaughn	In December 2012 the US FDA issued draft guidance for submitting Bioresearch Monitoring (BIMO) Summary Level Site Data for the Center for Drug Evaluation and Research's (CDER) Inspection Planning. The guidance calls for submitting aggregate results-level data for each site enrolling subjects in pivotal trials in a clinical development program, in addition to the subject-level data we currently submit. The FDA is beginning to request this data and the time and resources required to create them are not trivial (3). In the past year our organization has received several requests from the FDA for Summary Level Site Data (BIMO dataset). This paper presents an overview of the BIMO dataset and implementation issues that arise from its creation. The paper divides the discussion into four major sections. The first section provides an overview of the guidance pertaining to BIMO data. The second section describes the input sources and processes that are required to create the BIMO dataset	raw data, clinical research, data cleaning, phase iii, primary endpoint, phase ii, treatment arm, sample	source presentation source	9
116	Successfully Embracing Challenges to CDISC Standards	Elke Sennewald	Presentation	clinical research, data cleaning, serious adverse event, adverse events, clinical trial, statistical programmer, clinical programming, cdisc	source	30
117	Technology Trends with the Potential to Change Clinical Development	Christopher R. Olinger	The current clinical development process is fraught with anachronisms and outdated technologies. This poster explores some of the trends in technology from an edge-dweller's perspective: leveraging unstructured data models, open source analysis and reporting tools, semantics and metadata driven content, source repositories, and PaaS and Cloud-based deliveries. It should be possible to modernize clinical process development by utilizing these platforms, yet the industry is slow to adopt (or adapt) to them. The outside world is moving forward, and clinical process designers must find a way to move forward as well or forever be r eliant upon costly and outdated techniques. The companies that find a way to incorporate these next generation ideas stand the best chance to move their organizations forward	pharmaceutical industry, clinical research, clinical programming	source presentation source	5
118	The Capish Information Model - Simplify Access to Your Data	Anna Berg, Catharina Dahlbo	Opening up clinical data requires the ability to easily access and analyze data, irrespective of its location or the application that created it. In order to achieve this goal, data needs to be integrated from different sources and mapped to a clear and understandable common data framework. With unstructured data becoming more and more prevalent in clinical research, it becomes a prerequisite that the data framework has the ability to include all kinds of data, e.g. documents, comments, images etc. Capish has developed such an information model constituted of small messages of information, which are linked together by pre-defined relations. On top of this model is a well-defined terminology with the possibility to include existing standards and terminologies. In this paper, the opportunities for creating clinical data transparency by integrating data to a well-defined, source-independent information model will be discussed	clinical data, clinical trial, relational database, data standard	source presentation source	8
119	The On-Going Evolutionary Changes of our Industry	Christian Mueller	Presentation	clinical research, clinical trial, data analyst, csv, data governance, adam dataset, big data, cdisc	source	34
120	The PHUSE Therapeutic Area Wiki Page	Jothy Kalathiparampil, Oliver Wirtz	Therapeutic Area (TA) knowledge is important for all people in the biometric area. In preparation of the patient centric PhUSE annual conference 2013 in Brussels a small group of volunteers started to gather information about Rheumatoid Arthritis and Oncology. This project group collaborated through the PhUSE Wiki to increase the awareness and to prepare workshops for the conference. Today the PhUSE TA Wiki is not only a useful tool for beginners but it can be also seen as a tool for future development to improve the way professionals in the different TA's work. Several updates in the TA Wiki Page were done since its launch	clinical trial, data standard	source presentation source	2
121	The Quality System in Good Pharmacovigilance Practice (GVP)	Veronique Chapalain		sample, quality control, clinical trial, pharmaceutical industry, sample size	source presentation source	9
122	Time to Event Analysis	Michaela Mertes	Time-to-onset of an event will be analyzed using an un-stratified log-rank test. With the help of an example dataset the calculation will be explained step by step. This will be supported with output and graphs from SAS 9.2. Further example outputs added from other graphic providers (TIBCO Spotfire and Shiny R)	rank test, log-rank test	source presentation source	6
123	Traceability: Plan Ahead for Future Needs	Sandra Minjoe	Can you determine which records from which dataset(s) and what procedure(s) were used to create a table? Do you know how analysis values and records were derived from collected data? Do you know where a field from a CRF can be found in the data? Put another way, can you trace your data, from collection, through each dataset, and onto your output tables? Traceability is something that, if not built into the process of data and output development, can be difficult to add later. However, when traceability is included, it helps streamline the development of programs, enables efficient quality control, assists in future analysis needs, and allows for easier review. This paper describes some simple ways to incorporate traceability into the dataset and output development process, and elaborates on some of the benefits seen when traceability is incorporated	adam dataset, reviewer guide, label statement, clinical trial, cdisc	source presentation source	8
124	Transparency in the Time of Constant Change	Todd Case		clinical trial, crf	source presentation source	4
125	Understanding that Data Transparency Matters: The Academic View	Sarah Nolan, Catrin Tudor-Smith	Presentation		source	19
126	Unuttered Questions of Statistical Programmers	Aparajita Dey	We know that all the bright folks from various backgrounds, be it medical, engineering, pharmacy, biotechnology or statistics, enter our world of clinical programming. Together they are called 'statistical programmers'. Data issues to complicated coding, a mbiguous specifications to stringent timelines, nothing can beat their enthusiasm. The only thing they hesitate to do is to ask a particular question - "why?" Whenever they are asked to provide standard error instead of standard deviation; whenever they ar e suggested to use different formulae each time they calculate p - value; whenever they are asked to plot mean and confidence interval in the errorbar graph instead of usual mean +/ - SE plot; they follow the instructions immediately and deliver the output in a quick and efficient manner. Though "What to do" and "How to do" have become common questions now; "Why to do" is still something very rarely asked	sample, null hypothesis, standard deviation, clinical trial, proc calis, confidence interval, normal distribution, sample size, qq plot, proc means, histogram, statistical programmer, proc mixed, pk parameter, adpp, safety population, alternative hypothesis, probability distribution, significance level, analysis set, data science, population	source presentation source	10
127	Update on Regulatory Guidance: Data Standardisation Planning for Clinical Development Programs	James R. Johnson	Data Standardization within and across clinical development programs is now a requirement as Regulatory Authorities worldwide are all requesting that clinical data, and metadata, be made available in a structured and standardized CDISC compliant architecture. The development and evolution of a Clinical Development Plan (CDP) is a critical document and process for cost effective and efficient drug development programs. The CDP is now more important than ever with the integration of a data standardization into the CDP architecture. Recent FDA Guidance (February 2014) on Providing Regulatory Submissions in Electronic Format - Standardized Study Data and corresponding Technical Conformance Guidelines identified that a Study Data Standardization Plan (SDSP) should be submitted with an IND to outline standards to be followed throughout the course of the development program. In this paper we present a high level model for an SDSP with a specific focus on endpoints	clinical research, phase i, phase ii, data standard, phase iii, proof of concept, clinical trial, clinical data, data standardization, cdisc	source presentation source	10
128	Using CDISC Validation Tools in a Validated Hosted Environment	Sandeep Juneja	Regulatory submission agencies worldwide request th at submission data be delivered in CDISC format. Va lidating submission data to confirm that it adheres to CDISC standards is a big challenge, but it is a vital st ep in the pre- submission process because regulatory agencies run compliance checks against the submission data that they review. Validation tools like SAS Clinical Standards Toolki t (`SAS/CST`) and OpenCDISC Validator can be easily o perated in open environments by running SAS Macros and by issu ing operating system commands to invoke the Java executable JAR files, respectively. However, users of validated hosted environments - like SAS Drug De velopment - are not allowed to issue operating system commands or access the SAS installation location, and requir e another way to execute the Validation tools	macro definition, clinical research, data cleaning, excel, cdisc	source presentation source	5
129	Using SAS Arrays	Ruurd Bennink	This paper shows 5 situations on which the use of ARRAYs is quite practical, and in my personal opinion the most appropriate. Each purpose is explained by one or more examples of actual SAS code. All examples are gained from daily life experience as a statistical SAS programmer in the Life Sciences industry. The reason that I came up with this topic for the PhUSE Conference is that I'm quite impressed by the elegancy of using ARRAYs in SAS since the day I started as a SAS programmer almost 14 years ago. When I started I was trained on the job and my tutor at the time also emphasised that ARRAYs are such a nice tool of SAS. It is since then that I first seek a solution by using ARRAYs in all of my programs	do loop, change from baseline, in=, clinical trial, proc step, numeric variable, out=, sas programmer, data step, macro loop, proc sql, array	source presentation source	7
130	Welcome to the New World of Data Transparency	Chrissie Fletcher	Presentation		source	35
131	What Makes one Project Successful and Another Stressful?	Colin Brown		clinical research, proc standard	source presentation source	5
132	What's Next in Version Control for SAS Programming Environments?	David J. Garbutt		clinical programming, sas programming	source presentation source	11
133	Why Don't You Define It?	Nick De Donder, Anne-Sophie Bekx	Define was first released by CDISC as version 1.0 in 2005 and has been an integral part of FDA submissions ever since. However, the Define.xml has greater potential than to be used for submissions only. It could potentially allow study metadata to be used for improving study design and database set-up, standardizing metadata between CROs, vendors and sponsors, it can be checked against database structure and content for validation purposes and is an integral part of any FDA submission. Indeed, in recent years BDLS has noticed that sponsors are increasingly moving towards a metadata-based approach of data management (data collection and ultimately conversion to	data governance, error message, cdisc	source presentation source	7
134	WISE a World Wide Integrated SAS Environment for Today and Beyond	Alain Vasseur, Christelle Le-Beaudour	Presentation	audit trail, sas enterprise guide, 21 cfr part 11, big data	source	22
135	Work-Life Balance: Making it a Reality	Manjusha Gode, Ajay Sathe	The era has ended when people had a fixed and limited number of hours and their own skill set which was adequate for providing job satisfaction and money for their entire life time. This was working out so well that - 'How do I find out time for my family, hobbies and interests and most importantly for myself?' - was a question faced by not many. Well, this has now become a 1000 dollar question for today's working population. Where Hurry, Curry and Worry have become an inevitable way of life, it has become critical to find an answer to this question. In this paper, authors talk about the practical experiences over a number of years, what it takes to ensure that the proper work-life balance is achieved and maintained , and some tips about how to achieve it - again based on authors' own experience		source presentation source	8

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