PHUSE EU 2021 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A Beginner's Guide to Data Analysis	Shuva Dey		adam dataset, data science, proc means, clinical trial, clinical data, confidence interval, nominal variable, efficacy summary, phase i, clinical programming, proc freq, cdisc	source video	15
2	A case of mistaken ID: reimagining rescreenings and reenrollments	Matt Metherell	When a subject is able to re-enroll across multiple studies within a single project, it can be difficult to keep track of this one subject while maintaining CDISC compliance and making it clear which observations were recorded pre- and post-rescreening. If we need to copy observations between studies as well, this can multiply the complications, and the guidance currently in place could be improved. This paper proposes the solution we arrived upon for a three- study project with multiple rescreenings within and between each study. We are proud to suggest a new variable: SUBJIDC, Subject Identifier as Collected	sdtmig, proc compare, cdisc	source video presentation source	5
3	Accelerating Beyond Excel: Mapping Specifications in an Intelligent Web-based GUI	Nathan Johnson	Presentation	excel	source video	41
4	Adapting Git to Novo and Novo to Git	Christian Olsen	Presentation	statistical programmer	source video	40
5	Advanced Excel Specification for Automatic SDTM Generation	Aleksandra Gorbunova, Ilya Mishchenko	Being one of the most important parts of clinical trials submission to regulatory authorities SDTM gives significant advantage to all stakeholders via providing a predictable and clear structure for data accumulation and further analysis. Comparing to ADaM or TFLs, SDTM seems to be a less sophisticated part of deliverables with defined non-extensible types of domains and variables. Also SDTM requires just few and straight-forward derivation types from raw data. We propose a way for automation of SDTM generation by extending standard `EXCEL` specification to a structure acceptable for both human and machine interpretation and implementation. We describe the self-sufficient `EXCEL`-document architecture for automatic generation of SDTM domains with additional variables and sheets for SAS-code instructions and external metadata	excel, macro loop, macro quoting function, data step, adverse events, sdtm domain, macro definition, raw data, clinical trial, proc step, adam dataset, cdisc	source video presentation source	10
6	AI-powered Drug Development: Accelerating Trials with AI & ML	Raj Indupuri	Presentation	data cleaning, clinical trial, data governance, data pipeline, data science, machine learning, data standard	source video	10
7	An Ongoing Modernisation Journey at Orion Pharma	Mark Lambrecht, Olavi Kilkku	Presentation	sas viya, data science	source video	15
8	An overview of regression approaches for the competing risks data	Oksana Mykhailova, Julia Ditkovska	Time-to-event data are used in clinical trials for investigating time from the start of the treatment till the time of death or the time till full recovery. The classical survival analysis proposes strong methods for working with time-to-event data. These methods work well when there is one researched event for the subject. Sometimes as a part of efficacy analysis on the study, we would investigate time from the start of study till death due to progressive disease, which can be concealed by other reasons of death. In the case subject at risk by few different types of events, these different types are called competing risks. In the paper, we have prepared an overview of regression approaches for the competing risks data. These methods are used for estimating the effect of the covariates on one of the measures (the cause-specific rate, the subdistribution hazard rate). SAS 9.4 was used	competing risks, pd, regression analysis, cause-specific hazard, proc phreg, hazard ratio, survival analysis, hazard function, proc step	source video presentation source	6
9	Augmenting Therapeutic Effectiveness through Novel Analytics (ATHENA) - A Public-Private Partnership Project Funded by the Flemish Government (VLAIO)	Ploingarm Petsophonsakul, Wouter Botermans	Presentation	data governance, machine learning, data cleaning, raw data, clinical trial, data science	source video	14
10	Automated creation of a mapping specification for a pooled database	Thomas Wollseifen	Provision of SDTM/ADaM data as part of integrated analyses is an important part in preparing the submission of data and reports from clinical studies to authorities. Integrated databases could change over time. I would like to introduce a SAS macro that is used for the creation of a mapping description of several studies that also takes changes to the metadata / data over time into account. A simple macro call creates an `EXCEL` file based on the study data and metadata from the pooled database. Mapping sugge stions and information from the content of variables is included in the mapping description. Earlier descriptions of studies are automatically integrated into the `EXCEL` file. Changes to the metadata are also taken into account. The completed mapping description can finally be used to create a define.xml. It also serves as a template for creating the mapping programs. Mapping descriptions are an important part of the creation of pooled databases	excel, macro call, clinical trial, raw data, sas function, macro definition, cdisc	source video presentation source	12
11	Automated Testing of the Programming Workflow Using Cypress	Darwin Hadley	Presentation	pharmaceutical industry, type i error, bootstrap, proc freq, regression analysis	source video	15
12	Automation of Process Flow Instances	Caro Sluijter	One of the notable features of SAS Life Science Analytics Framework (LSAF) is the workflow capability. This functionality allows you to automate processes by using process flows; to allocate tasks and to keep track of the progress of tasks and activities. A process flow is particularly useful when a process consists of several standardised tasks that need to be executed routinely and chronologically within or across studies. Process flows can thus eliminate repetitive tasks. However, setting up process-flow instances can in itself be a repetitive task, because for all process flow elements the details need to be selected manually. As process flows are meant to reduce or eliminate repetitiveness, we also wanted to remove repetitiveness in creating the Process Flow Instance. In this paper we will demonstrate how LSAF API macros can be used to set up a Process Flow Instance in an automated fashion requiring as little as one or two parameters	macro variable, life sciences	source video presentation source	8
13	Balancing Automation and Flexibility in Programming Processes	Helen Nicholson, Punitha Philip	Many technical solutions and systems these days are built around end-to-end standardization, generating efficiency through consistency from one study to the next. However, this is not always completely feasible for CROs with small to mid-sized clients. In this case there may be distinct requirements or interpretation of standards, not always fully utilizing CDASH. This can result in challenges in completely standardizing metadata and hence the production of automated systems. This paper describes some of the methods Cmed have used to build adaptable solutions, striking the balance between automation and flexibility, avoiding the restrictions of a rigid system. Examples include making effective use of SAS to semi-automate program generation for SDTM and ADaM datasets	sdtm domain, data cleaning, cdisc	source video presentation source	5
14	Best Practices: Analysing Classical Clinical Trial Data with ML Methods	Uwe Pritzsche	Presentation	correlation, data science, regression analysis, csv, clinical data, proc phreg, feature selection	source video	10
15	Beyond P21 Data Checks	Abdulkadir Lokhandvala	Presentation	macro catalog, excel, macro call, out=, variable list, proc contents, flag variable	source video	24
16	Bringing to Light - Analysis Results Metadata v1.0 for Define-XML v2	Hrishikesh K S, Limna Salim	Presentation	least squares mean, change from baseline, proc calis, treatment effect, confidence interval, inferential statistics, excel, cdisc	source video	11
17	Building a Scalable, Machine-learning-driven Anonymisation Pipeline for Clinical Trial Transparency	David Di Valentino, Muqun (Rachel) Li	Poster		source video	1
18	Building an SCE that Users Want to Use	Alastair Scarlett, Chris Decker	Presentation	data science, clinical programming, clinical data, clinical trial, statistical programmer	source video	9
19	Business Outcomes: What's Love Got to Do with It?	Marcus Plating	Presentation		source video	33
20	Case Study: Automating a Complex Data Migration Using SCE API	Guy R. Garrett	Presentation	data cleaning	source video	6
21	CDISC Analysis Results Standard Team: Enhancing Analysis Results and Traceability	Bess LeRoy, Jeffrey M. Abolafia	Presentation	change from baseline, adverse events, baseline characteristics, clinical trial, data standard, medical history, safety population, cdisc	source video	41
22	Clinical Design - Visualisation and Analysis Using the Interactive Application by R Shiny	Athira Sudhakar, Mahesh Divakaran	Shiny is an open-source R package, provides a great framework for interactive analysis and visualization of data using R. It has great power to make the design and interactive analysis, so it acquired popularity among R users. These applications are used by non-programmers to perform analysis, therefore, utilize the statistical capacities of R by naive clicks. So, its utilization has been on the rise over a couple of years. Through the paper, we demonstrate a Shiny application example in clinical trial design and data review. The application is aimed to visualize and analyz e clinical trial-related tasks such as calculate sample size, calculating the probability of study success based on all kinds of information in a Bayesian framework, and visualization of ADaM datasets in a more efficient manner. In the future, Shiny apps will help in dynamic dataset creation, report making as well as visualization of clinical trial data	sample, clinical trial, clinical data, logistic regression, logistic distribution, regression analysis, inferential statistics, adam dataset, sample size, box plot, cdisc	source video	7
23	Code Sharing is Caring	Lucy Palmen	Presentation	adam dataset, pharmaceutical industry, sdtm programming, clinical programming, cdisc	source video	16
24	Comparison of PROC REPORT SAS and FLEXTABLE Package of R	Abu Zafar Mohammad Sami	Presentation	proc report, hazard ratio, proc step, confidence interval, proc phreg, output delivery system, forest plot	source video	23
25	Consideration for Plain Language Summary (PLS) Development and Patient Engagement: A Tool for Patient, Caregiver and Clinician Review of PLS and Beyond	Theresa Shalaby	Poster		source video	1
26	Creative Data Science Using SAS Open Source Integration	Serena Faruque, Stijn Rogiers	Poster	sas viya, sas studio, data science	source video	1
27	Data Engineering - An Emerging Skill for Today's Statistical Programmers	Axel Muehlig, Sangeeta Bhattacharya	Presentation	data science, clinical data, data pipeline, data wrangling, banking, biomarker, clinical endpoint	source video	18
28	Data-driven Decisions: How an Increase in Data Demand is Driving the Creation of New Platforms Focused on Non-data Natives	Trevor Standish	Presentation	life sciences, data science, clinical data, data analyst	source video	10
29	Deep Active Learning for Clinical Entity Recognition	Nadia Abraham	Presentation	f1 score, sample, deep learning, model training, neural network, test set, training set	source video	33
30	DEEP Presentation and Launch? : New Era of Collaboration - DEEP Enables an Ecosystem Approach to Advance Digital Medicine	Bert Hartog, Kai Langel	Presentation	data cleaning, clinical research, phase i, phase iv	source	30
31	Demystification of Cloud	Marcus Plating, Roger Chivers	Poster		source video	1
32	Design Your Future Path with Professional Development (PD01, PD02 & PD03)	Emily Jones, Henning Pontoppidan Föh	When the time comes for a programmer to transition into a new project, either within their current organization or at another, it is in the interest of both the current programmer and future programmer to ensure a smooth transition. This paper gives guidan ce on key points to be considered and pitfalls to be avoided as a programmer for internal or external transition. An example of transition plan will be presented and will cover responsibilities (Ongoing and upcoming work assignments ), list the daily tasks, timelines, team members, expectations, and instructions to name a few. These attributes need to be tuned to ensure maximum synergies with minimal disruption in daily routines. Close insight of current programmer's work and projects is required so that suc cessor is confident to complete the project successfully. Methods for effective transition management are discussed and lessons learned documented, which would immensely help all lead programmers	statistical programmer, clinical programming	source presentation source	5
33	Design Your Future Path with Professional Development (PD01, PD02 & PD03)	Emily Jones, Henning Pontoppidan Föh	Presentation	statistical programmer	source	11
34	Design Your Future Path with Professional Development (PD01, PD02 & PD03)	Emily Jones, Henning Pontoppidan Föh	Presentation		source	16
35	Differences in Performance Between the Options KEEP and WHERE as Compared to the Statements KEEP and IF in SAS Data Steps	Ruurd Bennink	In general, it is assumed that keeping variables while reading in a SAS dataset (via the KEEP option on the `SET` statement) is more efficient than applying a KEEP statement within the SAS `DATA STEP`. It is also assumed that selecting observations while reading in a SAS dataset (via the WHERE option on the `SET` statement) is more efficient than applying the IF statement. This paper will look into the difference in performance of using the beforementioned options and statements by applying them in several scenarios. Is there really a big difference in performance? Or is it negl igible?	keep statement, set statement, if-then-else, data step, character variable, numeric variable, sas log	source video presentation source	8
36	Do's and Don'ts for PRCI Submissions	Parvee Kumar	Poster: Clinical Documents publishing is mandatory for any submissions to Health Canada. Hence, it is necessary to standardize the process of anonymization within organization ad across the industry. This will help in reducing the time, cost and efforts needed for this additional activity	adverse events, clinical data	source video	1
37	Does No Errors Equal High Quality?	Julie Solbjerg Appel, Sanna Herrgård	Presentation	audit trail, missing value, outlier, clinical trial, data cleaning, primary endpoint, raw data	source video	23
38	Easy Parallel Processing in SAS/BASE: A Case Study	Anna Socha, Paul Wicks	Presentation	parallel processing, stage 1, sample, confidence interval, in=, proc step, bootstrap	source video	17
39	Electronic Study Data (eData) Submission Experience to the PMDA and Collaboration between the Japan Submission Team and the Global Project Team	Manabu Uehara, Shubhramoy Mandal	Presentation		source video	30
40	EMA Panel Discussion: What Might the Future Landscape of Submitting Data Look Like in 2025 ?	Eftychia-Eirini Psarelli, Jesper Kjær	Presentation	data cleaning, data standard, phase iii	source	14
41	EMA Panel Discussion: What Might the Future Landscape of Submitting Data Look Like in 2025 ?	Eftychia-Eirini Psarelli, Jesper Kjær	Presentation	clinical trial, clinical data, machine learning, clinical research, data standard, clinical programming	source	11
42	EMA Panel Discussion: What Might the Future Landscape of Submitting Data Look Like in 2025 ?	Eftychia-Eirini Psarelli, Jesper Kjær	Presentation	proc freq, clinical trial, outlier, data governance, big data, biostatistics, data science	source	21
43	EMA Panel Discussion: What Might the Future Landscape of Submitting Data Look Like in 2025 ?	Eftychia-Eirini Psarelli, Jesper Kjær	Presentation		source	16
44	Engineering an MDR of the Future	Ali Dootson, Mike Grove	Presentation	data science, excel, adverse events, ascii, life sciences, medical history, data cleaning	source video	19
45	Ensuring Distributed Data Custody on Cloud Platforms	Ben E. Bocchicchio, Sandeep Juneja	Presentation		source video	26
46	Evaluating the Predictive Accuracy of Survival Models with PROC PHREG	Lotte Jacobs, Roxane Debrus	Predictive accuracy measures the ability of a model to predict future risks. It can be used to validate a model or to compare new data to a pre -existing model. The concordance statistic (C -statistic) is the most commonly used discrimination measure in the context of logistic regression with binary outcomes. Besides the C -statistic, receiver operator characteristic (ROC) curves and AUC (area under the ROC curve) statistics are also commonly used to assess the discrimination ability of the model with binary outcomes. In the context of survival analysis, various C-statistics have been formulated to deal with right-censored data. In SAS, the `PHREG` procedure provides a number of state-of-the-art techniques to calculate overall concordance statistics and time -dependent ROC curves and AUC statistics for right - censored data. This presentation describes how to use these criteria to validate and compare fitted survival models and presents an example to illustrate its application	roc curve, logistic regression, c-statistic, proc phreg, censoring, logistic distribution, regression analysis, proc step	source video presentation source	7
47	F3: Fitness, FAIR & Filing	Bhupendra Mistry, Nigel Montgomery	Presentation	adam dataset, define-xml, data mart, data cleaning, reviewer guide, sdtm domain, cdisc	source video	19
48	Fingerprinting Your TFL Outputs to Monitor Changes Between Batch Runs	Pamphile Beaujean	Presentation	macro definition, macro loop, %if-%then-%else, infile statement, proc compare, artificial intelligence, clinical research	source video	20
49	FSP Associates: Team Player, But For Which Team?	Faheeza Rangunwala	Presentation	statistical programmer, service provider	source video	19
50	GUI for CSR Reporting: R You Ready to Shine with SAS?	Chris Ridley, Matthew Brierley	Presentation	full analysis set, analysis set, output delivery system	source video	9
51	Hey, Data Scientist: Are You Following the Rules and Your Conscience?	Lisa Brooks, Dhananjay Chhatre	Presentation	data science, clinical programming	source video	22
52	How Agile R You?	Rachel Linacre	Presentation		source video	15
53	How Has the Immunotherapy Era Changed Response Criteria?	Anna Amosova	Presentation	pd, duration of response, primary endpoint, standard deviation, change from baseline, objective response rate, best response	source video	30
54	How IA Alleviated the Impact of COVID-19 on JJCO by Quickly Automating the ESDCAQ V-TMF Filing Process	Dann Connelly, Peter Violand	Poster		source video	1
55	How to Be Traceable in ADaM	Angelo Tinazzi	Traceability is crucial in all steps of clinical data handling, from data collection to final analysis. For analysis , good traceability starts with a clev er design of ADaM datasets, where both data and metadata traceability should be guaranteed. The CDISC ADaM Team should release in 2021 a document where additional examples of traceability will be provided; furthermore, the importance of traceability in ADaM in data submission, has been recently highlighted by the FDA and PMDA at CDISC EU 2021 ("CDER's experience with the ADaM traceability assessment and common data quality issues", Jesse Anderson). Retaining --SEQ variable from SDTM source dataset is a starting point, together with other techniques recommended in the CDISC ADaM IG i.e., keeping original variables (and records) used in the derivation	adam dataset, reviewer guide, sdtm domain, data lineage, cdisc	source video presentation source	9
56	How to Grow as a Programmer in the Pharmaceutical Industry	Julie Barré	Presentation	statistical programmer, pharmaceutical industry, clinical programming, data science, proc fcmp, sas programming, proc step	source video	22
57	How to Handle Large Data Volumes in SDTM Using Continuous Glucose Monitoring as an Example	Ari Siggaard Knoph, Martin Gram	Presentation	laboratory data, clinical programming, cdisc	source video	14
58	How to Lead with Innovation and Make it Front and Centre of Your Company - Not Just a Poster in the Canteen or a Strapline on the Corporate PowerPoint Deck	Adam Mitchell	Presentation		source video	17
59	HoW: New Era of Collaboration - Towards the Adoption of Digital Measures - DEEP Initiative	Kai Langel, Bert Hartog		data cleaning, life sciences, clinical trial	source	13
60	HoW: RWD Applications in Pharma with a Focus on Clinical Trials	Berber Snoeijer, Samiul Haque	Presentation	logistic regression, feature importance, logistic distribution, regression analysis, proc hpforest, proc hpsplit, gradient boosting, sas studio	source	47
61	HoW: RWD Applications in Pharma with a Focus on Clinical Trials	Berber Snoeijer, Samiul Haque	Presentation		source	38
62	iListing - Get More Out of Listings	Katja Glass	Presentation	data cleaning, clinical database, clinical trial, load, in=	source video	37
63	Impact of Leadership Styles on Innovation	Adam Mitchell, Bhavesh Patkar	Presentation		source video	16
64	Implementation of a flat model for reactogenicity events in vaccine studies	Charumathy Sreeraman, Ephicacy Lifescience	Vaccines can protect us from infectious diseases by preparing the immune system to elicit antibody- and/or cell- mediated responses, which are specific against the pathogen. The safety and usability of an experimental vaccine is evaluated by assessments of reactogenicity. Reactogenicity describes immediate short-term reactions to vaccines, not long-term sequelae. In clinical trials, reactogenicity is measured by collecting sets of injection site and systemic signs and symptoms that are solicited from study participants over a specified time after vaccination. The CDISC Vaccine TAUG allows for three different strategies for mapping reactogenicity data in SDTM; known as the flat model, the nested model, and the highly nested model. The FDA has stated preference for the flat model in the guidance 'Submitting Study Datasets for Vaccines to the Office of Vaccines Research and Review'	clinical trial, adverse events, cdisc	source video presentation source	7
65	Incorporating Novel Real-world Evidence Across the Drug Development Continuum: Leveraging the Value of Digital Social Media	Nardin Farid, Stephen Doogan	Presentation	output delivery system, sample, proc freq, clinical endpoint, clinical research, treatment-regimen strategy, unstructured data	source video	24
66	Increasing Utility of Anonymised Data - Improved Banding Techniques	Jasper Enderman	Presentation	nominal variable, outlier	source video	14
67	Integrated Summary of Safety and Efficacy Production Strategies	Navneet Agnihotri, Sumit Pratap Pradhan	The integrated summary of safety (ISS) and integrated summary of efficacy (ISE) are vital components of a successful submission for regulatory approval in the pharmaceutical industry. ISS and ISE allow reviewers to easily compare individual outcomes, tracking subject's results across the entire clinical development of the investigational product, facilitating broad views of the investigational product's overall efficacy and safety profiles. With ISS and ISE, a single database is formed by pooling the results of all the clinical trials. This paper discusses step-by-step guidance for the efficient and accurate creation of ISS and ISE datasets. 1) Approaches to integration methods for safety and efficacy analysis data pool. 2) Best practices to ensure the consistency of integrated datasets by up-versioning all data with the same coding dictionaries version, controlled terminologies version, harmonizing values of critical variables and all variable attributes	clinical trial, controlled terminology, adam dataset, cdisc controlled terminology, data science, cdisc	source video presentation source	9
68	Introduction to Handling Efficacy Data in CDISC Standard Datasets	Kate Booth	Presentation		source video	32
69	Leadership & Innovation Panel Discussion: Unlocking Innovation	Adam Mitchell, Benedickt Egersdoerfer	Presentation		source	10
70	Learnings Using R for Creating CDISC Data for Analysis and Reporting Purposes!	Gustav Söderström, Nicole Major	Presentation	proc transpose, data visualization, proc compare, array bounds, proc step, data cleaning, cdisc	source video	23
71	Legacy Data's Journey: From Conversion to Electronic Data Submission Planning & Execution	Sowmya Singuluri	Presentation		source video	19
72	Let's ROC: An Empirical Approach to Confidence Intervals of Receiver-Operator Characteristic Curves	Alexander Ullmann, Sanna Ström	Presentation	confidence interval, roc curve, bootstrap, sample, sensitivity analysis	source video	41
73	medac: R Validated Environment - A Platform for and Analysis of Clinical Studies	Bernhard Braun, Max Horneck	Presentation		source video	12
74	Metadata: Errors Not Detected by the Pinnacle 21 Tool	Caroline Pelckmans, Pamphile Beaujean	Presentation	type ii error, adam dataset, missing value, type i error, adae, adverse events, statistical programmer, cdisc	source video	20
75	Methods and Challenges of R Validation for Regulatory Submission Analysis	Parvee Kumar	Presentation	data cleaning, 21 cfr part 11, adam dataset, clinical trial, regulatory submission, data science, statistical software, cdisc	source video	14
76	Moving Your SCE to the Cloud, Focusing on Qualification and Validation	Daniel Christen, Martin Doughty	Poster	data cleaning, test set	source video	1
77	Multi-study programming - a best practice outline	Simon Purkess, Matt Metherell		macro variable, adam dataset, %if-%then-%else, cdisc	source video presentation source	9
78	Multiple Graphs with Common Horizontal Axis Using GTL	Navneet Agnihotri, Sumit Pratap Pradhan	In clinical trials, graphical representation of data is an important method for communicating clinical trials results. In a majority of scenarios, only one graph or multiple graphs are shown on one page having independent x-axis for each graph. Figure becomes complex if multiple graphs are shown on one page having common x-axis. X-axis values can be in the form of discrete (e.g., avisit values like Screening, Day 1 etc.) or continuous (study day values like -14, 1, 7 etc.). Consider an example of a figure having two graphs on one page - top and bottom graph. The major problem in drawing such graphs is alignment of x-axis values for top and bottom graph. For example - at a particular point, the bottom graph has value 21 on x-axis but top graph has value 30. So making these values align is a tricky task. In this paper, figure will be explained using GTL method	clinical trial, bar chart	source video poster source	4
79	Navigating the Evolving Regulations for Health Canada	Gina Bennet	Presentation	medical history, concomitant medications, clinical trial, interim analysis, threshold	source video	20
80	New Insights Through Cross-portfolio Risk Analysis	Abdollahi Rouhollah, Julian Ortiz	Presentation	key risk indicator, portfolio risk, sas risk engine, protocol deviations, data science, clinical trial, concomitant medications	source video	17
81	New Starters Session: The Secret Sauce to Start in Our Industry	Amritpal Singh, Lucie Bowden	Presentation	data science, clinical data, clinical research	source	5
82	New Starters Session: The Secret Sauce to Start in Our Industry	Amritpal Singh, Lucie Bowden	Presentation	proc sql, statistical programmer, problem solving, proc step	source	9
83	New Starters Session: The Secret Sauce to Start in Our Industry	Amritpal Singh, Lucie Bowden	Presentation	proc sql, statistical programmer, problem solving, proc step	source	8
84	New Starters Session: The Secret Sauce to Start in Our Industry	Amritpal Singh, Lucie Bowden	Presentation	data science, crf, clinical trial protocol, clinical trial, reviewer guide, adamig, label statement	source	10
85	New Starters Session: The Secret Sauce to Start in Our Industry	Amritpal Singh, Lucie Bowden	Presentation	clinical data, data science, clinical trial, clinical programming, machine learning, adam dataset, independent qc, cdisc	source	7
86	New Starters Session: The Secret Sauce to Start in Our Industry	Amritpal Singh, Lucie Bowden	Presentation	clinical research, statistical programmer, data science, adam dataset, clinical trial	source	11
87	New Starters Session: The Secret Sauce to Start in Our Industry	Amritpal Singh, Lucie Bowden	Presentation		source	10
88	Next Generation MDR Solution for Study Design and Configuration Using Concept-based Standards	Mikkel Traun	Presentation	data standard, edc, audit trail, cdisc controlled terminology, controlled terminology, visit, output delivery system, cdisc	source video	15
89	No One Expects the Spanish Inquisition! How can (Monty) Python Help with Your Daily Job around Deliverables?	Andrés Käsa	Poster		source video	1
90	No XSLX Engine in SAS? No XLSX Problem	Will Greenway	Presentation	proc sql, clinical programming, excel, proc step, libname statement, proc import, call execute	source video	22
91	One Giant PROC for Mankind: Using PROC LUA for Code Generating Code and Automating TFLs	Tom Ratford	Presentation	proc step, proc lua, dataset, proc report, run, comment, macro definition	source video	16
92	Ongoing Safety Monitoring Using R-shiny	Pernille Højlund Nielsen, Pernille Juul Jørgensen	Presentation	clinical trial	source video	12
93	Overview of Bayesian Approaches in Survival Analysis Using SAS	Anastasiia Kinkolykh, Roman Ganzha	Presentation		source video	10
94	Overview of the Submission Process for the FDA, EU and PMDA	Nadeem Gul	Poster: The Regulatory Environment Workstream Acknowledgements PHUSE EU Connect 2021 Figure 2. Initial Review by the CHMP Figure 1. The general submission process Regulatory agencies and governance organisations around the world play a vital role in ensuring the safety, quality and efficacy of medicines. Every country has its own regulatory authority, which is responsible for enforcing the rules and issuing guidelines to regulate activities such as licensing, registration, manufacturing, marketing and labelling of pharmaceutical products in their region. Major regulatory bodies that pharma companies interact with include o US FDA (Food and Drug Administration) o EMA (European Medicines Agency) o PMDA (Pharmaceuticals and Medical Devices Agency) Data analysts/scientists are considered experts in the structure and analysis of clinical data submitted to regulatory authorities		source video	1
95	Overview Projects Timeline to Arrange Statistical Programmer Resource via R Shiny	KuenHung Lin	For the clinical trial industry, it is always full of competitive tasks in a company. Hence, one of the most important duty of line manager is overview multiple projects timeline to arrange members' resource. Although, there are several application packages to manage project timeline. Most of those applications can't be customizable. This article provides an interactive web application to visualize projects timeline via R Shiny which can be customized by statistical programmer who has experience in R. Hence, many customizable information can be added in the application, such as descriptive statistics for resource per month and making key points stand out freely	excel, statistical programmer	source video presentation source	5
96	Parsing JSON Files in SAS Using PROC LUA	Lex Jansen	Presentation	proc lua, proc step	source video	36
97	PowerSAS - SAS Interactive Line Mode (NODMS) on Microsoft Windows Using PowerShell	Stuart Malcolm	Presentation	distributed processing, proc printto, proc step	source video	10
98	Preparing PK/PD ADaM Datasets and eCRT Data Package for FDA Submission	Junying Zhang	The CDISC Analysis Data Model (ADaM), which is required by FDA for clinical trial data submission, is designed to support analysis datasets. In this paper, we share our experience with creating the following ADaM datasets for pharmacokinetic & pharmacodynamic (PK/PD) analysis: ADSL and ADPKPD . The PK/PD ADaM electronic Case Report Tabulation (eCRT) data package has its own character. We will share our experience with creating the PK/PD ADaM eCRT data package and associated documentation for FDA electronic submission. It includes the ADSL and ADPKPD datasets in SASTM Transport File Format (XPORT), the PK/PD analysis data reviewer's guide, define.xml and the SAS programs submitted in the eCTD module folder. This paper will only focus on the tasks of statistical programmers when preparing the PK/PD eCRT Data Package for FDA submission	pd, adam dataset, pk parameter, sample, adverse events, baseline characteristics, proc means, geomean, population pk, sample size, data science, data cleaning, clinical trial, efficacy summary, cdisc	source video presentation source	11
99	PROC MIXED: A Complicated Procedure in Simple Words	Kateryna Fedoryshyna	Presentation	proc mixed, efficacy summary, proc step, covariance structure, covariance, data science, ods output	source video	21
100	Processing RTF Documents with SAS	Cara Murphy	Presentation	macro definition, scatter plot	source video	14
101	Programming Infrastructure to Automate Process Flow for CSS	Archana Nayak, Salvatore Saporito	Presentation	clinical trial, clinical data, nominal variable, data wrangling, sdtm domain, data science, missing value, cdisc	source video	22
102	Programming Techniques to Enhance the Presentation of Forest Plot with SAS 9.4	Zhen (Laura) Li	Presentation	forest plot, proc template, proc step, data science, proc sql, artificial intelligence, statistical programmer	source video	18
103	R and SAS Working Together - DNR's Experience Moving to LSAF 5.3	Paul Vervuren	Presentation	sample, life sciences, sample size, data cleaning, audit trail, clinical trial, clinical research	source video	17
104	R Packages Management for Regulatory Compliance - Challenges and Solutions	Pierre Wallet	Presentation	r package	source video	19
105	R Shiny App: Smart Log Checker Visualisation Solution More Than Log Checking	Chen Yang	Poster		source video	1
106	R String Manipulation Functions vs SAS Character Functions	Jagadish Katam	In this paper will do a comparative analysis of character functions between R and SAS. This is a better way of learning any new programming language. The industry has been using SAS for quite some time and recently there is a new trend of R in clinical trials. Considering the same, we might soon move on to use R in generating datasets and outputs, so for data manipulation we need to know how and which R string function we need use for the data manipulation. Some of the common R string functions include paste(), str_replace(), str_split(), str_subset() etc., including the regex functions like str_view() which we will try to understand and compare them with SAS character functions like catx(), tranwrd(), scan(), find() and prxmatch(). We will explore the string functions of R vs SAS with some examples. This paper will help programmers to understand R string functions	string functions, character functions, concomitant medications	source video	8
107	Real-time Analysis of Clinical Trial Data	Jem Chang, Prasanna Murugesan	Presentation	clinical trial, clinical data, clinical research	source video	9
108	Real-world Data, CDISC SDTM, HL7 FHIR and the Future of Regulatory Submissions	Jozef Aerts	Presentation	clinical research, sdtm domain, web service, clinical trial, edc, adam dataset, data review, cdisc	source video	17
109	Redefining Clinical Data Flow at AstraZeneca	Malgorzata (Gosia) Grabowska	Poster	csr, clinical data	source video	1
110	Replicating R Figures into SAS	Antonio Rodríguez Contestí		proc step, scatter plot, histogram, box plot, proc sql, proc format, density plot, proc freq	source video presentation source	26
111	Results from a TEAE Scenario Survey by the PHUSE Treatment Emergence Definition Project Team	Mary Nilsson, William Palo	Poster	screening period	source video	1
112	SAS Coding Efficiency: Developing a Performance Mindset	Richard Paterson	Presentation	cpu time, proc step, proc sql, create table, life sciences, proc contents, out=	source video	21
113	SAS Formats On The Fly Using ADaM Datasets	John Hendrickx	Presentation	call execute, proc format, user-defined format, proc step, character variable, format statement, proc sort, cdisc	source video	12
114	SAS! R(aaah), Saviours of the Data Vis	Jack Finch, Sophie Shapcott	Poster		source video	1
115	SAS, APIs and All the REST	Sven Prasse	Presentation	proc http, rest api, filename statement, libname statement, sas log, proc step, exception handling	source video	17
116	Software Demonstration: Advancements in Clinical Trial T&D: How Software Opens the Door for Proactive Data and Document Disclosure - Sponsored by Certara	Trevor Standish	Presentation		source	10
117	Software Demonstration: Aspire "Out of this World" Demo - Sponsored by d-wise	Andy Ratcliffe	Presentation	audit trail, sas studio, sysrc, data integrity, data science, big data, data validation rules	source	44
118	Software Demonstration: Freedom or Regulatory Compliance? Have Both, in a Language-agnostic Statistical Computing Environment - Sponsored by Entimo	Jörn Bilow	Presentation	21 cfr part 11, audit trail, data integrity, machine learning, rest api, data cleaning	source	14
119	Software Demonstration: Unlocking Value from Structured Clinical Trial Data - Sponsored by Privacy Analytics	Thomasin Element	Presentation	clinical trial, data science, clinical data, clinical research, financial services, unstructured data	source	18
120	Software Demonstrations: elluminate Clinical Data Cloud - Automating Clinical Data from Ingestion to Actionable Insights - Sponsored by eClinical Solutions	Achilleas Zaras	Presentation	database lock, excel	source	9
121	Software Evolutions are Inevitable: Phase Your Coding evolution to Modern Clinical Trial Analytics by Using both Old/New Techniques in a Modern SAS Platform	Olivier Bouchard, Pritesh Desai	Presentation	sas viya, clinical trial, proc step, proc mixed, parallel processing, data science, proc sql	source video	19
122	Standard Implementation: Battling Through the Adversity of a Pandemic	Ana Arques, Vicky Poulsen	Presentation	missing value, clinical trial, protocol deviations, clinical data, edc, sas consultant	source video	18
123	Standardising Schematics for the Subject ID across the Industry	Ananya Jain	Poster		source video	1
124	Standards and Governed Data in the Clinical Data Pipeline	Robert Musterer	Presentation		source video	10
125	Standing Room Only: ?LI01: From Concept to Reality: GSK's Journey to R as a Case Study for Innovation	Michael S Rimler	Presentation	likelihood function	source	11
126	Standing Room Only: AR02: The Importance of Being Ernest - The Role of QTLs in QbD	Andrew Lawton	Presentation	root cause analysis, clinical trial, adverse events, clinical research, phase ii, protocol deviations, quality control	source	19
127	Standing Room Only: DH01: ClinicalTrials.gov REST API - Tapping into the Clinical Studies Registry Database the Smart Way	Lukas Kniola	Presentation	clinical trial, rest api	source	30
128	Standing Room Only: DH07: I'll Take Your Brain to Another Dimension, Pay Close Attention	Cathal Gallagher	Presentation		source	20
129	Standing Room Only: RW08: The Convenient Structure of the OMOP CDM	George Allerston, Khaleeq Mannan	Presentation	clinical database, clinical programming, raw data	source	20
130	Standing Room Only: SA04: Diversity, Equity and Inclusion of Your Submission Package	Anne-Sophie Bekx	Presentation	clinical trial, excel, clinical research, open label, regulatory submission	source	19
131	Standing Room Only: SA07: Programming Pooled Safety Data for Ongoing Oncology Submissions	Sarah Berenbrinck	Presentation	dose interruption, dose, trial elements, treatment period, adverse drug reaction, data cleaning	source	36
132	Standing Room Only: TT04: Agile Programming: Towards a More Collaborative Programming Set-up	Bo Sølgaard Andersen, Frederik Vandvig Heinen	Presentation	statistical programmer, clinical programming	source	19
133	Standing Room Only: TT12: Clinical Statistical Reporting in a Multilingual World	Michael S Rimler, Michael Stackhouse	Presentation		source	12
134	Statistical Approaches in Complex Efficacy Analysis for Vaccine Trials	Drisya C A, Nijitha L M	Presentation	goodness of fit, logistic regression, regression analysis, response variable, correlation, chi-square test, hazard ratio	source video	10
135	Statistical Training for Health Technology Assessment Statistical Programmers	Anastasia Kokoreva, William Malbecq	Statistical programmers (SPs) are key contributors to the preparation of the statistical results that support Health Technology Assessments (HTA) submissions. In order to collaborate optimally with statisticians, programmers should have a good understanding of the statistical methods and approaches used in HTA submissions. These methods represent a mix of standard techniques taught in applied statistical courses and more innovative/recent techniques. At MSD in the Biostatistics and Research Decision Sciences (BARDS) Statistical Programming HTA team, statistical programmers have diverse educational backgrounds and work experience and consequently different levels of statistical knowledge. The Statistical Training for SP in HTA initiative has been ongoing since 2020. Its main objective is to increase the level of statistical knowledge within the statistical programming team and bring it to a common level that optimizes analysis and reporting activities, both in efficiency and quality	statistical programmer, clinical trial, survival analysis, regression analysis, treatment effect, proc means, hypothesis testing, probability distribution, overall survival, randomization	source video presentation source	7
136	Study Designs Using FHIR - Schedule of Activities Exchange Using FHIR Resources	Andy Richardson	Presentation	proof of concept, clinical research	source video	19
137	Studyathons: A New Way of Organising and Promoting Multidisciplinary Collaborations in Biomedical Open Science	Julia Kurps, Maria Escala-Garcia	Presentation	proc sql, proc phreg, regression analysis, data science, exposure, feature extraction, life sciences	source video	22
138	The ABC of Managing a Standards Macro Library	Amit Bhaniani, Carl Herremans	SAS macro is a powerful programming language to develop dynamic programs. It enables the end-users to customize various attributes of the output tables, listings, and figures (TLFs) based on the value of macro parameters. While developing and maintaining high quality macro programs as a standards macro library is a common practice in statistical programming, efficient management of a vital resource like macro library is not an easy task. Three main challenges in the management of standards macro library are: (1) adding to and updating macros in the standards macro library to address changing business needs; (2) ensuring adherence to best programming practices and departmental SOPs, and (3) fine-tuning communication channels established for end-users, stakeholders and sponsors of the standards macro library. This paper shares some management tools and techniques to illustrate how a standards macro library can be managed efficiently	data cleaning, key performance indicator, sample, statistical programmer, macro parameter	source video presentation source	9
139	The Application of Pattern Mixture Models and Tipping Point Analysis in Clinical Trials Involving Multiple Imputations	Anastasiia-Narmina Fataliieva, Daria Shamaida	Poster	imputation, multiple imputation, sensitivity analysis	source video	1
140	The Art of Swim Lane Plot	Ganesh Lekurwale	In oncology trials swim lane or swimmer plot is very dynamic to present patient level information. It gives bird's eye view of overall key trial milestones in one glance. It helps multiple stakeholders to understand key efficacy or safety milestones of each patient or group of patients during the span of the trial. Often it is important to show some of the baseline characteristics of the patient to know how and why particular response is observed in the trial. Inspired from forest plot, this paper presents baseline characteristics table and swimmer plot together in one graph using SAS GTL. To achieve this graph various GTL techniques have been explored - annotation dataset, hexadecimal codes for symbols and external image imported as alternate to SAS inbuilt markers	swimmer plot, baseline characteristics, proc step	source video presentation source	7
141	The AstraZeneca Data Automation Team: Future-proofing Programming Teams to Meet Tomorrow's Challenges	Ruhul Amin, Tony Allen	Poster: A lot has changed in the statistical programming paradigm in recent years. We have seen a shift from 'SAS only' programming & analytics to a 'blended' approach which includes SAS software in combination with R, Python, Java etc. The key driving forces behind this are: new and advanced study design and conduct, data collection using sensor and mobile apps, increased volume of study data, quicker turnaround time for trials and the introduction of Data Science & Artificial Intelligence (AI). AstraZeneca is a forward-looking company and is investing in developing next generation programming teams that are future proof	data science, clinical trial	source video	1
142	The Concept of IoT - The Next Generation of Statistical Computing Platform	Huadan Li, Zhiping Yan	Presentation	clinical programming, machine learning, dataset, sas log, data science, deep learning, mock shell	source video	20
143	The Good, The Bad and The Ugly Styles of Line Management	Emma O'Brien, Robert Croft	Presentation	clinical programming	source video	23
144	The Last Mile of Analytics: Turn Your SAS Programs and Models into Interactive and 'Shiny' Apps	Dragos Coles, Mark Lambrecht	Presentation	sas viya, scenario analysis, data science, rest api, macro variable, resource allocation, sas visual analytics	source video	15
145	The Library Approach Driving EDC Set-up Efficiencies: A Case Study	Jan De Cleir	Presentation	clinical trial, data cleaning, ecrf, crf, clinical data, data validation rules	source video	23
146	The Value of Optimising Your CDISC Analysis Data Model (ADaM) Specifications	Amine Khemiri, Caroline Pelckmans	Presentation	adam dataset, adamig, cdisc	source video	17
147	Tips and Tricks for Being More Productive with SAS Studio	Andy Bayliss	Presentation	sas studio, sas enterprise guide, machine learning, data wrangling, dataset, data science, sas programmer	source video	32
148	Transformation of Clinical Trials Requires a Revolution in Your Data Flow	Bernard Panes, Ishan Vyas	Presentation		source video	1
149	Transforming and Streamlining the Report Analysis Plan - A Journey in Capability Build and Changing Responsibilities	Insa Gathman, Joseph Rowley	Presentation		source video	12
150	Transforming Reporting - A Journey into Web Technology for Listing Generation	Joseph Rowley, Kartik Rajan	Presentation	adverse events, data cleaning, full analysis set, analysis set, clinical data, dose, medical history	source video	23
151	Transition Probability for Analysis of COVID Test Positivity Ra	Hrishikesh K S, Limna Salim	Presentation	markov chain, sample, maximum likelihood estimation, pharmaceutical industry, transition matrix, likelihood function, proc freq	source video	10
152	Update to Recommended Analyses and Displays for Laboratory Data	Charles Beasley, Wei Wang	Poster	box plot	source video	1
153	Use Python to Combine LST Files into a PDF file with Bookmarks	Hengwei Liu	In a clinical study the programmers may produce hundreds of LST files with SAS for tables and listings. To make it easier for the study team to review the output, those LST files are often combined into a single file, either in the RTF format with navigation pane or in the PDF format with bookmarks. In this paper we discuss a way to convert those LST files into PDF files and combine the PDF files into a single PDF file with bookmarks. The development is done with Python 3.9.5 in Linux	clinical trial, sample	source video presentation source	10
154	Using Jobs and API Macros to Create a Structured, Controlled and Personalised LSAF Environment	Stephan van Driel	Presentation	life sciences, access control, clinical trial, do loop, data governance, sas consultant, macro definition	source video	30
155	Using Modular Programming to Create Mean Plots and Adverse Event Frequency Plots	Claus Stenberg	Presentation	adverse events, proc template, proc step, proc means, proc sgplot, geomean, structured data	source video	28
156	Using R - TFL Generation with Custom Functions	Jem Chang, Vikram Kasarala	Poster	overall survival, safety population	source video	1
157	Utilising LSAF for Data Management at a Belgian Biotech	Louella Schoemacher	While PC-SAS and Enterprise Guide are still widely used, they are slowly but surely being pushed aside by web and cloud-based solutions such as SAS Studio and SAS Life Sciences Analytics Framework (LSAF). LSAF is a hosted solution that is designed to be much more than just a programming environment. It is an integrated data repository and computing environment offering features such as audit trail and versioning out-of-the-box. Over the past year OCS Life Sciences has led the implementation of LSAF at a Belgian biotech company. This encompassed the (technical) configuration, design of th e folder hierarchy, associated permission and security model, defining best practices for the use of the system, creation of jobs and process flows, and much more. The LSAF environment i s now in production and being utilised by data managers, programmers and statisticians. In this paper we will share the most important learnings of our journey	excel, adverse events, dataset, concomitant medications, data governance, data cleaning, data review, ctcae grade, controlled terminology, sdtm domain, cdisc	source video presentation source	7
158	Visualising Knowledge Graphs: Information Solution or Ball of Confusion?	Tim P. Williams	Presentation	data cleaning, system organ class, machine learning, output delivery system, sas engineer, data profiling, data science	source video	57
159	Welcome, CDISC Library; Goodbye, PDF Documents	Joseph W. Hinson	Presentation	rest api	source video	15
160	When PROC COMPARE is not enough - the fast check of variables discrepancy through thousands of observations	Roman Chekhun	An important part of dataset programming is its validation. One of the common moments that programmers encounter is a mismatch due to the different number of records in the dataset. This can happen due to a large number of variables in supplementary datasets, the quality of the collected raw data, or other conditions. This paper pr esents a macro that makes it possible to pinpoint all inconsistent values between the datasets on the Production and QC sides. It allows key discrepancies to be identified more quickly. Equally important, there is no limit to the number of inconsistency records displayed. The limit of displayed 500 values in `PROC COMPARE` sometimes makes it difficult to find the root cause of the discrepancy. The main convenience and difference from the standard `PROC COMPARE` procedure is the compactness of th e displayed result in the form of a table with both a specific parameter and a group of parameters	proc compare, proc step, create table, group by, data cleaning	source video presentation source	7
161	Wild-merge for Automated Data Reconciliation in both SAS and R	Gerard Hermus, Machiel Visser	Presentation		source video	19

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