Presentations

Please note, content listed below is subject to change.

Analytics, Big Data & Statistics

AB01: Drug-Induced Liver Injury (DILI) Classification Using FDA-Approved Drug Labeling and FAERS Data – FDA

AB02: Adverse Event Analysis - One step forward! – inVentiv Health

AB03: Interim Survival Analysis Across 3 Platforms – Bowden Analytics

AB04: R or Python: A Programmer's Response – d-Wise

AB05: A Measurement Error Model Framework for Biomarkers based on Emerging Technologies – NCTR/FDA

AB06:Shaken But Not Stirred: An Example of Subject Classification using Multidimensional Scaling – Bayer

AB07: Feature Selection for Predictive Modelling – A Needle in a Haystack Problem – Cytel

ABO8: Machine Learning Driven Clinical Insights – What When & How? – Accenture

AB09: Modeling and Text Analysis to Empower FAERS Adverse Event Mitigation – FDA

AB10:Going Pro with PROs: Implementation and Analysis of Patient Reported Outcomes – Ephicacy

AB11: Registry/Observational Studies: Study Designs Data Sources and Key Steps in Designing Such Studies – Bayer

AB12: Statistical Analysis Plan – Clinical Programming Reviewers Guide – GlaxoSmithKline

AB13: Augmented Realtime Clinical DataMart – inVentiv Health

AB14: Data Science Applications and Scenarios – PRA Health Sciences

AB15: Harnessing Real-time Patient Data to Improve Clinical Outcomes and Research – Biogen

AB16: Integrating Externally Acquired Data Into Standard Workflows – Biogen


 

Coding Tips & Tricks

CT01: Fuzzy Joins with Proc SQL for Better Data Utilization – Cytel

CT02: A Practical and Efficient Method of Calculating Actual Time Since Last Dose Data in PK Analysis – PAREXEL

CT03: How to automate validation with Pinnacle 21 command line interface and SAS – Pinnacle 21

CT04: Challenges of Developing Microbiology dataset – Merck

CT05: How To Derive Parameters At ADaM Level For Generating Yearly Interval Exposure Tables – GCE Solutions

CT06: A Macro Tool to Find and/or Split Variable Text String Greater Than 200 Characters for Regulatory Submission Datasets – Shionogi

CT07: Controlling the Drawing Space in ODS Graphics by Example – GlaxoSmithKline

CT08: Convert the Clinical SAS datasets from RTF files – GCE Solutions

CT09: PROC TEMPLATE and its Application IN TABLES & CROSSTABULATION TABLES – inVentiv Health

CT10: Row Row Row of Zeroes: Their Significance in Summary Tables and Subgroup Analyses – Ephicacy

CT12: Infrastructure Designed to Maximize Workflow – Omeros Corporation

CT13: Upholding Ethics and Integrity: A macro-based approach to detect plagiarism in programming – Ephicacy

CT14: Push a Button in Excel to Run all Your SAS Programs – Sarah Cannon

CT15: ANALOG an Interactive Browser-Based and Business-Oriented SAS Log Checker – Roche

CT16: Proc FCMP: Stay Magical – inVentiv Health

Data Handling

DH01: Transformative Re-Emerging Best Practices: Collect Analyze Share – d-Wise

DH02: Split-Coalesce Tumor Data Handling Woes and Safety-Efficacy Dilemma – Advanced Clinical

DH04: Missing Data Imputation and Its Effect on the Accuracy of Classification – PRA Health Sciences


DH05: Methods for Handling Concentration Values Below the Limit of Quantification in PK Studies – Summit Analytical

DH06: Automated Creation of Submission-Ready Artifacts – Accenture

DH07: Secure Data Office: An Independent Team that Can Come to the Rescue in Blinded Trials – SGS Life Sciences

DH08: PDBLoader - Combining Documentation and Code to Create a Pooled Database – UCBChiltern

DH09: Managing Programs Development Life-Cycles in SAS LSAF – Qualiance

DH10: Janus Clinical and Nonclinical Loading: The Most Common Issues Identified from Sponsor Submissions – FDA


DH11: Practical Solutions when Analyzing Incomplete Disposition Data – Bayer

DH12: An Automated Macro to Compare Data Transfers – Covance

DH13: Programmers are from Mars and Statisticians are from Venus – Zifo RnD Solutions

DH14: Handling Data "Blinding" for Oncology Open Label Studies Using a SAS Macro – Merck

Data Standards and Governance

DS01: Comparing and Contrasting Differences between the PhUSE SDSP and CBER Checklist – Merck

DS02: Developing Implementing and Governing End-to-End Standards at Gilead – Gilead Sciences


DS03: Using a Waiver Process as a Change-Control Tool from ADaM Standards Governance – Shire

DS04: SDTM and SEND: An Integrated View and Approach – Shire & Alnylam Pharmaceuticals

DS05: Keeping Control in a Changing World – S-cubed

DS06: Incremental Changes: ADaMIG v1.2 Update – MedImmune & Chiltern

DS07: Making PK Analysis Easier: The New ADaM Data Standard ADNCA – VCA-Plus

DS08: Implementing an Metadata Repository Solution - A User Story – NurocorPfizer

DS09: Business-based Value in an MDR – Merck

DS10: Generating Define.xml v2.0 and Analysis Result Metadata using ADaM Specifications ADaM Datasets and TFL Shells Annotation – ICON

DS11: Automate Analysis Result Metadata in the Define-XML v2.0 – Merck

DS12: CRF Design for Data Standards – Rho

DS13: Best Practices for Explaining Validation Results in the Study Data Reviewer's Guide – Pinnacle 21

Data Visulatisation

DV01: Clinical Timelines Visualized – Rho

DV02: Mapping Mirena Users in the US – Bayer

DV03: Benefits of a Rapidly Growing Library of  Clinically Relevant Visualization Templates/Patterns – Integrated Clinical Systems

DV04: Identification of New Information in a Data Based on a Data Point Level – Bayer

DV05: R2SAS R2SAS2PDF & SAS2SHINY: Seamless R and SAS – Glaxosmithkline

DV06: Subgroup Explorer and AdEPro – The Full Picture on Clinical Trial Data – Bayer

DV07: A Gentle Introduction to R Graphics from a SAS Programmer's Perspective – Biogen

DV08: Unleashing Potential of Graphs for Oncology Trials – Ephicacy

DV09: Supporting a Data Review and Visualization Application with SDTM – Chiltern & MedImmune

DV10: A Picture is Worth a Thousand Words; Analyzing Clinical Data with Sequencing Data – Roche

DV11: How ODS GRAPHICS INFRASTRUCTURE can be Used in Statistical Analysis – Chiltern

DV12: Web Codebooks – Interactive Data Summaries for Clinical Trial Data – Rho

DV13: PowerDataExplorer - Data Exploration in an All-In-One Dynamic Report Using SAS & EXCEL – Janssen Research & Development

DV14: Data Visualization for Data Monitoring Committees for Clinical Trials – Cytel

DV15: Insights to Meticulous Clean Patient tracking via Analytics – Vita Data Sciences

DV16: How Automatic Visualization of Summary Results Can Speed up Review to Tables 

DV17: Building Dashboards for Real-time Insights into Clinical Data – Vita Data Sciences

 

Poster Presentations

PP01: PhUSE Working Groups - Now & Next – Roche & d-Wise

PP02: Define.pdf - Just F.O.P. it – UCB

PP03: Streamline the Collaboration – From Whitepaper Targets (TFL) to Sharable Scripts – Frontage Lab & IQVIA

PP04: Behind the Scenes: From Data to Customized Swimmer Plots Using SAS® Graph Template Language (GTL) – ICON

PP05: Define.xml Value Level Metatadata Coding and Naming Guidance – IQVIA

PP07: UTF What? A Guide for Handling SAS Transcoding Errors with UTF-8 Encoded Data – Chiltern

PP08: Improving Traceability for Complex Algorithms in ADaM Datasets – Levstat

PP09: Automating and Customizing TLG Outputs with a Single Click – Zifo R&D

PP10: Robust Test Data – ICON

PP11: Common Define.xml File Issues Seen During FDA’s JumpStart Service – IBMFDA

PP12: Post-Marketing Surveillance and the FAERS Big Data Paradox – MMS

PP13: Expedite Your Submissions Using a Conformed Data Approach – Achieve IntelligenceNovartisAb Tartarus

PP14: Project Programmer Role – GSK

PP15: Bili's Journey – MMSIris Statistical Computing

PP16: Planning and Executing a Two-Stage Adaptive Design with Interim Analysis  – Cytel

PP17: Standardization of Adjudication Outcomes Data – Novartis

PP18: Touchpoints for the Study Data Standardization Plan – Shire
 
PP19: Programming Development and Validation Tracking Application – ICON

PP20: Understanding the Power of Information Modeling for Efficient and Effective Visualization – Capish

PP21: Qualitative Analysis of Actual SEND Datasets for Trial Design Domains – Eisai

PP22: Challenges and Opportunities for Real-time Clinical Data Review – A Case Study – Janssen Research & Development

PP23: One Vision Five Different Needs: CPE Plan for Standard Analyses and Code Sharing – MPI Research

PP24: Why Data and Document Anonymization Must Carpool? – MMS

PP25: Standard Analyses and Displays for Common Data in Clinical Trials – The Journey Continues! – FDA

PP26: Interactive Visualization of SEND-Formatted Clinical Pathology Data Using R Shiny – FDA

Regulatory

RG01: Achieving Highest Quality and Usability of SEND through Industry and Regulatory Collaboration – Bristol-Myers SquibbFDA

RG02: Role of Associate Director of Biomedical Informatics in Clinical Review at the FDA – FDA

RG03: Confusing Data Validation Rules Explained – Pinnacle 21

RG04: Updates from the EMA  Technical Anonymization Group & Policy 0070 – d-Wise

RG05: Policy 0070 Update: How the Impact of Latest Changes Goes Beyond Biostatistics – d-Wise

RG06: It's Time To Change – Assero

RG07: BIMO Listings - Check That Off Your NDA To-Do List – Vita Data Sciences a division of Softworld

RG08: "What's Rules Got to Do with It?" – FDA

RG09: FDA View: Technical Rejection Criteria for Study Data – FDA

RG10: FDALabel Database: Enabling Insights from Product Labelling to Accelerate Advancement of Regulatory S – FDA

RG11: Use of Real-Time Application for Portable Interactive Device (RAPID) for Data safety and Patient Out – FDA

RG12: Assuring Data Integrity and Data Quality in Sponsor Submissions – FDA

RG13: The Changing Landscape of Data Quality at CDER: an Overview of the Core DataFitness Assessment – FDAIBM

RG14: FDA CBER Data Standards Activity Update – FDA

RG15: Biomedical Informatics in the Office of New Drugs CDER/FDA – FDA

RG16: CDER’s Clinical Investigator Site Selection Tool – FDA

Standard Implementation

S101: The Untapped Potential of the Protocol Representation Model – Rho & CodeCrafters

SI02: Practical Benefits of EC: Building a Comprehensive Exposure Story – SGS Life Sciences

SI03: Automated CRF Annotations – A shift from Manual – Ratilan Technologies

SI04: SDTM: It is Not all Black and White – SGS Life Sciences

SI05: Qurie - Our own SDTM Bot – Zifo RnD Solutions

SI06: Increase Quality and Reduce SDTM Development Time with a Test Study Data Simulator – Chiltern

SI07: Optimizing SDTM Specification Development with Auto-population – Chiltern

SI08: A Successful Meta-Data Repository (MDR): It's About Managing Relationships – GlaxoSmithKline

SI09: Commissioning a MDR System: Next-generation of Standards implementation – Ephicacy

SI10: Metadata-based Auto-Programming Process – Janssen Research & Development

SI11: Using the TFL Workbench for Standards Governance – PRA Health Sciences

SI12: How to Prepare High Quality Metadata for Submission – Cytel

SI13: Step Up Your ADaM Compliance Game – ICON

SI14: How to Create your Define.xml as Early as Possible with a Click on a Button – SGS Life Sciences

SI15: The First and Last Words of ADaM – Maximum Likelihood

SI16: ADaM Intermediate Dataset: How to Improve Your Analysis Traceability – Cytel

SI17: Want Submission-Ready Datasets Package from the Get-Go? – Vita Data Sciences a division of Softworld

SI18: Patterns Emerging from Chaos – PAREXEL

SI19: Easing Your Pain with Biomedical Concepts – S-cubed

Strategy in Programming

SP01: ADaM Programming - The Good the Bad and the Ugly – ICON

SP02: How do I have ONE Interpretation of CDISC for all my Studies? – Bioforum

SP03: Ready Set Go:  Planning and Preparing a CDISC Submission – GlaxoSmithKline

SP04: Moving Cheese: How to Lead Traditional Programmers on the Journey to a New World – d-Wise & Rho

SP05: Lead Programmer Needs Help: Dedicated Programming Project Manager to the Rescue! – Vita Data Sciences a division of Softworld

SP06: Challenges and Tactics of Managing Clinical Programming in Long Term Outcomes Trials – GlaxoSmithKline

SP07: Mine the Gap: How the Role of Data Scientist Fills a Need in the Pharmaceutical Industry –FMDGrünenthal

SP08: Cue the Word – QC - All You Need to Know! – GlaxoSmithKline

SP09: SAS Programming is Done by Human Beings – PRA Health Sciences

SP10: A Standardized Collaborative Approach for Statistical Programming Project Information – Roche

SP11: Don't Sweat It! Consider Taking the Risk Out of your Outsourced Deliverables – GlaxoSmithKline

SP12: Introducing Project Management and Emotional Intelligence to Statistical Programmers – Amgen

SP13: Many Minds Make Great Work: Perspectives on Successful Collaborations – d-WiseFDA

SP14: Perfect Your Presentation Pitch: A Presentation on Presentations – GlaxoSmithKline

Trends & Technologies

TT01: Alexa for Clinical Research – d-Wise

TT02: Why SAS Programmers Should Learn Python Too – Chiltern

TT03: Harnessing the Web to Streamline Statistical Programming Processes – Biogen

TT04: Upcoming Technology with Transformative Potential for Life Sciences – Oracle

TT05: Postmarket Surveillance Powered by IBM Watson: Assessing Adverse Event Causality – IBM

TT06: FDA Experience with Emerging Genomics Technologies – NCTR/FDA

TT07: A Semantic NLP Approach for Structuring and Analysis of FDA Meeting Minutes Documents – FDA

TT08: The Next Generation "Smart Program Repository" – inVentiv Health

TT09: Generating Tables Listings and Figures Without Any Programs – Shafi Consultancy
 
TT10: Building Efficient Programming Teams Using RStudio with Git in Pharmaceutical Industry – Pfizer

TT11: Evaluating how Blockchain can Transform the Pharmaceutical and Healthcare Industry – UCB & Biogen

TT12: Breaking Away from Shell Scripts for Batch Mode Submissions on SAS GRID – Chiltern

TT13: A Statistics-based Tool to Inform Risk-based Monitoring Approaches – CROS NT

TT14: Can Clinical Trial Datasets (CDSIC - SDTM/ADaM) be Generated Using R? – AstraZeneca 

TT15: Incorporating Pinnacle 21 with LSAF – Alexion Pharma

TT16: Use of HL7 FHIR as eSource to Pre-populate CDASH case Report Forms using a CDISC ODM API – Next Step Clinical Systems

TT17: Transforming Clinical Trials with Linked Data – UCB

Please note, content detailed above is subject to change

 

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