Event

PSI Scientific Meeting: Generating Insights through Modern Applications of Data Visualisation

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Date: Friday 17th & Friday 24th September 2021 (Please note: this event is split into 2 parts)
Time: 13:00-17:00 BST both days
Speakers: 
Part 1: Susan Mayo (FDA), Jeremy Wildfire (Gilead), Sheila Dickinson (Novartis), Matthias Trampisch (Boehringer Ingelheim), Charlotta Fruechtenicht (Roche), Patrick Schlömer (Bayer).  
Part 2: Alexander Schacht (Veramed), Charlotta Fruechtenicht (Roche), James Black (Roche), Jeremy Wildfire (Gilead), Madhurima Majumder (Bayer)

Who is this event intended for? Anyone interested in data visualisation in the pharmaceutical industry.
What is the benefit of attending? Gain new insights into data visualisations and learn about the regulatory perspective. With no technical or coding knowledge assumed, a variety of practical workshop exercises will bring to life many of the concepts learned in part one. Topics include graphic design, and interactive data visualisation tools.

Event cost

Part 1
Member rate = £20+VAT
Non-Member rate = £115*+VAT
*Please note: Non-Member rates include membership for the rest of the 2021 calendar year.
Part 2
Member & Non-Member rate
= £60+VAT

Registration

PLEASE NOTE: Registration for Part 1 is compulsory in order to register for Part 2; in so doing, your registration for Part 2 only will be submitted for approval. As there are a limited amount of places available for Part 2, we advise booking early to avoid disappointment!

To register for Part 1 (17/09/21), please click here.
To register for Part 2 (24/09/21), please click here.

Overview

Part 1
Data visualisation has been used to gain insights into medical data for over 150 years. More modern methods include interactive and animated data visualisation tools, and the development of open source code using agile methods. 
This online event will provide a practical introduction to modern methods of data visualisation, including presentations from some well-known and influential speakers in part 1 and practical hands-on workshop exercises in part 2.
To view the agenda for Part 1, please click here.

Part 2
This workshop will provide a practical introduction to modern methods of data visualisation through practical hands-on workshop exercises.
This includes interactive and animated data visualisation tools, and the development of open source code. 
To view the agenda for Part 2, please click here.

Speaker Details

Part 1

 Speaker  Biography  Abstract

Susanedit
Susan Mayo
(FDA)

Susan is a senior mathematical statistician at the Food and Drug Administration, Center for Drug Evaluation’s Office of Biostatistics, with a demonstrated interest and impact in areas that help to make sound regulatory and drug development decisions: graphical design, drug safety and benefit-risk assessment, and the estimand framework. She has been with FDA for over 3 years, and previously worked as an industry statistician and internal company consultant in biotech and big pharma for a few more than that.

 

Making Impactful Graphs: Looking through the eyes of your audience
Anyone who has graphed data has discovered there is both an art and science to doing it well. Graphs take more effort to create than tables, and when constructed well, they have the potential for their audience to see deeper into the data when there are complexities a table may not be able to address. This talk aims to address some overlooked factors beyond the technical aspects of graphing data. What are the human brain’s visual superpowers? How can a graph be more impactful with its audience? The talk will conclude with some considerations for impactful use of interactive graphics. Susan’s talk will cover:

  • Visual perception, and its relationship with statistical graphics design
  • Pharma/regulatory interactions –a personal perspective
  • Impactful use of interactive graphics

 

Jeremyedit
Jeremy Wildfire
(Gilead)

 

Jeremy Wildfire is a Director of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worked in clinical trial research for 15 years, first as a biostatistician on NIH funded asthma and allergy studies and more recently on cross-functional data science teams focused on creating open source tools that seek to improve the clinical trial analysis pipeline.

Building Open Source Tools for Safety Monitoring: Advancing Research Through Community Collaboration
The Interactive Safety Graphics workstream of the ASA-DIA Biopharm Safety Working Group is excited to introduce version 2 of the safetyGraphics R package. safetyGraphics is an interactive framework for evaluating clinical trial safety in R. Version 2 includes support for multiple data domains, reusable data pipelines and user-defined custom charts. This enhanced framework allows users to easily re-use both static and interactive charts on multiple studies. safetyGraphics includes several interactive graphics by default, including a chart for monitoring drug-induced liver injury that is paired with an in-depth clinical workflow. Charts using existing R packages can also be added to the framework via a straightforward mapping process. To ensure quality and accuracy, the package includes more than 300 automated unit tests and has been vetted through a beta testing process that included feedback from more than 20 clinicians and analysts.

The Interactive Safety Graphics group seeks to modernize clinical trial safety monitoring by building tools for data exploration and reporting in a highly collaborative open source environment. At present, our team includes clinical and technical representatives from the pharmaceutical industry, academia, and the FDA, and additional contributors are always welcome

Sheilaedit
Sheila Dickinson (Novartis)

 

 

 

 

Sheila Dickinson is a Global Benefit-Risk Lead, working in the Quantitative Safety and Epidemiology group at Novartis. Her responsibilities include promoting and facilitating the use of a structured benefit-risk approach by Novartis project teams. Sheila is also working on the topic of patient preference studies and is on the management board of the IMI PREFER project, which is working on developing guidelines about when and how to perform patient preference studies to support medical product decision-making.

Sheila holds a degree in mathematics from Imperial College, London and an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine. After joining Novartis in 1997, she worked as a statistician supporting projects in the various disease areas including both diabetes and malaria, before moving to the Quantitative Safety team in 2013.

Points to bear in mind for visual displays of benefit-risk data
Recent years have seen an increased focus on taking a structured approach to the description of benefit-risk in a Clinical Overview. This presentation will cover:

  • The regulatory expectations about structured benefit-risk
  • How these regulatory expectations influence our approach to visual displays of benefit-risk data
  • Suggestions for addressing common issues when displaying benefit-risk data
  • Example benefit-risk data display
  • Comments on using an R-Shiny App as a tool to create benefit-risk figures

 

 

Matthiasedit
Matthias Trampisch (Boehringer Ingelheim)

 

 

 

Matthias works as a Safety Statistician in independent Safety Analysis Team (iSAT) at Boehringer Ingelheim. iSAT is specialized on analyzing ongoing trial data in an unblinded fashion supporting interim analysis, ad-hoc unblinding requests, or Data Monitoring Committees (DMCs).

 

Dynamic data visualization for Benefit/Risk Assessment DURING trial conduct - Insights based on DMC output for monitoring a trial in patients hospitalized with COVID-19
Benefit/Risk assessment is a fundamental part of any clinical trial report. However, during trial conduct, the analysis of Benefit/Risk may substantially differ from the final assessment due to various reasons: ongoing recruitment, incomplete/missing data, time points not (yet) available and so on.

This talk presents learnings from a recent Boehringer Ingelheim trial targeting to prevent Adult Respiratory Distress Syndrom (ARDS) in hospitalized COVID-19 patients. It focuses on a specific output created for the Data Monitoring Committees (DMC) which combined most of the pre-defined efficacy and safety endpoints in an interactive heat-plot. Sample data and code to generate the plot will be provided

Patrickedit
Patrick Schloemer (Bayer)

 

 

 

Patrick received his PhD in Statistics from the University of Bremen, Germany, in 2014 for his work on group sequential and adaptive designs for three-arm non-inferiority trials. Since then he has been working at Bayer as a clinical statistician in the cardio-renal area with increasing responsibilities. Currently he acts as the Compound Statistician for a novel treatment for chronic kidney disease in type 2 diabetes that recently received FDA approval.

His methodological interests include group sequential and adaptive designs, multiple comparison procedures and recurrent events. In the past years he has been working on the application for an EMA “Qualification opinion of clinically interpretable treatment effect measures based on recurrent event endpoints that allow for efficient statistical analyses”. Besides this he has been actively involved in the development of the Data Insight Generation (D.I.G) concept at Bayer, which was recently piloted in a large Phase III trial to gain deeper insights into the study data by means of intelligent visualizations.

 

Data Insight Generation (D.I.G) – A concept to foster interactive and interdisciplinary data investigations using intelligent visualizations
In recent years various data visualization apps have been developed as part of Bayer’s Biostatistics Innovation Center (BIC), with the objective to provide insights that go beyond the classical Tables, Listings and Figures (TLFs) which are the basis for the submission dossier. The Data Insight Generation (D.I.G) concept has been developed to foster and streamline the use of these data visualization apps and position them as a central piece in the interpretation of clinical study data at Bayer. The D.I.G activities culminate in a two-day workshop shortly after TLF delivery in a dedicated, fully-equipped meeting room, where a cross-functional team uses intelligent visualizations and machine learning methods to gain broader and deeper insights into the study data. This talk will give an overview about the D.I.G concept and present first-hand experiences from the pilot D.I.G workshop that was recently held after the close-out of a large Phase III trial.

 

Charlottaedit
Charlotta Fruechtenicht (Roche)

Charlotta is a computational biologist by training and works as a senior data scientist in the Data, Analytics & Imaging team in the Pharma Development Personalized Healthcare department at Roche. Her interests lie in using fit-for-purpose analytics (including graphical design) to untap the wealth of multimodal data coming from healthcare care systems in the real world to support the development of new medicines.

 

Learnings from implementing good graphical principles in a ready-to-use R package
In this talk we will present key learnings from a cross-pharma collaboration tackling the problem of streamlining effective and efficient graphical communication. The application of good graphical principles to the output of statistical analyses, especially in R, can be time consuming and tedious and is thus oftentimes omitted. However, effective visualization is important to enable clear communication between Data Scientists and stakeholders, which makes it crucial to facilitate informed decision making. To enable easy-to-use, multi-purpose visualizations for clinical data, we collaborated across multiple Pharma companies and functions to develop the thoroughly tested R package visR. This new software-package, which is now available on CRAN, enables seamless integration of effective visualization (figures and tables) into analytics and reporting workflows.

 

 

Part 2

 Speaker  Biography  Abstract

Alexanderedit
Alexander Schacht (Veramed)

 

 

I studied mathematics and received my PhD in biostatistics on non-parametric statistics from the University of Göttingen in Germany. I authored more than 70 scientific manuscripts in peer-reviewed journals and regularly speak at international conferences – both statistical ones like PSI and medical ones like EADV. During my career at university and within the pharma industry, I have collected more than 20 years of experience.

My career focused mostly on phase III and IV (RCT, observational studies, HTA submission, commercialization work) with some regulatory work as well as some experience in the early phases of clinical development.

I’m interested in a broad range of methodological areas but specifically on making better decisions based on data. As such, I was the chair of the EFSPI/SPI SIG on benefit-risk for some time. PSI provided my with many more opportunities, which I’m happy to work on.

At work, I supervise of a small but mighty team of statisticians in a large pharma company. The virtual work environment requires me to adjust my communication style and focus on my ability to deliver results effectively.

I’m a happy husband and father of 3 wonderful kids, who I love to spend time with. In the rest of my time, I love running and listening to podcasts.

Data Visualisation Paper and Pen Exercise
This activity will use low-tech methods (paper and pencil), in which participants will sketch out the design of an optimum graph, to communicate trends or patterns in the data. With no programming skills needed, the focus will be on applying good design principles, without the distraction of using a particular software package.

 

Charlottaedit
Charlotta Fruechtenicht (Roche)

 

 

Charlotta is a computational biologist by training and works as a senior data scientist in the Data, Analytics & Imaging team in the Pharma Development Personalized Healthcare department at Roche. Her interests lie in using fit-for-purpose analytics (including graphical design) to untap the wealth of multimodal data coming from healthcare care systems in the real world to support the development of new medicines.

How to implement effective visualisations in R using visR
This workshop will give a hands-on introduction to visR. It will show you how to create figures and tables for data exploration and time to event analyses adhering to good graphical principles. in only a few lines of R code.

 

 

Jeremyedit
Jeremy Wildfire
(Gilead)

 

 

Jeremy Wildfire is a Director of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worked in clinical trial research for 15 years, first as a biostatistician on NIH funded asthma and allergy studies and more recently on cross-functional data science teams focused on creating open source tools that seek to improve the clinical trial analysis pipeline.

ASA Safety Monitoring Tool Exercise
This workshop will provide a hands-on introduction to the safetyGraphics R package. Participants will load the newest version of the R package and then use the safetyGraphics shiny app to interactively explore the safety signals in a sample study - implementing customizations along the way

Madhurimaedit
Madhurima Majumder (Bayer)

 

 

Madhurima Majumder is a Senior Manager of Statistics and Data Insights at Bayer US LLC. Since joining Bayer in 2017, Madhurima has supported cardiovascular and thrombosis studies. She is also a member of various cross-industry working initiatives like the Drug Information Association’s (DIA) Clinically Meaningful Change Group, The Forum for Collaborative Research, etc. Her research interest focuses not only in statistical methodology but also in developing user-friendly data visualization tools for clinical trial results. She holds a PhD in Statistics from the University of Rochester, New York.

Elaborator App
elaborator is a comprehensive and easy-to-use interactive browser-based application developed by Bayer’s Biostatistics Innovation Center to visualize huge numbers of laboratory parameters measured at several visits for several treatment groups. It is implemented in the statistical software R. This session introduces participants to the elaborator package through live demonstration with an example. The session consists of launching the app in RStudio, discussing the format and uploading the data, interactively visualizing three types of analyses: frequently occurring changes in laboratory values, treatment-related changes, and changes beyond normal ranges. After the session, participants are expected to be familiar with elaborator’s general functionalities.

 

Alexander Schacht (Veramed)

 

Improving a “bad” graph
Attendees will learn how to improve an existing graph by applying principles of good graphical design.

 

 


Cancellation and Moderation Terms
For cancellations received up to two weeks prior to a PSI event start-date, the event registration fee will be refunded less 25% administrative charge. After this date, no refunds will be possible. A handling fee of 20 GBP per registration will be charged for every registration modification received two weeks prior or less, including a delegate name change.

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