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DTSTART;VALUE=DATE:20230101
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BEGIN:VEVENT
DESCRIPTION:\n\n\n\n\n\n\nDate: Friday 17th &\; Friday 24th September 20
21 (Please note: this event is split into 2 parts)\nTime: 13:00-17:00 BST
both days\nSpeakers: \;\nPart 1: \;Susan Mayo (FDA)\, Jeremy Wildf
ire (Gilead)\, Sheila Dickinson (Novartis)\, Matthias Trampisch (Boehringe
r Ingelheim)\, Charlotta Fruechtenicht (Roche)\, Patrick Schlö\;mer (B
ayer). \; \;\nPart 2: \;Alexander Schacht (Veramed)\, Charlott
a Fruechtenicht (Roche)\, James Black (Roche)\, Jeremy Wildfire (Gilead)\,
Madhurima Majumder (Bayer)\n\nWho is this event intended for? \;Anyon
e interested in data visualisation in the pharmaceutical industry.\nWhat i
s the benefit of attending? \;Gain new insights into data visualisatio
ns 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 d
esign\, and interactive data visualisation tools.\nEvent cost\nPart 1\nMem
ber rate = £\;20+VAT\nNon-Member rate = £\;115*+VAT\n*Please not
e: \;Non-Member rates include membership for the rest of the 2021 cale
ndar year.\nPart 2\nMember &\; Non-Member rate = £\;60+VAT\nRegist
ration\nPLEASE NOTE: \;Registration for Part 1 is \;compulsory&nbs
p\;in order to register for Part 2\; in so doing\, your registration for&n
bsp\;Part 2 only \;will be submitted for approval. As there are a limi
ted amount of places available for Part 2\, we advise booking early to avo
id disappointment!\n\nTo register for Part 1 (17/09/21)\, please click her
e.\nTo register for Part 2 (24/09/21)\, please click here.\nOverview\nPart
1\nData visualisation has been used to gain insights into medical data fo
r over 150 years. More modern methods include interactive and animated dat
a visualisation tools\, and the development of open source code using agil
e methods. \;\nThis online event will provide a practical introduction
to modern methods of data visualisation\, including presentations from so
me well-known and influential speakers in part 1 and practical hands-on wo
rkshop exercises in part 2.\nTo view the agenda for Part 1\, please click
here.\n\n\nPart 2\nThis workshop will provide a practical introduction to
modern methods of data visualisation through practical hands-on workshop e
xercises.\nThis includes interactive and animated data visualisation tools
\, and the development of open source code. \;\nTo view the agenda for
Part 2\, please click here.\n\nSpeaker Details\nPart 1\n\n \n \
n  \;Speaker\n  \;Biography\n &nb
sp\;Abstract\n \n \n \n \n
Susan Mayo \n (FDA)\n \n \n Su
san is a senior mathematical statistician at the Food and Drug Administrat
ion\, Center for Drug Evaluation&rsquo\;s Office of Biostatistics\, with a
demonstrated interest and impact in areas that help to make sound regulat
ory and drug development decisions: graphical design\, drug safety and ben
efit-risk assessment\, and the estimand framework. She has been with FDA f
or over 3 years\, and previously worked as an industry statistician and in
ternal company consultant in biotech and big pharma for a few more than th
at.\n  \;\n \n \n Making I
mpactful Graphs: Looking through the eyes of your audience\n An
yone 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 deep
er into the data when there are complexities a table may not be able to ad
dress. This talk aims to address some overlooked factors beyond the techni
cal aspects of graphing data. What are the human brain&rsquo\;s visual sup
erpowers? How can a graph be more impactful with its audience? The talk wi
ll conclude with some considerations for impactful use of interactive grap
hics. Susan&rsquo\;s talk will cover:\n \n Visua
l perception\, and its relationship with statistical graphics design\n
Pharma/regulatory interactions &ndash\;a personal perspective\
n Impactful use of interactive graphics\n \n
 \;\n \n \n \n \n
\n Jeremy Wildfire \n (Gilead)\n  
\;\n \n \n Jeremy Wildfire is a Director
of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worke
d in clinical trial research for 15 years\, first as a biostatistician on
NIH funded asthma and allergy studies and more recently on cross-functiona
l data science teams focused on creating open source tools that seek to im
prove the clinical trial analysis pipeline.\n \n \n
Building Open Source Tools for Safety Monitoring: Advancing Res
earch Through Community Collaboration\n The \;Interactive S
afety Graphics workstream \;of the ASA-DIA Biopharm Safety Working Gro
up is excited to introduce version 2 of the \;safetyGraphics \;R p
ackage. 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 fra
mework 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 pac
kages can also be added to the framework via a straightforward mapping pro
cess. To ensure quality and accuracy\, the package includes more than 300
automated unit tests and has been vetted through a beta testing process th
at included feedback from more than 20 clinicians and analysts.\n
The \;Interactive Safety Graphics \;group seeks to modernize cl
inical 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 pharmace
utical industry\, academia\, and the FDA\, and additional contributors are
always welcome\n \n \n \n \n
\n \n \n Sheila Dickinson (Novartis)\n
 \;\n  \;\n  \;\n
 \;\n \n \n Sheila Dickinson is a Gl
obal Benefit-Risk Lead\, working in the Quantitative Safety and Epidemiolo
gy group at Novartis. Her responsibilities include promoting and facilitat
ing the use of a structured benefit-risk approach by Novartis project team
s. Sheila is also working on the topic of patient preference studies and i
s on the management board of the IMI PREFER project\, which is working on
developing guidelines about when and how to perform patient preference stu
dies to support medical product decision-making.\n Sheila holds
a degree in mathematics from Imperial College\, London and an MSc in Medi
cal Statistics from the London School of Hygiene and Tropical Medicine. Af
ter joining Novartis in 1997\, she worked as a statistician supporting pro
jects in the various disease areas including both diabetes and malaria\, b
efore moving to the Quantitative Safety team in 2013.\n \n
\n Points to bear in mind for visual displays of benefit
-risk data\n Recent years have seen an increased focus on takin
g a structured approach to the description of benefit-risk in a Clinical O
verview. This presentation will cover:\n \n The
regulatory expectations about structured benefit-risk\n How
these regulatory expectations influence our approach to visual displays o
f benefit-risk data\n Suggestions for addressing common iss
ues when displaying benefit-risk data\n Example benefit-ris
k data display\n Comments on using an R-Shiny App as a tool
to create benefit-risk figures\n \n  \;\n
 \;\n \n \n \n \n
\n Matthias Trampisch (Boehringer Ingelheim)\n &nbs
p\;\n  \;\n  \;\n \n \
n Matthias works as a Safety Statistician in independent Safety
Analysis Team (iSAT) at Boehringer Ingelheim. iSAT is specialized on anal
yzing ongoing trial data in an unblinded fashion supporting interim analys
is\, ad-hoc unblinding requests\, or Data Monitoring Committees (DMCs). \n
 \;\n \n \n Dynamic data
visualization for Benefit/Risk Assessment DURING trial conduct - Insights
based on DMC output for monitoring a trial in patients hospitalized with C
OVID-19\n Benefit/Risk assessment is a fundamental part of any
clinical trial report. However\, during trial conduct\, the analysis of Be
nefit/Risk may substantially differ from the final assessment due to vario
us reasons: ongoing recruitment\, incomplete/missing data\, time points no
t (yet) available and so on.\n This talk presents learnings fr
om a recent Boehringer Ingelheim trial targeting to prevent Adult Respirat
ory Distress Syndrom (ARDS) in hospitalized COVID-19 patients. It focuses
on a specific output created for the Data Monitoring Committees (DMC) whic
h combined most of the pre-defined efficacy and safety endpoints in an int
eractive heat-plot. Sample data and code to generate the plot will be prov
ided\n \n \n \n \n \n
Patrick Schloemer (Bayer)\n  \;\n  \;\
n  \;\n \n \n Patrick rece
ived his PhD in Statistics from the University of Bremen\, Germany\, in 20
14 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. C
urrently he acts as the Compound Statistician for a novel treatment for ch
ronic kidney disease in type 2 diabetes that recently received FDA approva
l.\n 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 &ldquo\;
Qualification opinion of clinically interpretable treatment effect measure
s based on recurrent event endpoints that allow for efficient statistical
analyses&rdquo\;. Besides this he has been actively involved in the develo
pment 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 t
he study data by means of intelligent visualizations. \n  \
;\n \n \n Data Insight Generation (D.I.G)
&ndash\; A concept to foster interactive and interdisciplinary data inves
tigations using intelligent visualizations\n In recent years va
rious data visualization apps have been developed as part of Bayer&rsquo\;
s Biostatistics Innovation Center (BIC)\, with the objective to provide in
sights that go beyond the classical Tables\, Listings and Figures (TLFs) w
hich 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 th
ese data visualization apps and position them as a central piece in the in
terpretation of clinical study data at Bayer. The D.I.G activities culmina
te in a two-day workshop shortly after TLF delivery in a dedicated\, fully
-equipped meeting room\, where a cross-functional team uses intelligent vi
sualizations and machine learning methods to gain broader and deeper insig
hts into the study data. This talk will give an overview about the D.I.G c
oncept and present first-hand experiences from the pilot D.I.G workshop th
at was recently held after the close-out of a large Phase III trial.\n
 \;\n \n \n \n \n
\n Charlotta Fruechtenicht (Roche)\n \n
\n Charlotta is a computational biologist by training and wor
ks 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 des
ign) to untap the wealth of multimodal data coming from healthcare care sy
stems in the real world to support the development of new medicines.\n
 \;\n \n \n Learnings from im
plementing good graphical principles in a ready-to-use R package\n
In this talk we will present key learnings from a cross-pharma collabo
ration tackling the problem of streamlining effective and efficient graphi
cal communication. The application of good graphical principles to the out
put 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 sta
keholders\, 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 visuali
zation (figures and tables) into analytics and reporting workflows.\n
 \;\n \n \n \n\n \n \;\nPart 2\n\n
\n \n\n\n \n \n  \;Speaker\n  \
;Biography\n  \;Abstract\n \n \n \
n \n Alexander Schacht (Veramed)\n  \
;\n  \;\n \n \n I studied
mathematics and received my PhD in biostatistics on non-parametric statist
ics from the University of Gö\;ttingen in Germany. I authored more tha
n 70 scientific manuscripts in peer-reviewed journals and regularly speak
at international conferences &ndash\; both statistical ones like PSI and m
edical ones like EADV. During my career at university and within the pharm
a industry\, I have collected more than 20 years of experience.\n
My career focused mostly on phase III and IV (RCT\, observational studi
es\, HTA submission\, commercialization work) with some regulatory work as
well as some experience in the early phases of clinical development.\n
I&rsquo\;m interested in a broad range of methodological areas bu
t specifically on making better decisions based on data. As such\, I was t
he chair of the EFSPI/SPI SIG on benefit-risk for some time. PSI provided
my with many more opportunities\, which I&rsquo\;m happy to work on.\n
At work\, I supervise of a small but mighty team of statisticians
in a large pharma company. The virtual work environment requires me to adj
ust my communication style and focus on my ability to deliver results effe
ctively.\n I&rsquo\;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 run
ning and listening to podcasts.\n \n \n D
ata Visualisation Paper and Pen Exercise\n This activity will u
se 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 applyi
ng good design principles\, without the distraction of using a particular
software package.\n  \;\n \n \n \n
\n \n Charlotta Fruechtenicht (Roche)\n
 \;\n  \;\n \n \n
Charlotta is a computational biologist by training and works as a s
enior data scientist in the Data\, Analytics &\; Imaging team in the Ph
arma Development Personalized Healthcare department at Roche. Her interest
s lie in using fit-for-purpose analytics (including graphical design) to u
ntap the wealth of multimodal data coming from healthcare care systems in
the real world to support the development of new medicines.\n \
n \n How to implement effective visualisations in R
using visR\n This workshop will give a hands-on introduction to
visR. It will show you how to create figures and tables for data explorat
ion and time to event analyses adhering to good graphical principles. in o
nly a few lines of R code.\n  \;\n  \;\n
\n \n \n \n \n Jere
my Wildfire \n (Gilead)\n  \;\n  
\;\n \n \n Jeremy Wildfire is a Director
of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worke
d in clinical trial research for 15 years\, first as a biostatistician on
NIH funded asthma and allergy studies and more recently on cross-functiona
l data science teams focused on creating open source tools that seek to im
prove the clinical trial analysis pipeline.\n \n \n
ASA Safety Monitoring Tool Exercise\n This workshop
will provide a hands-on introduction to the safetyGraphics R package. Part
icipants will load the newest version of the R package and then use the sa
fetyGraphics shiny app to interactively explore the safety signals in a sa
mple study - implementing customizations along the way\n \n
\n \n \n \n Madhurima Majumder
(Bayer)\n  \;\n  \;\n \n
\n Madhurima Majumder is a Senior Manager of Statistics and
Data Insights at Bayer US LLC. Since joining Bayer in 2017\, Madhurima ha
s supported cardiovascular and thrombosis studies. She is also a member of
various cross-industry working initiatives like the Drug Information Asso
ciation&rsquo\;s (DIA) Clinically Meaningful Change Group\, The Forum for
Collaborative Research\, etc. Her research interest focuses not only in st
atistical methodology but also in developing user-friendly data visualizat
ion tools for clinical trial results. She holds a PhD in Statistics from t
he University of Rochester\, New York.\n \n \n
Elaborator App\n elaborator is a comprehensive and easy-t
o-use interactive browser-based application developed by Bayer&rsquo\;s Bi
ostatistics Innovation Center to visualize huge numbers of laboratory para
meters measured at several visits for several treatment groups. It is impl
emented in the statistical software R. This session introduces participant
s to the elaborator package through live demonstration with an example. Th
e session consists of launching the app in RStudio\, discussing the format
and uploading the data\, interactively visualizing three types of analyse
s: frequently occurring changes in laboratory values\, treatment-related c
hanges\, and changes beyond normal ranges. After the session\, participant
s are expected to be familiar with elaborator&rsquo\;s general functionali
ties.\n  \;\n \n \n \n
\n Alexander Schacht (Veramed)\n \n \n
 \;\n \n \n Improving a &ldq
uo\;bad&rdquo\; graph\n Attendees will learn how to improve an
existing graph by applying principles of good graphical design.\n
 \;\n \n \n \n\n\n \;\n\n\nCancellation an
d Moderation Terms\nFor 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 registra
tion modification received two weeks prior or less\, including a delegate
name change.
DTEND:20210924T160000Z
DTSTAMP:20240328T155406Z
DTSTART:20210917T120000Z
LOCATION:
SEQUENCE:0
SUMMARY:PSI Scientific Meeting: Generating Insights through Modern Applicat
ions of Data Visualisation
UID:RFCALITEM638472380467251668
X-ALT-DESC;FMTTYPE=text/html: Part 1 PLEASE NOTE: \;Registration fo
r Part 1 is \;compu
lsory \;in order to register for Part 2\; in so doing\
, your registration for \;P
art 2 only \;will be submitted for approval. As there are a lim
ited amount of places available for Part 2\, we advise booking early to av
oid disappointment! Part 1
\n
\n
\n
\n
\n\n
\nDate: Friday 17th &\; Friday 24th Septem
ber 2021 (Please note: this event is split into 2 parts)
\n<
strong>Time: 13:00-17:00 BST both days
\nSpeakers:
strong> \;
\nP
art 1: \;Susan Mayo (FDA)\, Jeremy Wildfire (Gilead)\,
Sheila Dickinson (Novartis)\, Matthias Trampisch (Boehringer Ingelheim)\,
Charlotta Fruechtenicht (Roche)\, Patrick Schlö\;mer (Bayer). \;&
nbsp\;
\nPart 2:
span> \;Alexander Schacht (Veramed)\, Charlotta Fruechtenicht
(Roche)\, James Black (Roche)\, Jeremy Wildfire (Gilead)\, Madhurima Maju
mder (Bayer)
\n
\nWho is this event intended for? \;Anyone interested in data visualisation in the pharmaceutical ind
ustry.
\nWhat is the benefit of attending? \;Gai
n new insights into data visualisations and learn about the regulatory per
spective. With no technical or coding knowledge assumed\, a variety of pra
ctical workshop exercises will bring to life many of the concepts learned
in part one. Topics include graphic design\, and interactive data visualis
ation tools.\nEvent cost
\n
\nMember rate
= £\;20+VAT
\nNon-Member rate = £\;115*+VA
T
\n*Please note: \;Non-Membe
r rates include membership for the rest of the 2021 calendar year.<
/em>
\nPart 2
\nMember &\; Non-Member rate = £\;60+VATRegistration
\n
\n
\nTo register for Part 1 (17/09/21)\, ple
ase
click here.
\nTo register for Part 2 (24/09/21)\, please
cli
ck here.Overview
\n
\nData 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. \;
\nThis online event will provide a practical introduction to modern metho
ds of data visualisation\, including presentations from some well-known an
d influential speakers in part 1 and practical hands-on workshop exercises
in part 2.
\nTo view the agenda for Part 1\, please click here.
\n
\n
\nPart
2
\nThis workshop will provide a practical introduct
ion to modern methods of data visualisation through practical hands-on wor
kshop exercises.
\nThis includes interactive and animated data visual
isation tools\, and the development of open source code. \;
\n
\n
 \;Speaker | \n \;Biography | \n \;Abst ract | \n
\n
| \n Susan is a senior mathematical statistician at the Food and Drug Administr ation\, Center for Drug Evaluation&rsquo\;s Office of Biostatistics\, with a demonstrated interest and impact in areas that help to make sound regul atory and drug development decisions: graphical design\, drug safety and b enefit-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. \n \; \n | \n \n Making Impact
ful Graphs: Looking through the eyes of your audience
 \; \n | \n
\n
 \; \n | \n
\n Jeremy Wildfir e 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 bio statistician 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. \n | \n \n
Building Open Source Tools for Safety Monitoring: Advancing R
esearch Through Community Collaboration The \
;I
nteractive Safety Graphics \;group seeks to modernize clinical tri
al safety monitoring by building tools for data exploration and reporting
in a highly collaborative open source environment. At present\, our team i
ncludes clinical and technical representatives from the pharmaceutical ind
ustry\, academia\, and the FDA\, and additional contributors are always we
lcome | \n
\n
 \; \n  \; \n \; \n \; \n | \n \n
Sheila Dickinson is a Global Benefit-Risk Lead\, working in the Quanti tative Safety and Epidemiology group at Novartis. Her responsibilities inc lude promoting and facilitating the use of a structured benefit-risk appro ach by Novartis project teams. Sheila is also working on the topic of pati ent preference studies and is on the management board of the IMI PREFER pr oject\, which is working on developing guidelines about when and how to pe rform patient preference studies to support medical product decision-makin g. \nSheila holds a degree in mathematics from Imperial College\, London and an MSc in Medical Statistics from the London School o f Hygiene and Tropical Medicine. After joining Novartis in 1997\, she work ed as a statistician supporting projects in the various disease areas incl uding both diabetes and malaria\, before moving to the Quantitative Safety team in 2013. \n | \n \n Points to bear in mind for visua
l displays of benefit-risk data
 \; \n \; \n | \n
\n
 \; \n \; \n \; \n | \n \n 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 (DM Cs). \n \; \n | \n \n Dynamic data
visualization for Benefit/Risk Assessment DURING trial conduct - Insights
based on DMC output for monitoring a trial in patients hospitalized with C
OVID-19 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 safe ty endpoints in an interactive heat-plot. Sample data and code to generate the plot will be provided \n | \n
\n
 \; \n \; \n \; \n | \n
\n Patrick received his PhD in Statistics from the University of Bremen\, Germany\, in 2014 f or his work on group sequential and adaptive designs for three-arm non-inf eriority trials. Since then he has been working at Bayer as a clinical sta tistician in the cardio-renal area with increasing responsibilities. Curre ntly he acts as the Compound Statistician for a novel treatment for chroni c kidney disease in type 2 diabetes that recently received FDA approval. p>\n His methodological interests include group sequential a nd adaptive designs\, multiple comparison procedures and recurrent events. In the past years he has been working on the application for an EMA &ldqu o\;Qualification opinion of clinically interpretable treatment effect meas ures based on recurrent event endpoints that allow for efficient statistic al analyses&rdquo\;. Besides this he has been actively involved in the dev elopment of the Data Insight Generation (D.I.G) concept at Bayer\, which w as recently piloted in a large Phase III trial to gain deeper insights int o the study data by means of intelligent visualizations. \n \; \n | \n \n Data Insight Generation (D.I.G) &nd
ash\; A concept to foster interactive and interdisciplinary data investiga
tions using intelligent visualizations  \; \n | \n
\n
| \n \n Charlotta is a computational biologist by training and works as a senior data scientist in the Data\, A nalytics &\; Imaging team in the Pharma Development Personalized Health care department at Roche. Her interests lie in using fit-for-purpose analy tics (including graphical design) to untap the wealth of multimodal data c oming from healthcare care systems in the real world to support the develo pment of new medicines. \n \; \n |
\n \n
 \; \n | \n
\n
 \ ;Speaker | \n \;Biography | \n \;Abstract | \n
\n
 \; \n&nb sp\; \n | \n \n I studied mathematics and received my PhD in biost atistics on non-parametric statistics from the University of Gö\;tting en in Germany. I authored more than 70 scientific manuscripts in peer-revi ewed journals and regularly speak at international conferences &ndash\; bo th 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. \nMy career focused mostly on ph ase III and IV (RCT\, observational studies\, HTA submission\, commerciali zation work) with some regulatory work as well as some experience in the e arly phases of clinical development. \nI&rsquo\;m intere sted in a broad range of methodological areas but specifically on making b etter 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 opportun ities\, which I&rsquo\;m happy to work on. \nAt work\, I supervise of a small but mighty team of statisticians in a large pharma c ompany. The virtual work environment requires me to adjust my communicatio n style and focus on my ability to deliver results effectively. \nI&rsquo\;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 lis tening to podcasts. \n | \n \n Data Visualisation Paper an
d Pen Exercise  \; \n | \n
\n
\n Charlotta Fruechtenicht (Roche) \n \; \n \; \n | \n
\n Charlotta is a computational biologist by training and works as a senior data scientist in the Data\, Analytics &\; Imaging team in the Pharma Development Pers onalized Healthcare department at Roche. Her interests lie in using fit-fo r-purpose analytics (including graphical design) to untap the wealth of mu ltimodal data coming from healthcare care systems in the real world to sup port the development of new medicines. \n | \n
\n How to i
mplement effective visualisations in R using visR  \; \n \; \n | \n
\n
 \; \n \; p>\n | \n \n Jeremy Wildfire is a Director of Biostatistics on the Ad vance Analytics team at Gilead. Jeremy has worked in clinical trial resear ch for 15 years\, first as a biostatistician on NIH funded asthma and alle rgy studies and more recently on cross-functional data science teams focus ed on creating open source tools that seek to improve the clinical trial a nalysis pipeline. \n | \n \n ASA Safety Monitoring Tool Ex
ercise | \n
\n
 \; \n \; p>\n | \n \n Madhurima Majumder is a Senior Manager of Statistics and Data Insights at Bayer US LLC. Since joining Bayer in 2017\, Madhurima ha s supported cardiovascular and thrombosis studies. She is also a member of various cross-industry working initiatives like the Drug Information Asso ciation&rsquo\;s (DIA) Clinically Meaningful Change Group\, The Forum for Collaborative Research\, etc. Her research interest focuses not only in st atistical methodology but also in developing user-friendly data visualizat ion tools for clinical trial results. She holds a PhD in Statistics from t he University of Rochester\, New York. \n | \n
\n Elaborat
or App  \; \n | \n
\n
Alexander Schacht (Veramed) \n | \n
\n  \; p>\n | \n \n Improving a &ldquo\;bad&rdquo\; graph  \; \n | \n