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DTSTART;VALUE=DATE:20230101
TZNAME:UTC
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BEGIN:VEVENT
DESCRIPTION:The aim of this meeting is to provide a relaxed environment for
career young statisticians\nwhere they can present and discuss various st
atistical topics and interact/network with other statisticians in similar
positions to themselves. The PSI conference and similar events can be daun
ting for some attendees both in terms of who is in the audience and also i
n being happy that what they present is deemed new and original. For this
one day meeting the audience will be peers with similar levels of experien
ce and will include content of interest to career young statisticians. In
addition to presentations from fellow statisticians there will be a soft s
kills workshop on explaining statistics to non‐statisticians and ample opp
ortunity to connect with colleagues across the industry. CLICK HERE TO VI
EW THE FLYER \nRegistration Costs: PSI Members \;  \;£\;2
5 + VAT Non-Members  \;£\;95 + VAT Registration has now clo
sed. The agenda for the meeting can be found below: 9.30 -9.55 Reg
istration 9.55 &ndash\; 10.00 Welcome and Introduction 10.00 &n
dash\; 11.00 One label with different interpretations&hellip\;Statistica
l lead\, what does it really mean?  \; Charlotte Eden and Lucie
Tesarova\, QuintilesIMS 11.00 &ndash\; 11.20 Break 11.20 &ndash\
; 11.50 Exploring recent developments in the MAMS methodology for design
ing an adaptive trial  \; Julia Abery\, University of Reading
11.50 &ndash\; 12.20 Seamless Study Designs &\; Real-time Data Capt
ure Using Electronic Devices  \; Rhian Jacob\, Roche 12.20 &
ndash\; 13.20 Lunch 13.20 &ndash\; 14:20 Training session: Explain
ing statistics to non-statisticians  \; James Matcham\, AstraZen
eca / PSI Training Committee 14.20 &ndash\; 14.40 Break 14.40 &n
dash\; 15.10 Introduction to Pharmacokinetics  \; Laura Cope\,
Quanticate 15.10 &ndash\; 15.40 AdePro &ndash\; A new Perspective
on Safety Profiles  \; Nicole Mentenich\, Christoph Tasto and Ba
stian Becker\, Bayer AG 15.40 &ndash\; 16.00 Break 16.00 &ndash\
; 16.30 Marginal and conditional structural mean models for optimising D
ynamic Treatment Regimes  \; Nirav Ratia\, GSK 16.30 Clos
e  \;  \;One label with different interpretations&hellip\;Stat
istical lead\, what does it really mean? Charlotte Eden and Lucie Tesa
rova\, QuintilesIMS Exploring the role of the Statistical lead from diff
erent perspectives. Focusing on life in a CRO to experiences in Pharma wit
h audience participation to discuss how we work on some key factors throug
hout the life of a study.  \; Exploring recent developments in th
e MAMS methodology for designing an adaptive trial Julia Abery\, Unive
rsity of Reading Multi-arm adaptive trials allow several new drugs to be
assessed simultaneously\, potentially giving improved efficiency over con
ventionally designed trials. In such a trial\, data accumulating during th
e study are utilised to inform decisions about how the remainder of the tr
ial should be conducted. Several different methodological approaches have
been developed for multi-arm adaptive trials and a number of studies have
compared the performance of different subsets of these proposed methods. T
he comparisons have not\, however\, considered the so-called MAMS approach
\, since methodology for the latter did not incorporate strong control of
the family-wise error rate (FWER). Recently\, the MAMS approach has been e
xtended such that strong control of the FWER can be guaranteed. Furthermor
e\, an automated process has been developed which can produce efficient de
signs for trials with any number of stages and treatment arms.Since MAMS i
s relatively easy to understand and implement\, we set out to explore thes
e developments and to compare MAMS to other well established methods.We sh
ow how MAMS compares favourably with the more established combination meth
od for some scenarios and explore how use of a novel selection rule may of
fer a further option within MAMS methodology.  \; Seamless Study D
esigns &\; Real-time Data Capture Using Electronic Devices Rhian Ja
cob\, Roche Developing a new drug in a highly competitive environment ne
cessitates a fast to market approach. \; This talk looks at the ongoin
g '944' trial\, a multiple cohort seamless phase II/III study. Key study e
ndpoints are captured using electronic devices\, providing high volume of
data in real-time. This talk describes the opportunities and challenges fo
r a statistician leading a phase II readout within a phase III framework\,
with discussion on novel data capture methods\; is the industry ready to
handle device data?  \; Training session: Explaining statistics to
non-statisticians  \; James Matcham\, AstraZeneca / PSI Training
Committee  \;  \; Introduction to Pharmacokinetics Laura
Cope\, Quanticate Pharmacokinetics is the study of the effect of the bo
dy on the drug. The pharmacokinetic profile maps the concentration of the
drug in the body over time tracking how the drug is absorbed\, distributed
\, metabolised and excreted by individuals. Drug concentration is linked t
o both efficacy and safety and hence pharmacokinetic studies form the basi
s of much earlier phase clinical trials. Pharmacokinetic profiles are ofte
n described using summary measures such as the area under the curve (AUC)
and the maximum concentration (Cmax) but how are these parameters derived?
What models are used and what assumptions do they make? This presentation
will describe the derivation of pharmacokinetic parameters for both intra
venous and extravascular dosing\, first looking at a single dose and then
extending to multiple dosing schedules.  \; AdePro &ndash\; A new
Perspective on Safety Profiles Nicole Mentenich\, Christoph Tasto and
Bastian Becker\, Bayer AG The database in a clinical trial contains vas
t information on adverse events\, involving hundreds of different adverse
event terms with varying severity grades and different start and end dates
. Despite this plethora of information\, insight into the adverse events i
n a clinical study is usually limited to simple summary tables of absolute
and relative numbers of adverse event occurrences. AdEPro &ndash\; an inn
ovation of Bayer&rsquo\;s Biostatistics Innovation Center &ndash\; is an u
nparalleled approach to audio-visualize the safety profile of both the ind
ividual patient and of the entire study cohort\, which enables every study
team member to experience the study and emphasize with the patients. The
AdEPro app depicts the temporal progress of all adverse events in every st
udy subject and enables the user to give profound answers to complex quest
ions surrounding adverse events such as the frequency\, duration and corre
lation of adverse events of interest.  \; Marginal and conditional
structural mean models for optimising Dynamic Treatment Regimes Nirav
Ratia\, GSK Marginal structural models (MSMs) are casual models of dyna
mic treatment regimes (also known as treatment strategies or policies) des
igned to adjust for time-dependent confounding. These models are designed
to adjust for exposures or treatment that vary over time\, and standard ap
proaches for adjustment of confounding can be biased when there exist time
-dependent confounding. Dynamic treatment regimes (DTRs) provide the basis
for statistical analysis in personalised medicine. A DTR is a decision ru
le that guides the treatment choices over the course of the therapy. The s
equence of treatments a patient receives depends on the patient&rsquo\;s h
ealth status\, response to prior treatment and other patient characteristi
cs.  \;
DTEND:20170619T163000Z
DTSTAMP:20240328T112245Z
DTSTART:20170619T093000Z
LOCATION:RG2 6UU\,Reading\,QuintilesIMS\, 500 Brook Drive
SEQUENCE:0
SUMMARY:PSI One Day Meeting: Career Young Statisticians
UID:RFCALITEM638472217653938622
X-ALT-DESC;FMTTYPE=text/html:The aim of this meeting is to provide a relaxe
d environment for career young statisticians
\nwhere they can present
and discuss various statistical topics and interact/network with other st
atisticians in similar positions to themselves. The PSI conference and sim
ilar events can be daunting for some attendees both in terms of who is in
the audience and also in being happy that what they present is deemed new
and original. For this one day meeting the audience will be peers with sim
ilar levels of experience and will include content of interest to career y
oung statisticians. In addition to presentations from fellow statisticians
there will be a soft skills workshop on explaining statistics to non‐stat
isticians and ample opportunity to connect with colleagues across the indu
stry.
CLICK HERE TO VIEW THE FLYER
\nRegistration Costs:
PSI Members \; |  \;£\;25 + VAT |
Non-Members |  \;£\;95 + VAT |
The agenda for
the meeting can be found below:
9.30 -9.55 | Registration |
9.55 &nd ash\; 10.00 | Welcome and Introduction |
10.00 &ndash\; 11.00 | One label with different interpretations&hellip\;Statistical lead \, what does it really mean? |
 \; | Charlotte Eden and Lucie Tesarova\, QuintilesIMS | 11.00 &ndash\; 11.20 < /td> | Break |
11.20 &ndash\; 11.50 | Exploring recent developmen ts in the MAMS methodology for designing an adaptive trial |
 \; | Julia Abery\, University of Reading< /p> |
11.50 &nd ash\; 12.20 | Seamless Study Designs &\; Real-time Data Capture Using Electronic Devices |
 \; < /td> | Rhian Jacob\, Roche < /td> |
12.20 &ndash\; 13.20 | Lunch |
13.20 &ndash\; 1 4:20 | Training sessio n: Explaining statistics to non-statisticians |
 \; | James Matcham\, AstraZeneca / PSI Training Commit tee |
14.20 &ndash\; 14.40 | Brea k |
14.40 & ndash\; 15.10 | Introd uction to Pharmacokinetics |
 \; | Laura Cope\, Quanticate |
15.10 &ndash\; 15.40 | AdePro &ndash\; A new Perspective on Safety Profile s |
 \; | Nicole Mentenich\, Christoph Tasto and Bastian Becker\, Bayer AG |
15.40 &ndash\; 16.00 | Break |
16.00 &ndash\; 16.30 | Marginal and conditional structural me an models for optimising Dynamic Treatment Regimes |
Nirav Ratia\, GSK | |
16.30 | Close |
  \;
 \;One label with different interpretations&hellip\;Statisti cal lead\, what does it really mean?
Charlotte Eden and Lucie Tesarova\, QuintilesIMS | Exploring the role of the Statistical lead f rom different perspectives. Focusing on life in a CRO to experiences in Ph arma with audience participation to discuss how we work on some key factor s throughout the life of a study. |
&n bsp\;
Exploring recent developments in the MAMS methodology for des igning an adaptive trial
Julia Abery \, University of Reading | Multi-arm adaptive trials allow several new drugs to be assessed simu ltaneously\, potentially giving improved efficiency over conventionally de signed trials. In such a trial\, data accumulating during the study are ut ilised to inform decisions about how the remainder of the trial should be conducted. Several different methodological approaches have been developed for multi-arm adaptive trials and a number of studies have compared the p erformance of different subsets of these proposed methods. The comparisons have not\, however\, considered the so-called MAMS approach\, since metho dology for the latter did not incorporate strong control of the family-wis e error rate (FWER). Recently\, the MAMS approach has been extended such t hat strong control of the FWER can be guaranteed. Furthermore\, an automat ed process has been developed which can produce efficient designs for tria ls with any number of stages and treatment arms.Since MAMS is relatively e asy to understand and implement\, we set out to explore these developments and to compare MAMS to other well established methods.We show how MAMS co mpares favourably with the more established combination method for some sc enarios and explore how use of a novel selection rule may offer a further option within MAMS methodology. |
  \;
Seamless Study Designs &\; Real-time Data Capture Using Elect ronic Devices
Rhian Jacob\, Roche | Developing a new drug in a highly competitive environment necessitates a fast to market approach . \; This talk looks at the ongoing '944' trial\, a multiple cohort se amless phase II/III study. Key study endpoints are captured using electron ic devices\, providing high volume of data in real-time. This talk describ es the opportunities and challenges for a statistician leading a phase II readout within a phase III framework\, with discussion on novel data captu re methods\; is the industry ready to handle device data? |
 \;
Training session: Exp laining statistics to non-statisticians  \;
James Matcham\, AstraZeneca / PSI Training Committee |
 \; |
 \;
Introduction to Pharmacoki netics
Laura Cope\, Quanticate < /td> | Pharmacokinetics is the s tudy of the effect of the body on the drug. The pharmacokinetic profile ma ps the concentration of the drug in the body over time tracking how the dr ug is absorbed\, distributed\, metabolised and excreted by individuals. Dr ug concentration is linked to both efficacy and safety and hence pharmacok inetic studies form the basis of much earlier phase clinical trials. Pharm acokinetic profiles are often described using summary measures such as the area under the curve (AUC) and the maximum concentration (Cmax) but how a re these parameters derived? What models are used and what assumptions do they make? This presentation will describe the derivation of pharmacokinet ic parameters for both intravenous and extravascular dosing\, first lookin g at a single dose and then extending to multiple dosing schedules. < /td> |
 \;
AdePro &nd ash\; A new Perspective on Safety Profiles
Nicole Mentenich\, Christoph Tasto and Bastian Becker\, Bayer AG | The database in a clin ical trial contains vast information on adverse events\, involving hundred s of different adverse event terms with varying severity grades and differ ent start and end dates. Despite this plethora of information\, insight in to the adverse events in a clinical study is usually limited to simple sum mary tables of absolute and relative numbers of adverse event occurrences. AdEPro &ndash\; an innovation of Bayer&rsquo\;s Biostatistics Innovation Center &ndash\; is an unparalleled approach to audio-visualize the safety profile of both the individual patient and of the entire study cohort\, wh ich enables every study team member to experience the study and emphasize with the patients. The AdEPro app depicts the temporal progress of all adv erse events in every study subject and enables the user to give profound a nswers to complex questions surrounding adverse events such as the frequen cy\, duration and correlation of adverse events of interest. |
 \;
Marginal and condi tional structural mean models for optimising Dynamic Treatment Regimes
Nirav Ratia\, GSK | Marginal structural models (MSMs) are cas ual models of dynamic treatment regimes (also known as treatment strategie s or policies) designed to adjust for time-dependent confounding. These mo dels are designed to adjust for exposures or treatment that vary over time \, and standard approaches for adjustment of confounding can be biased whe n there exist time-dependent confounding. Dynamic treatment regimes (DTRs) provide the basis for statistical analysis in personalised medicine. A DT R is a decision rule that guides the treatment choices over the course of the therapy. The sequence of treatments a patient receives depends on the patient&rsquo\;s health status\, response to prior treatment and other pat ient characteristics. |
 \;
END:VEVENT END:VCALENDAR