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BEGIN:STANDARD
DTSTART;VALUE=DATE:20230101
TZNAME:UTC
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
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BEGIN:VEVENT
DESCRIPTION:Date: Monday 24th - Thursday 27th January 2022\nTime: Lectures
09:00-12:00 on the 24th &\; 27th. Separate forum setup for practical ex
ercises between the 24th-26th.\nSpeakers: Sebastian Weber and Gaë\;lle
Saint-Hilary\n\nWho is this event intended for? \;All statisticians w
orking on clinical trials\, with a basic knowledge of R. Some notions of B
ayesian statistics could be helpful.\nWhat is the benefit of attending?&nb
sp\;Attendees will have the chance to cover\; Bayesian Dynamic Borrowing d
esigns based on the meta-analytic predictive (MAP) model\; Design planning
\, operating characteristics\, statistical analysis\; and Applications usi
ng the R package RBesT.\nCourse Cost\nRegular \;Members \;= £
\;340+VAT\nRegular \;Non-Members \;= £\;465*+VAT\n*Please not
e: \;Non-Member rates include PSI membership until 31 Dec. 2022.\nRegi
stration\nTo book your space\, please \;click here.\nOverview\nThere i
s an intrinsic interest of leveraging all available information for an eff
icient design and analysis of clinical trials. Including external data in
the analysis of clinical trials may increase study power or allow for the
reduction of (usually) the control group sample size. The use of external
data in trials are nowadays used in earlier phases of drug development (Tr
ippa\, Rosnerand Muller\, 2012\; French\, Thomas and Wang\, 2012\; Hueber
et al.\, 2012\; Smith et al.\, 2019)\, occasionally in phase III trials (F
rench et al.\, 2012\; Viele et al.\, 2018)\, and also in special areas suc
h as medical devices (FDA\, 2010a)\, orphan indications (Dupont and Van Wi
lder\, 2011) and extrapolation in pediatric studies (Berry\, 1989\; Best e
t al.\, 2019). This allows adequately powered trials at smaller sample siz
es leading to faster trial conduct and exposure of fewer patients to a pot
entially in-effective control treatment.\n\nIn this short course\, we will
provide a statistical framework to incorporate external information into
a trial. During the first part of the course\, we will introduce Bayesian
Dynamic Borrowing designs based on the meta-analytic predictive (MAP) mode
l (Neuenschwander et al.\, 2010). The MAP model is a Bayesian hierarchical
model\, which combines the evidence from different potentially heterogene
ous sources. Dynamic borrowing permits to limit the use of historical data
when it is incompatible with the data observed within the trial.\n\nIn th
e second part of the course\, we will propose a practice session with appl
ications using the R package RBesT\, the R Bayesian evidence synthesis too
ls\, which are freely available from CRAN. These exercises will enable par
ticipants to apply the presented approach themselves. During third and las
t part of the course\, more advanced topics will be detailed such as effec
tive and maximum sample sizes\, advanced operating characteristics\, and p
robability of success.\nAgenda\nLecture session 1 \n(morning of Monday 24t
h January (09:00-12:00))\n&bull\; Meta-analytic predictive (MAP) model\n&b
ull\; Robustification for dynamic borrowing\n&bull\; Design planning\, ope
rating characteristics\n&bull\; Final analysis\nPractice sessions \;\n
(afternoon of Monday 24th\, all day Tuesday 25th\, all day Wednesday 26th)
\n&bull\; Supervised homework\, set-up in a separate forum\nLecture sessi
on 2\n(morning of Thursday 27th January (09:00-12:00))\n&bull\; Effective
sample size\, maximum sample size\n&bull\; Advanced operating characterist
ics\n&bull\; Equivalence between MAP and MAC (Meta-analysis combined)\n&bu
ll\; Probability of success\, decision rule\nSpeaker details\n\n\n\n \n
\n \n Speaker\n \n \n
Biography\n \n \n \n \n
\n Gaë\;lle Saint-Hilary\n \n
\n Gaë\;lle Saint-Hilary is Statistical Methodologist\, CE
O and founder of the consulting company Saryga (France). Prior to this rol
e\, Gaë\;lle was Statistical Methodologist at Servier until December 2
021. With more than 15 years of experience in the pharmaceutical industry
(Servier\, Novartis) and a strong and long-lasting collaboration with acad
emia\, Gaë\;lle Saint-Hilary is an expert in Bayesian statistics and d
ecision-making support. She has worked at developing novel approaches to i
mprove drug development&rsquo\;s performances\, and her main scientific in
terests are quantitative decision-making\, benefit-risk assessment\, innov
ative study designs and historical data.\n \n \n
\n \n \n Sebastian Weber\n \n
\n Sebastian Weber is working as Director in the Dep
artment of Advanced Methodology and Data Science at Novartis. He holds a P
hD in Physics from the TU Darmstadt and joined Novartis 8+ years ago. He h
as worked extensively on enabling the use of historical (control) informat
ion in clinical trials through consulting and working on tools to facilita
te the application of historical control information from trial design to
analysis. Furthermore\, Sebastian has experience in designing Oncology pha
se I dose-escalation trails and is also involved in pediatric drug develop
ment programs\, where he applies extrapolation concepts. His research inte
rests include the application of pharmacometrics in statistics\, model-bas
ed drug development and application of Bayesian methods for drug developme
nt.\n \n \n \n\n \;\n\n\n\n\nDisclaimer\nPSI is a
non-profit organisation run by volunteers. Many of the event organisers a
nd presenters donate their time\, while the majority of the event registra
tion cost is spent on administrative support\, venue rental / online confe
rencing\, travel costs for the presenter\, software licences\, and general
running of the society. PSI strives to offer high quality courses\, but c
annot offer a guarantee that the content presented is accurate or fit for
your particular needs. Please check if any software is required for this c
ourse and ensure you are able to run it prior to registering.\n\nCancellat
ion and Moderation Terms\nFor cancellations received up to two weeks prior
to a PSI event start-date\, the event registration fee will be refunded l
ess 25% administrative charge. After this date\, no refunds will be possib
le. A handling fee of 20 GBP per registration will be charged for every re
gistration modification received two weeks prior or less\, including a del
egate name change.\n\n
DTEND:20220124T120000Z
DTSTAMP:20240329T144948Z
DTSTART:20220124T090000Z
LOCATION:
SEQUENCE:0
SUMMARY:PSI Training Course: Use of Historical Data in Clinical Trials: An
Evidence Synthesis Approach
UID:RFCALITEM638473205880865227
X-ALT-DESC;FMTTYPE=text/html:Date: Monday 24th - Thursday
27th January 2022
\nTime: Lectures 09:00-12:00 on th
e 24th &\; 27th. Separate forum setup for practical exercises between t
he 24th-26th.
\nSpeakers: Sebastian Weber and Ga&eum
l\;lle Saint-Hilary
\n
\n
Who is this event intended f
or? \;All statisticians working on clinical trials\, with a b
asic knowledge of R. Some notions of Bayesian statistics could be helpful.
\nWhat is the benefit of attending? \;Attendees
will have the chance to cover\; Bayesian Dynamic Borrowing designs based
on the meta-analytic predictive (MAP) model\; Design planning\, operating
characteristics\, statistical analysis\; and Applications using the R pack
age RBesT.
Regular \;Members \;= £\;340+VAT
\nRegular \;Non-Members \;= £\;465*+VAT
\n*Please note: \;Non-Member r
ates include PSI membership until 31 Dec. 2022.
To book your space\, please \;click here.
\nThere is an intrinsic interest of leveraging all availab
le information for an efficient design and analysis of clinical trials. In
cluding external data in the analysis of clinical trials may increase stud
y power or allow for the reduction of (usually) the control group sample s
ize. The use of external data in trials are nowadays used in earlier phase
s of drug development (Trippa\, Rosnerand Muller\, 2012\; French\, Thomas
and Wang\, 2012\; Hueber et al.\, 2012\; Smith et al.\, 2019)\, occasional
ly in phase III trials (French et al.\, 2012\; Viele et al.\, 2018)\, and
also in special areas such as medical devices (FDA\, 2010a)\, orphan indic
ations (Dupont and Van Wilder\, 2011) and extrapolation in pediatric studi
es (Berry\, 1989\; Best et al.\, 2019). This allows adequately powered tri
als at smaller sample sizes leading to faster trial conduct and exposure o
f fewer patients to a potentially in-effective control treatment.
\n<
br />\nIn this short course\, we will provide a statistical framework to i
ncorporate external information into a trial. During the first part of the
course\, we will introduce Bayesian Dynamic Borrowing designs based on th
e meta-analytic predictive (MAP) model (Neuenschwander et al.\, 2010). The
MAP model is a Bayesian hierarchical model\, which combines the evidence
from different potentially heterogeneous sources. Dynamic borrowing permit
s to limit the use of historical data when it is incompatible with the dat
a observed within the trial.
\n
\nIn the second part of the cour
se\, we will propose a practice session with applications using the R pack
age RBesT\, the R Bayesian evidence synthesis tools\, which are freely ava
ilable from CRAN. These exercises will enable participants to apply the pr
esented approach themselves. During third and last part of the course\, mo
re advanced topics will be detailed such as effective and maximum sample s
izes\, advanced operating characteristics\, and probability of success.
Lecture session 1
\n(morning of Monday 24th January (09:00-12:00))
\n&bull\; Me
ta-analytic predictive (MAP) model
\n&bull\; Robustification for dyna
mic borrowing
\n&bull\; Design planning\, operating characteristics\n&bull\; Final analysis
\nPractice sessions \;
\n(afternoon of Monday 24th\, all day Tuesday 25th\, all day
Wednesday 26th)
\n&bull\; Supervised homework\, set-up in a sep
arate forum
\nLecture session 2
\n(morning
of Thursday 27th January (09:00-12:00))
\n&bull\; Effective samp
le size\, maximum sample size
\n&bull\; Advanced operating characteri
stics
\n&bull\; Equivalence between MAP and MAC (Meta-analysis combin
ed)
\n&bull\; Probability of success\, decision rule
\n
Speaker \n | \n \n Biography< /p>\n | \n
\n
| \n \n Gaë\;lle Saint-Hilary is Statistical Methodolo gist\, CEO and founder of the consulting company Saryga (France). Prior to this role\, Gaë\;lle was Statistical Methodologist at Servier until D ecember 2021. With more than 15 years of experience in the pharmaceutical industry (Servier\, Novartis) and a strong and long-lasting collaboration with academia\, Gaë\;lle Saint-Hilary is an expert in Bayesian statist ics and decision-making support. She has worked at developing novel approa ches to improve drug development&rsquo\;s performances\, and her main scie ntific interests are quantitative decision-making\, benefit-risk assessmen t\, innovative study designs and historical data. \n | \n
\n
| \n \n Sebastian Weber is working as Dir ector in the Department of Advanced Methodology and Data Science at Novart is. He holds a PhD in Physics from the TU Darmstadt and joined Novartis 8+ years ago. He has worked extensively on enabling the use of historical (c ontrol) information in clinical trials through consulting and working on t ools to facilitate the application of historical control information from trial design to analysis. Furthermore\, Sebastian has experience in design ing Oncology phase I dose-escalation trails and is also involved in pediat ric drug development programs\, where he applies extrapolation concepts. H is research interests include the application of pharmacometrics in statis tics\, model-based drug development and application of Bayesian methods fo r drug development. \n | \n