BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 13.3//EN BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD DTSTART;VALUE=DATE:20230101 TZNAME:UTC TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: Wednesday 21st September - Thursday 22nd September 2022\n Time: \;\n21st = 12:30-17:00 | \;22nd \;= 09:00-17:00 BST\n21s t \;= 13:30-18:00 | \;22nd \;= 10:00-18:00 CEST\nLocation: Onl ine\n\nWho is this event intended for? \;Pre-Clinical Statisticians in the Pharmaceutical Industry\, with interest and/or some basic knowledge o f R/STAN/BUGS and Bayesian statistics.\nWhat is the benefit of attending?& nbsp\;This workshop offers the chance to meet with colleagues across indus try and learn more about Bayesian Methodology and its applications in pre- clinical.\nOverview\n\nThis is the pre-clinical SIG&rsquo\;s 10th workshop and will be the first one run virtually. Our theme for this workshop is B ayesian\; the workshop will run over a day and a half and will include a t raining course on Bayesian methods (see more below)\, two presentations on applications of Bayesian methodology in a pre-clinical setting and a brea kout session.\n\n\nCourse\nBayesian Statistics for Preclinical Research: N ew Opportunities\n\nThe course will start by introducing the key concepts of Bayesian statistics\, emphasizing the context and key objectives of pre clinical research in pharmaceutical and medical device development. \; We then move on to show how Bayesian thinking and practices are a fit-for -purpose paradigm. \; \; Over the last decade\, preclinical resear ch has been identified in the literature as an area of research suffering from a lack of reproducibility. Causes for this are many\, but in this cou rse\, we&rsquo\;ll show how to frame a Bayesian strategy to address reprod ucibility concerns by proposing new study designs\, modelling\, and decisi on-making. Preclinical research is a learning process\, making Bayesian st atistical learning a very natural partnership. \;\n\nKey topics covere d by the course include:\n\n Define the question and the research objec tive\n Strategies for determining\, using\, and checking robustness of prior distributions\n Replacing experiment-based decisions in favor of project-based decision\n Use of informed control groups and unbalanced designs\n Design of the overall project\, integrating the potential sou rces of irreproducibility in advance\n Progress under uncertainty\, ado ption of adaptive designs\n Designing experiments using Bayesian assura nce\, rather than power\n Understand risks and predictive probability o f success to meet the objective\n Bayesian incorporation of real world evidence (RWE)\n Examples of Bayesian programming using R/STAN/BUGS and SAS\n\nBruno Boulanger\, Senior Director\, PharmaLex\nBradley Carlin\, Se nior Advisor\, PharmaLex\n\n\nTalks\nBayesian Tumor volume analysis with B RMS R package\nMarie Miossec\nIn cancer drug development\, demonstrated ef ficacy in tumor xenograft experiments on severe combined immunodeficient m ice who are grafted with human tumor tissues or cells is an important step to bring a promising compound to human. A key outcome variable is tumor v olumes measured over a period of time\, while mice are treated with certai n treatment regimens. The tumor growth inhibition delta T/delta C ratio is commonly used to quantify treatment effects in such drug screening tumor xenograft experiments In this presentation\, we propose a Bayesian approac h to make a statistical inference of the T/C ratio\, including both hypoth esis testing and a credibility interval estimate. Through a practical case \, implementation\, diagnosis\, model selection and results with the BRMS R package will be discussed.\n\n\nA Bayesian\, Generalized Frailty Model f or Comet Assays\nHelena Geys\nThis paper proposes a flexible modelling app roach for so-called comet assay data regularly encountered in pre-clinical research. While such data consist of non-Gaussian outcomes in a multi-lev el hierarchical structure\, traditional analyses typically completely or p artly ignore this hierarchical nature by summarizing measurements within a cluster. Non-Gaussian outcomes are often modelled using exponential famil y models. This is true not only for binary and count data\, but also for\, e.g.\, time-to-event outcomes. Two important reasons for extending this f amily are: (1) the possible occurrence of over dispersion\, meaning that t he variability in the data may not be adequately described by the models w hich often exhibit a prescribed mean-variance link\, and (2) the accommoda tion of a hierarchical structure in the data\, owing to clustering in the data. The first issue is dealt with through so-called over dispersion mode ls. Clustering is often accommodated through the inclusion of random subje ct-specific effects. Though not always\, one conventionally assumes such r andom effects to be normally distributed. In the case of time-to-event dat a\, one encounters\, for example\, the gamma frailty model (Duchateau and Janssen 2007). While both of these issues may occur simultaneously\, model s combining both are uncommon. Molen berghs et al (2010) proposed a broad class of generalized linear models accommodating over dispersion and clust ering through two separate sets of random effects. In Ghebretinsae et al\, we used this method to model data from a comet assay with a three-level h ierarchical structure. Whereas a conjugate gamma random effect is used for the over dispersion random effect\, both gamma and Normal random effects are considered for the hierarchical random effect. Apart from model formul ation\, we place emphasis on Bayesian estimation.\nWorkshop Cost\nThis Wor kshop is open to both Members and Non-Members of PSI. Please see below for confirmation of fees.\nPSI Members \;= £\;125+VAT\nPSI Non-Membe rs = £\;125+VAT\nRegistration\nPlease note: this event will take plac e online via Zoom\, and has a limited number of places available. \;\n To register for this workshop\, please click here.\nSpeaker details\n\n\n\ n \n \n \n Speaker\n \n \n Biography\n \n \n \n \n Bruno Boulanger\n \n \n \n \n \n Bruno Boulanger has 25 years of ex perience in several areas of pharmaceutical research and industry includin g discovery\, toxicology\, CMC and early clinical phases. He holds various positions in Europe and in USA. Bruno joined UCB Pharma in 2007 as Direct or of Exploratory Statistics. Bruno is also since 2000 Lecturer at the Uni versité\; of Liè\;ge\, in the School of Pharmacy\, teaching De sign of Experiments and Statistics. He is also a USP Expert\, member of th e Committee of Experts in Statistics since 2010. Bruno has authored or co- authored more than 100 publications in applied statistics and co-edited on e book in Bayesian statistics for pharmaceutical research.\n \n \n \n \n \n Brad Carlin\n Brad Carlin is a statistical researcher\, methodologist\, consu ltant\, and instructor. \; He currently serves as Senior Advisor for D ata Science and Statistics at PharmaLex\, an international pharmaceutical consulting firm. \; Prior to this\, he spent 27 years on the faculty o f the Division of Biostatistics at the University of Minnesota School of P ublic Health\, serving as division head for 7 of those years. \; He ha s also held visiting positions at Carnegie Mellon University\, Medical Res earch Council Biostatistics Unit\, Cambridge University (UK)\, Medtronic C orporation\, HealthPartners Research Foundation\, the M.D Anderson Cancer Center\, and AbbVie Pharmaceuticals. \; \; He has published more t han 185 papers in refereed books and journals\, and has co-authored three popular textbooks: &ldquo\;Bayesian Methods for Data Analysis&rdquo\; with Tom Louis\, &ldquo\;Hierarchical Modeling and Analysis for Spatial Data&r dquo\; with Sudipto Banerjee and Alan Gelfand\, and "Bayesian Adaptive Met hods for Clinical Trials" with Scott Berry\, J. Jack Lee\, and Peter Mulle r. \; From 2006-2009 he served as editor-in-chief of \;Bayesian An alysis\, the official journal of the International Society for Bayesian An alysis (ISBA). \; During his academic career\, he served as primary di ssertation adviser for 20 PhD students. \; Dr. Carlin has extensive ex perience teaching short courses and tutorials\, and won both teaching and mentoring awards from the University of Minnesota. During his spare time\, Brad is a health musician and bandleader\, providing keyboards\, guitar\, and vocals in a variety of venues.\n \n \n \n \n Marie Miossec\n \n \n Marie is a biostatistician engineer at IT&\;M STATS. She was gradua ted from ENSAI (National School of Statistics and Information Analysis\, F rance) in 2019 with a master's degree specializing in statistics for life sciences. She has been working for SANOFI as a contractor for three years in the team in charge of biostatistical support to non-clinical efficacy & amp\; safety studies.\n \n \n \n \n \n Helena Geys\n \n \n Helena Geys is Global head of the Discovery and Nonclinical Safety Stati stics group at Johnson and Johnson. Helena joined J&\;J 18 years ago du ring which period she has made significant contributions in various areas of nonclinical statistics: discovery\, toxicology\, manufacturing. She is an active participant in many professional organizations\, and has shown h erself a contributor to many successful external and cross-pharma initiati ves and academic collaborations leading to impactful successes in drug dev elopment strategies. The results of her research have been published in &g t\;100 methodological and applied publications on clustered non-normal dat a\, risk assessment\, spatial epidemiology\, translational medicine and su rrogate marker validation. In addition to her assignment at Janssen\, Hele na has a strong passion for teaching and mentoring. She combines her work at Janssen Pharmaceutica with a position as tenure-track professor in bios tatistics at the Data Science Institute of Hasselt University (Belgium) an d has mentored and coached >\;30 master and PhD students.\n \ n \n \n DTEND:20220922T160000Z DTSTAMP:20240328T204840Z DTSTART:20220921T113000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Pre-Clinical SIG Workshop 2022 UID:RFCALITEM638472557209340774 X-ALT-DESC;FMTTYPE=text/html:
Date<
/strong>: Wednesday 21st September - Thursday 22nd September 2022
\n<
strong>Time: \;
\n21st = 12:30-17:00 |&
nbsp\;22nd \;= 09:00-17:00 BST
\n21st \;= 13:30-18:
00 | \;22nd \;= 10:00-18:00 CEST
\nLocation: Online
\n
\nWho is this event intended for?&
nbsp\;Pre-Clinical Statisticians in the Pharmaceutical Industry\, with int
erest and/or some basic knowledge of R/STAN/BUGS and Bayesian statistics.<
br />\nWhat is the benefit of attending? \;This works
hop offers the chance to meet with colleagues across industry and learn mo
re about Bayesian Methodology and its applications in pre-clinical.
\nThis is the pre-clinical SIG&rsquo\;s 10th workshop and will be t
he first one run virtually. Our theme for this workshop is Bayesia
n\; the workshop will run over a day and a half and will include
a training course on Bayesian methods (see more below)\, two presentations
on applications of Bayesian methodology in a pre-clinical setting and a b
reakout session.
\n
\n
Bayesian Statistics fo
r Preclinical Research: New Opportunities
\n
\nThe cour
se will start by introducing the key concepts of Bayesian statistics\, emp
hasizing the context and key objectives of preclinical research in pharmac
eutical and medical device development. \; We then move on to show how
Bayesian thinking and practices are a fit-for-purpose paradigm. \;&nb
sp\; Over the last decade\, preclinical research has been identified in th
e literature as an area of research suffering from a lack of reproducibili
ty. Causes for this are many\, but in this course\, we&rsquo\;ll show how
to frame a Bayesian strategy to address reproducibility concerns by propos
ing new study designs\, modelling\, and decision-making. Preclinical resea
rch is a learning process\, making Bayesian statistical learning a very na
tural partnership. \;
\nKey topics covered by the course include:
\nBruno Boulanger\, Senior Director\, PharmaLex
\nBradley Carlin\, Senior Advisor\, Pharma
Lex
\n
\n
Bayesian Tumor volume analysis with BRMS R package
\nMarie Miossec
\nIn cancer drug development\, demonstra
ted efficacy in tumor xenograft experiments on severe combined immunodefic
ient mice who are grafted with human tumor tissues or cells is an importan
t step to bring a promising compound to human. A key outcome variable is t
umor volumes measured over a period of time\, while mice are treated with
certain treatment regimens. The tumor growth inhibition delta T/delta C ra
tio is commonly used to quantify treatment effects in such drug screening
tumor xenograft experiments In this presentation\, we propose a Bayesian a
pproach to make a statistical inference of the T/C ratio\, including both
hypothesis testing and a credibility interval estimate. Through a practica
l case\, implementation\, diagnosis\, model selection and results with the
BRMS R package will be discussed.
\n
\n
A Bayesian\, Generalized Frailty Model for Comet As
says
\nHelena Geys
\nThis paper proposes a fle
xible modelling approach for so-called comet assay data regularly encounte
red in pre-clinical research. While such data consist of non-Gaussian outc
omes in a multi-level hierarchical structure\, traditional analyses typica
lly completely or partly ignore this hierarchical nature by summarizing me
asurements within a cluster. Non-Gaussian outcomes are often modelled usin
g exponential family models. This is true not only for binary and count da
ta\, but also for\, e.g.\, time-to-event outcomes. Two important reasons f
or extending this family are: (1) the possible occurrence of over dispersi
on\, meaning that the variability in the data may not be adequately descri
bed by the models which often exhibit a prescribed mean-variance link\, an
d (2) the accommodation of a hierarchical structure in the data\, owing to
clustering in the data. The first issue is dealt with through so-called o
ver dispersion models. Clustering is often accommodated through the inclus
ion of random subject-specific effects. Though not always\, one convention
ally assumes such random effects to be normally distributed. In the case o
f time-to-event data\, one encounters\, for example\, the gamma frailty mo
del (Duchateau and Janssen 2007). While both of these issues may occur sim
ultaneously\, models combining both are uncommon. Molen berghs et al (2010
) proposed a broad class of generalized linear models accommodating over d
ispersion and clustering through two separate sets of random effects. In G
hebretinsae et al\, we used this method to model data from a comet assay w
ith a three-level hierarchical structure. Whereas a conjugate gamma random
effect is used for the over dispersion random effect\, both gamma and Nor
mal random effects are considered for the hierarchical random effect. Apar
t from model formulation\, we place emphasis on Bayesian estimation.
This Workshop is
open to both Members and Non-Members of PSI. Please see below for confirma
tion of fees.
\nPSI Members \;= £\;125+VAT<
br />\nPSI Non-Members = £\;125+VAT
Please note: this event will
take place online via Zoom\, and has a limited number of places available.
 \;
\nTo register for this workshop\, please click here.
\n Spea ker \n | \n \n Biography \n | \n
\n Bruno Boulanger \n \n \n \n \n | \n Bruno Boulanger has 25 years of experience in several areas of pharmaceu
tical research and industry including discovery\, toxicology\, CMC and ear
ly clinical phases. He holds various positions in Europe and in USA. Bruno
joined UCB Pharma in 2007 as Director of Exploratory Statistics. Bruno is
also since 2000 Lecturer at the Université\; of Liè\;ge\, in
the School of Pharmacy\, teaching Design of Experiments and Statistics. He
is also a USP Expert\, member of the Committee of Experts in Statistics s
ince 2010. Bruno has authored or co-authored more than 100 publications in
applied statistics and co-edited one book in Bayesian statistics for phar
maceutical research. \n \n | \n
<
img src="https://psiweb.org/images/default-source/default-album/bradleyedi
t.png?sfvrsn=ce85a0db_0&\;MaxWidth=150&\;MaxHeight=&\;ScaleUp=fal
se&\;Quality=High&\;Method=ResizeFitToAreaArguments&\;Signature=1
A508230FC2424F40F3F8C7E5D26B3E7" data-method="ResizeFitToAreaArguments" da
ta-customsizemethodproperties="{'MaxWidth':'150'\,'MaxHeight':''\,'ScaleUp
':false\,'Quality':'High'}" data-displaymode="Custom" alt="Bradleyedit" ti
tle="Bradleyedit" /> \n Brad Carlin | \n Brad Carlin is a statistical researche r\, methodologist\, consultant\, and instructor. \; He currently serve s as Senior Advisor for Data Science and Statistics at PharmaLex\, an inte rnational pharmaceutical consulting firm. \; Prior to this\, he spent 27 years on the faculty of the Division of Biostatistics at the University of Minnesota School of Public Health\, serving as division head for 7 of those years. \; He has also held visiting positions at Carnegie Mellon University\, Medical Research Council Biostatistics Unit\, Cambridge Univ ersity (UK)\, Medtronic Corporation\, HealthPartners Research Foundation\, the M.D Anderson Cancer Center\, and AbbVie Pharmaceuticals. \; \ ; He has published more than 185 papers in refereed books and journals\, a nd has co-authored three popular textbooks: &ldquo\;Bayesian Methods for D ata Analysis&rdquo\; with Tom Louis\, &ldquo\;Hierarchical Modeling and An alysis for Spatial Data&rdquo\; with Sudipto Banerjee and Alan Gelfand\, a nd "Bayesian Adaptive Methods for Clinical Trials" with Scott Berry\, J. J ack Lee\, and Peter Muller. \; From 2006-2009 he served as editor-in-c hief of \;Bayesian Analysis\, the official journal of the Int ernational Society for Bayesian Analysis (ISBA). \; During his academi c career\, he served as primary dissertation adviser for 20 PhD students.& nbsp\; Dr. Carlin has extensive experience teaching short courses and tuto rials\, and won both teaching and mentoring awards from the University of Minnesota. During his spare time\, Brad is a health musician and bandleade r\, providing keyboards\, guitar\, and vocals in a variety of venues. | \n
\n
| \n \n Marie is a biostatistician engineer at IT&\;M STATS. She was graduated from ENSAI (National School of Statistics and Informati on Analysis\, France) in 2019 with a master's degree specializing in stati stics for life sciences. She has been working for SANOFI as a contractor f or three years in the team in charge of biostatistical support to non-clin ical efficacy &\; safety studies. \n | \n
\n
| \n \n
Helena Geys is Global head of the Discovery and Nonclinical Safety St atistics group at Johnson and Johnson. Helena joined J&\;J 18 years ago during which period she has made significant contributions in various are as of nonclinical statistics: discovery\, toxicology\, manufacturing. She is an active participant in many professional organizations\, and has show n herself a contributor to many successful external and cross-pharma initi atives and academic collaborations leading to impactful successes in drug development strategies. The results of her research have been published in >\;100 methodological and applied publications on clustered non-normal data\, risk assessment\, spatial epidemiology\, translational medicine and surrogate marker validation. In addition to her assignment at Janssen\, H elena has a strong passion for teaching and mentoring. She combines her wo rk at Janssen Pharmaceutica with a position as tenure-track professor in b iostatistics at the Data Science Institute of Hasselt University (Belgium) and has mentored and coached >\;30 master and PhD students. \n | \n