BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 13.3//EN BEGIN:VTIMEZONE TZID:GMT Standard Time BEGIN:STANDARD DTSTART:20231002T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYHOUR=2;BYMINUTE=0;BYMONTH=10 TZNAME:GMT Standard Time TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20230301T010000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYHOUR=1;BYMINUTE=0;BYMONTH=3 TZNAME:GMT Daylight Time TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: Monday 18th November 2019\nTime: 14:00 - 16:00 UK Time\nS peakers: \; \;\n\n José\; Pinheiro\n Bjö\;rn Bornk amp\n Tobias Mielke \;\n France Mentré\;\n Rob Hemmings \n\n\nPost-event access: To access the recording of this webinar\, please visit the Video On Demand library.\n\nRegistration: \;This webinar is free for PSI members\, but has a charge of £\;20+VAT for non-members. To register\, please \;click here. \; \;\nPlease email \; PSI@mci-group.com \;if you have any questions.\n___________________\n\ nLongitudinal data\, i.e. data that arises from repeated observations of t he variable over a period of time\, has long been put forward as one way t o improve to the efficiency of drug development. However\, even though the re is a rich statistical methodology for longitudinal data\, there is no f ull\, wholehearted uptake of these methods in pharmaceutical statistics. T he purpose of this webinar is to explore the use of longitudinal modelling across drug development\, highlighting its opportunities (such as usage a s primary analyses\, or for improved decision making at interim analyses) and caveats. One important aspect to be discussed is the evaluation of the efficiency of (parametric) longitudinal modelling versus standard cross-s ectional approaches\, and factors based upon which one approach might be p referable over the other.\n\n\n\nSpeakers\n\n \n \n \ n \n José\; Pinheiro\n (Janssen Res earch &\; Development)\n \n \n \n Abstract: \;Increasing the efficiency of drug development is im perative to make novel treatments available to patients sooner and at lowe r costs\, ensuring the long-term sustainability of the biopharmaceutical i ndustry. Among the various proposals that have been put forward to improve drug development efficiency\, leveraging longitudinal data via (parametri c) modeling stands out for its low additional cost and ease of implementat ion: oftentimes only involving different analysis methods for data already collected in clinical studies. While numerous statistical approaches have been developed over the past several decades for modelling longitudinal d ata (e.g. nonlinear and generalized linear mixed effects) the uptake of th ese methods in pharmaceutical statistics as part of mainstream (pre-specif ied) primary analyses in clinical trials remains quite limited\, with main exception being MMRM.\n Partially this may be due to concerns about making assumptions on the shape of longitudinal profiles\, leading t o the focus on cross-sectional (or landmark) analyses\, which do not requi re such assumptions\, but often only utilize a fraction of the information available per patient. By utilizing the full information collected over t ime\, longitudinal modelling\, especially of the parametric type\, has the potential to lead to substantially more efficient drug development\, even when the primary endpoint is cross-sectional in nature (e.g.\, change fro m baseline at Week 26). This potential advantage needs to be balanced agai nst standard concerns about model mis-specification and regulatory accepta nce.\n Bio: \;José\; Pinheiro has a Ph.D. in Statisti cs from the University of Wisconsin &ndash\; Madison\, having worked at Be ll Labs and Novartis Pharmaceuticals\, before his current position as Glob al Head of Statistical Modeling &\; Methodology in the Statistics and D ecision Sciences department at Janssen Research &\; Development. He has been involved in methodological development in various areas of statistic s and drug development\, including dose-finding\, adaptive designs\, and m ixed-effects models. He is a Fellow of the American Statistical Associatio n\, a past-editor of Statistics in Biopharmaceutical Research\, and past-p resident of ENAR.\n \n \n \n \n \n Bjö\;rn Bornkamp\n (Novartis)\n \n \n Title: When is a longitudinal test better t han a cross-sectional one for detecting a treatment effect? \;\n (Joint work with Ines Paule)\n Abstract: In this work\, w e explore the potential benefits of utilizing the full longitudinal profil e for testing for a treatment effect and compare this to a cross-sectional test at the last time point. \n While there are a number of pa pers showing huge gains for longitudinal testing\, current practice in cli nical development is to focus on a comparison at the last visit for the pu rpose of trial design and the primary statistical analysis. In this presen tation we try to characterize the factors and endpoint properties that det ermine if and by how much a longitudinal test will be more powerful than a cross-sectional test.\n We consider the setting of a continuou s endpoint measured repeatedly over time and utilize a test that uses a we ighted average of the treatment differences at the specific time points\, which is straightforward to implement with standard mixed effects software . We consider how to weight time-points optimally and which factors play a role in determining the weights. We then assess the potential gain of the longitudinal approach in a set of real case examples.\n Finall y\, a simulation study is performed that compares this simple longitudinal approach to more strongly parameterized traditional longitudinal mixed ef fects model. \n Bio: \;Bjö\;rn works in the Novartis S tatistcal Methodology and Consulting group. He consults for example on dos e-finding studies\, causal inference and estimands\, subgroup analysis\, B ayesian statistics and statistical modelling. In 2013 he received the RSS/ PSI award for developing innovative statistical dose-finding methodology\, in particular the development of the software package DoseFinding in R.\n \n \n \n \n \n Tobias Mielke\n (Janssen Research &\; Development)\n \n \n Title: Decision-making using longitudinal modelling in presence of model uncertainty\n Abstract: \;A s statisticians\, we design studies to answer some research questions and we would like to get answers to these research questions as quickly as pos sible\, using the least amount of resources while still providing enough i nformation to allow for some &ldquo\;significant&rdquo\; conclusions with high probability. It is common that our study designs require multiple ass essments of the same variable within the same subjects over some period of time. However\, while these correlated longitudinal measurements are coll ected\, our analysis approaches might not take full use of the information contained in the data\, leading to inefficiency in some of our analyses a nd potentially to wrong decisions. Many statistical techniques are availab le to leverage the information contained in longitudinal data\, like summa ry measures (e.g. AUCs) or the MMRM approach. Knowing the true underlying longitudinal profile and distribution\, parametric longitudinal modelling provides an efficient analysis technique. Unfortunately\, the true underly ing longitudinal profile is known only in rare situations (e.g. mechanisti c models in PK/PD). As a result\, high uncertainty in longitudinal profile s comes with high concerns in applying parametric longitudinal modelling f or decision-making: Using the wrong model for the analysis\, the results w ill be biased such that error probabilities will be inflated. While model uncertainty is a valid concern\, mitigation strategies should be evaluated in the design phase to support the selection of an efficient and robust a nalysis technique.\n Concerns on model uncertainty have been wi dely discussed for dose-finding studies in the recent scientific literatur e. The methodology can be generalized to longitudinal modelling\, covering model selection approaches\, model-based contrast tests and/or (Bayesian) model averaging approaches. The effects of model uncertainty on decision- making will be discussed in this presentation. Different parametric and se mi-parametric longitudinal modelling approaches will be evaluated for this purpose. The presented approaches will be compared in their ability of mi tigating concerns on the true underlying longitudinal model\, while increa sing efficiency in decision-making.\n Bio: \;Tobias works a s Scientific Director in Janssen&rsquo\;s internal statistical consulting group. His primary consultancy responsibilities are on adaptive study desi gns\, the handling of multiplicity and statistical modelling in general. T obias joined Janssen in 2018 from ICON Clinical Research\, where he implem ented adaptive dose ranging designs\, including MCP-Mod\, into ADDPLAN DF. In his consultancy roles at ICON and Janssen\, he supported many innovati ve study designs projects\, including: inferentially seamless Phase 2/3 de signs\, adaptive Phase 2 Dose-Finding designs with MCPMod\, Phase 1/2 PoC Dose-Finding designs using Bayesian Go/No-Go criteria and designs with ada ptive endpoint selection. Tobias holds a PhD degree from Otto-von-Guericke -Universitä\;t Magdeburg in Germany. His doctoral dissertation was on the topic of optimum experimental design for nonlinear mixed effects model s.\n \n \n \n\n \;\nDiscussants\n\n \n \n \n \n France Mentré\;\n (School of Medicine of University of Paris)\n \n \n Bio: \;France Mentré\; is Professor of Biostatisti cs in the School of Medicine of University of Paris. She heads an INSERM r esearch team on Biostatistical Modelling and Pharmacometrics in treatment of Infectious Diseases. She has worked on development and application of m ethods for nonlinear mixed-effects models and pharmacometrics for more tha n 30 years. She applies these models to understand the variability in the response to anti-infective agents. She is leading the development of the s oftware PFIM for optimal design in pharmacometrics. She has published more than 250 articles in biostatistics\, pharmacometrics\, clinical pharmacol ogy or medical research.\n She received in 2013 the USCF/ISoP L ewis B. Sheiner Lecturer Award and in 2018 the ASCPT Sheiner-Beal Pharmaco metrics award. She is the co-chair and one of the founder of the Special I nterest Group on Statistics and Pharmacometrics of ASA and ISOP. She is ed itor in chief since October 2018 of CPT: Pharmacometrics and System Pharma cology.\n \n \n \n \n \n Rob Hemmings\n (Consilium)\n \n \n Bio: Rob Hemmings is a partner at Consilium. Consilium is hi s consultancy partnership with Tomas Salmonson\, a long-standing member of the EMA&rsquo\;s CHMP and formerly the chair of that committee. Tomas and Rob support companies in the development\, authorisation and life-cycle m anagement of medicines.\n Previously Rob worked at AstraZeneca and for 19 years at the Medicines and Healthcare products Regulatory Agenc y\, heading the group of medical statisticians and pharmacokineticists. He is a statistician by background and whilst working at MHRA he was co-opte d as a member of EMA&rsquo\;s CHMP for expertise in medical statistics and epidemiology. At CHMP he was Rapporteur for multiple products and was wid ely engaged across both scientific and policy aspects of the committee&rsq uo\;s work. He was fortunate to chair the CHMP&rsquo\;s Scientific Advice Working Party for 8 years and also chaired their expert groups on Biostati stics\, Modelling and Simulation and Extrapolation. He wrote or co-wrote m ultiple regulatory guidance documents\, including those related to estiman ds\, subgroups\, use of conditional marketing authorisation\, development of fixed-dose combinations\, extrapolation and adaptive designs. He has a particular interest in when and how to use data generated in clinical prac tice to support drug development.\n \n \n \n\n\n\n\n\ n DTEND:20191118T160000Z DTSTAMP:20240328T132542Z DTSTART:20191118T140000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Scientific Committee Webinar - Longitudinal modelling: Time to take the next step? UID:RFCALITEM638472291430269982 X-ALT-DESC;FMTTYPE=text/html:
Date: Monday 18th November 2019
\nTime: 14:00 - 16:00 UK Time
\nSpeakers: \; \;
\nPost-event access: To access the recording of this webinar\, please visit the Video On Demand library.
\n
\n
Registration: \;This webinar is free for PSI members\, but ha
s a charge of £\;20+VAT for non-members. To register\, please \;<
a href="https://members.psiweb.org/Core_Content_PSI/Events/Event_Display.a
spx?EventKey=204">click here. \; \;
\nPlease email \;
PSI@mci-group.com \;if you have
any questions.
\n___________________
\n
\nLongitudinal data\, i.e. data that arises from repeated observations of t
he variable over a period of time\, has long been put forward as one way t
o improve to the efficiency of drug development. However\, even though the
re is a rich statistical methodology for longitudinal data\, there is no f
ull\, wholehearted uptake of these methods in pharmaceutical statistics. T
he purpose of this webinar is to explore the use of longitudinal modelling
across drug development\, highlighting its opportunities (such as usage a
s primary analyses\, or for improved decision making at interim analyses)
and caveats. One important aspect to be discussed is the evaluation of the
efficiency of (parametric) longitudinal modelling versus standard cross-s
ectional approaches\, and factors based upon which one approach might be p
referable over the other.
\n
\n
\n
Speakers
\n\n
| \n \n
Partially this may be due to concerns about making assumptions on the shape of longitudinal profiles\, leading to the focus on cross-sectional (or landmark) analyses\, which do not require such assumptions\, but often only utilize a fraction of the information available per patient. By util izing the full information collected over time\, longitudinal modelling\, especially of the parametric type\, has the potential to lead to substanti ally more efficient drug development\, even when the primary endpoint is c ross-sectional in nature (e.g.\, change from baseline at Week 26). This po tential advantage needs to be balanced against standard concerns about mod el mis-specification and regulatory acceptance. \n | \n
\n
| \n
\n Title: When is a longitudinal test
better than a cross-sectional one for detecting a treatment effect? \
; While there are a number of papers showing huge gains for longitudinal testing\, current practice in clinical development is to focu s on a comparison at the last visit for the purpose of trial design and th e primary statistical analysis. In this presentation we try to characteriz e the factors and endpoint properties that determine if and by how much a longitudinal test will be more powerful than a cross-sectional test. \nWe consider the setting of a continuous endpoint measured r epeatedly over time and utilize a test that uses a weighted average of the treatment differences at the specific time points\, which is straightforw ard to implement with standard mixed effects software. We consider how to weight time-points optimally and which factors play a role in determining the weights. We then assess the potential gain of the longitudinal approac h in a set of real case examples. \nFinally\, a simulati on study is performed that compares this simple longitudinal approach to m ore strongly parameterized traditional longitudinal mixed effects model. < /p>\n Bio: \;Bjö\;rn works in the Novartis Statistcal Methodology and Consulting group. He consults for exam ple on dose-finding studies\, causal inference and estimands\, subgroup an alysis\, Bayesian statistics and statistical modelling. In 2013 he receive d the RSS/PSI award for developing innovative statistical dose-finding met hodology\, in particular the development of the software package DoseFindi ng in R. \n | \n
\n
| \n
\n Title: Decision-making using longit udinal modelling in presence of model uncertainty \nConce rns on model uncertainty have been widely discussed for dose-finding studi es in the recent scientific literature. The methodology can be generalized to longitudinal modelling\, covering model selection approaches\, model-b ased contrast tests and/or (Bayesian) model averaging approaches. The effe cts of model uncertainty on decision-making will be discussed in this pres entation. Different parametric and semi-parametric longitudinal modelling approaches will be evaluated for this purpose. The presented approaches wi ll be compared in their ability of mitigating concerns on the true underly ing longitudinal model\, while increasing efficiency in decision-making. p>\n Bio: \;Tobias works as Scientific Director in Janssen&rsquo\;s internal statistical consulting group. His pr imary consultancy responsibilities are on adaptive study designs\, the han dling of multiplicity and statistical modelling in general. Tobias joined Janssen in 2018 from ICON Clinical Research\, where he implemented adaptiv e dose ranging designs\, including MCP-Mod\, into ADDPLAN DF. In his consu ltancy roles at ICON and Janssen\, he supported many innovative study desi gns projects\, including: inferentially seamless Phase 2/3 designs\, adapt ive Phase 2 Dose-Finding designs with MCPMod\, Phase 1/2 PoC Dose-Finding designs using Bayesian Go/No-Go criteria and designs with adaptive endpoin t selection. Tobias holds a PhD degree from Otto-von-Guericke-Universit&au ml\;t Magdeburg in Germany. His doctoral dissertation was on the topic of optimum experimental design for nonlinear mixed effects models. \n | \n
 \;
\nDiscussants
\n
| \n \n
Bio: \;France Mentré\; is Professor of Biostatistics in the School of Medicine of University of Paris. She he ads an INSERM research team on Biostatistical Modelling and Pharmacometric s in treatment of Infectious Diseases. She has worked on development and a pplication of methods for nonlinear mixed-effects models and pharmacometri cs for more than 30 years. She applies these models to understand the vari ability in the response to anti-infective agents. She is leading the devel opment of the software PFIM for optimal design in pharmacometrics. She has published more than 250 articles in biostatistics\, pharmacometrics\, cli nical pharmacology or medical research. \nShe received i n 2013 the USCF/ISoP Lewis B. Sheiner Lecturer Award and in 2018 the ASCPT Sheiner-Beal Pharmacometrics award. She is the co-chair and one of the fo under of the Special Interest Group on Statistics and Pharmacometrics of A SA and ISOP. She is editor in chief since October 2018 of CPT: Pharmacomet rics and System Pharmacology. \n | \n
\n
| \n \n Bio: Rob Hemm ings is a partner at Consilium. Consilium is his consultancy partnership w ith Tomas Salmonson\, a long-standing member of the EMA&rsquo\;s CHMP and formerly the chair of that committee. Tomas and Rob support companies in t he development\, authorisation and life-cycle management of medicines. \nPreviously Rob worked at AstraZeneca and for 19 years at the Medicines and Healthcare products Regulatory Agency\, heading the grou p of medical statisticians and pharmacokineticists. He is a statistician b y background and whilst working at MHRA he was co-opted as a member of EMA &rsquo\;s CHMP for expertise in medical statistics and epidemiology. At CH MP he was Rapporteur for multiple products and was widely engaged across b oth scientific and policy aspects of the committee&rsquo\;s work. He was f ortunate to chair the CHMP&rsquo\;s Scientific Advice Working Party for 8 years and also chaired their expert groups on Biostatistics\, Modelling an d Simulation and Extrapolation. He wrote or co-wrote multiple regulatory g uidance documents\, including those related to estimands\, subgroups\, use of conditional marketing authorisation\, development of fixed-dose combin ations\, extrapolation and adaptive designs. He has a particular interest in when and how to use data generated in clinical practice to support drug development. \n | \n
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