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.
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\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.
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Speakers
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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 |
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| \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 |
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| \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.\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 |
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\nDiscussants
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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 |
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| \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 |
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