In this webinar we look at some recent advances in statisti cal methods for identifying treatment effect heterogeneity in clinical tri als. This ranges from identifying baseline biomarkers likely to influence the treatment effect (ranking) to provide novel biomarker 'signatures' (su bgroups) with associated estimated enhanced effect (Individual Treatment E ffects). Some practical issues ranges from overfitting risks\, biases\, an d confounding of prognostic and predictive effects. Modern methods aim to overcome such potential difficulties while remaining flexible\, and offer a structured approach to the problem (aiming to avoid the notorious 'data dredging'). The novel techniques are often tree based and/or penalized reg ression\, i.e.\, with a machine learning flavour. Sometimes the aim of the analysis is to predict the individual optimal treatment allocation given baseline biomarker data (Individual Treatment Rules). Efficient Visualizat ion of relationships in the data is also of importance in the practical ap plications. The talks will highlight and discuss such aspects and will als o reflect typical aspects discussed within the EFSPI/PSI Subgroup Special Interest Group. (While this event is not intended as a formal course\, it will still serve as an introduction and overview to the area\, as well as covering some more technically challenging material for the more experienc ed participant).
\nThis webinar is free of ch
arge to both Members and Non-Members of PSI.
\nTo register your place
\, please click here. \;
Bjö\;rn Bornkam p works since 10 years in the Statistical Methodology Group at Novartis in Basel\, where he provides consulting to statisticians and clinical teams on topics related to dose-finding studies\, subgroup analyses\, Bayesian s tatistics as well as estimands and causal inference. Bjö\;rn holds a P hD in Statistics from TU Dortmund University in Germany. He received the R SS/PSI award for developing statistical dose-finding methodology\, in part icular the DoseFinding R package
\n \n\n Speaker \n | \n Biography \n | \n
Abstract \n |
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| \n Senior Statistical Science Director at the Statistical I nnovation group\, Data Science and AI\, Gothenburg. Has a Ph.D. in Mathema tical Statistics from Chalmers University of Technology\, Sweden and was P ost Doc at the University of Zurich &\; ETH in 2000-2003\, working & \; teaching in the Bioinformatics field. Joined AstraZeneca R&\;D in 20 03\, with involvement ranging from Discovery science to Late Phase drug de velopment\, with a particular interest in exploratory statistical modellin g and machine learning. Current Lead of the EFSPI Subgroup Special Interes t Group. \n | \n Introduction |
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| \n Ilya L ipkovich is a Sr. Research Advisor at Eli Lilly and Company. Ilya received his PhD in Statistics from Virginia Tech in 2002 and has more than 15 yea rs of statistical consulting experience in pharmaceutical industry. He is an ASA Fellow and published on subgroup identification in clinical data\, analysis with missing data\, and causal inference. He is a frequent presen ter at conferences\, a co-developer of subgroup identification methods\, a nd a co-author of the books &ldquo\;Analyzing Longitudinal Clinical Trial Data. A Practical Guide&rdquo\; and &ldquo\;Estimands\, Estimators and Sen sitivity Analysis in Clinical Trials.&rdquo\; \n | \n O
verview of recent innovations in subgroup identification for personalized
medicine and related methods \; \n |
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| \n Benchmarking a
lgorithms for discovering subgroups with differential treatment effect \; \n | |
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| \n Kostas is an Associate Director of Data Science in Novartis&rsquo\; Advanc ed Exploratory Analytics group. His main areas of interest are machine lea rning based biomarker discovery\, subgroup identification\, and developmen t of digital endpoints. Kostas did his PhD in statistical machine learning on the area of hypothesis testing and feature selection in semi-supervise d scenarios in the University of Manchester&rsquo\;s Department of Compute r Science. Afterwards\, he spent many years as post-doctoral researcher on developing novel methodologies for analysing: self-reported epidemiologic al data with Manchester&rsquo\;s Health e-Research Center\, clinical trial s data for personalised medicine with AstraZeneca and digital healthcare d ata for digital biomarker development with Roche. More information about h is work can be found at: https://s echidis.netlify.app/ \n | \n Using knockoffs for c
ontrolled predictive biomarker identification |
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| \n Eusebi is a Statistician and Statistical Programmer. After obtaining a PhD in Mathematics and Statistics in 2007\, he has worked as a statistician both in academia and in public health sec tor. Since 2019 he joined UCB and the pharmaceutical industry. His main re search interests are evidence synthesis\, patient reported outcomes\, stat istical learning techniques for subgroup identification\, and data visuali zation. He is Senior Editorial Board Member of BMC Medical Research Method ology and an active member of PSI and Data Visualization Society. \n\; \n | \n Visualize and interact with
subgroups \; \n |
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