BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 15.4//EN BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD DTSTART;VALUE=DATE:20250101 TZNAME:UTC TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: Wednesday 17th May 2023\nTime: \;11:30-13:00 BST\nLoc ation: Online\nSpeakers: \;Carsten Henneges \;(Sanofi)\, Munshi Im ran Hossain \;(Cytel) and Prof. Keith Abrams \;(University of Warw ick).\n\nWho is this event intended for? \;Statisticians who are inter ested in understanding the methods and applications of data science across the pharmaceutical industry and health applications.\nWhat is the benefit of attending? \;You will learn about where data science methods may i nfluence the pharmaceutical industry and healthcare research gaining insig ht into how they might complement your current work.\nCost\nThis webinar i s free of charge to both Members and Non-Members of PSI.\nRegistration\nTo register for this event\, please click here.\nOverview\nData Science is a growing area of expertise in the pharmaceutical industry complementing tr aditional statistical methods that are more well-established. In this webi nar\, we explore the application of data science methods in medicine\, tak ing in a range of perspectives. Three speakers from a pharmaceutical\, CRO and academic perspective talk about what Data Science in medicine means t o them. \;The session will include examples from their work and a pane l discussion.\nSpeaker details\n\n\n\n \n \n \n Speaker\n \n \n Biography\n \n \n Abstract\n \n \n \n \n \n Carsten Henneges\n \n \n Carsten Henneges studied bioinformatics with mai n at molecular biology at the Eberhard-Karls University of Tü\;bingen. He received a PhD in computer science for research in applied machine lea rning and data mining in Proteomics and Metabolomics. He then worked for 8 years at Eli Lilly as project statistician supporting late phase trials a nd analyses across multiple therapeutic areas. He received the certificate for Biometry in Medicine from the GMDS in 2017. After a short period work ing for the Comprehensive Heart Failure Center in Wü\;rzburg and suppo rting the Early Phase Immuno-Oncology team at Genentech\, he is employed b y Sanofi at the mRNA center of excellence. Currently he has been an active member of the PSI Data Science SIG since its initiation in 2019.\n  \;\n \n \n How much and where i s Software Development needed to be successful in Drug Development? As Dat a fuels this Industry\, it is inevitably a part of it. This presentation w ill try to shed some light onto the role and needs of Data Scientists in P harma.\n \n \n \n \n \n Munshi Imran Hossain\n  \;\n \n \n Munshi Imran Hossain is a Senior Research Consultant at t he Therapeutic Development Group at Cytel. He is a trained Biomedical engi neer. Imran has over 10 years of experience in the design and analysis of adaptive trials. He has also been involved in working on data science prob lems. He has worked on biomarker signatures for enrichment trials\, analys is of wearables data for device trials\, analysis of multi-array gene expr ession data\, among others. Imran is also a member of the R Validation Hub where he is working on the risk assessment of R packages.\n \n \n Data science in healthcare has seen rapid growth because of the availability of large amounts of data of various kinds. To day\, companies have access to real-time data from mobile phones and weara ble devices. They have access to gene expression data as well as many diff erent biomarkers.\n As part of the consulting group\, we are fo rtunate to have the opportunity to work on different kinds of problems. On e of the most common problems that we encounter is the discovery of biomar ker signatures that companies want to use for enrichment studies. We've al so seen other interesting problems such as signal alignment and reliabilit y when there is data from multiple sources.\n Another important \, although oft-neglected\, aspect is the reproducibility and explainabili ty of results. Healthcare is highly regulated\; the preference is for mode ls whose inner workings can be easily explained. The reproducibility of re sults is another challenge. This requires mature data and workflow pipelin es that allow for accessing and processing large quantities of data in a r eproducible environment.\n In this talk\, I would like to refle ct on some of the challenging problems and the challenges faced during the building of the solution.\n \n \n \n \n \n Prof. Keith Abrams\n \n Keith Abrams is Professor of Statistics &\; Data Science in the Departm ent of Statistics at the University of Warwick and a National Institute fo r Health Research (NIHR) Senior Investigator Emeritus. He is also \; H onorary Professor in the Centre for Health Economics at the University of York. His research centres around the development\, evaluation\, and appli cation of (Bayesian) statistical methods in Health Technology Assessment ( HTA) and Health Data Science\, and is supported by EU/UKRI\, Health Data R esearch (HDR) UK\, Medical Research Council (MRC)\, National Institute for Health &\; care Research (NIHR) and industry. Prof Abrams has been ext ensively involved with the UK National Institute for Health &\; Care Ex cellence (NICE) since its inception. He was a member of the NICE Technolog y Appraisals Committee for over 8 years\, and is currently a member of the NICE Diagnostics Advisory Committee\, NICE Decision Support Unit (DSU) an d NICE Technical Support Unit (TSU). He is a Fellow of the Royal Statistic al Society\, and a Chartered Statistician. He has published widely in both substantive and methodological areas including co-authoring books on Meth ods for Meta-Analysis in Medical Research\, Bayesian Approaches to Clinica l Trials and Healthcare Evaluation\, and Evidence Synthesis for Decision M aking in Healthcare\, in addition to co-editing a text on Methods for Evid ence-based Healthcare. Prof Abrams has extensive experience over the last 25 years as a consultant to the pharmaceutical and life sciences sectors\, providing both methodological and strategic HTA advice across a wide rang e of therapeutic areas\, as well as internationally to non-UK governments and reimbursement/HTA agencies. He is also a founding partner and director of Visible Analytics Limited &ndash\; an international HTA consultancy co mpany headquartered in Oxford\, UK. \n The current e xplosion in data availability raises a number of issues and challenges as regards how they should be analysed. In this talk I will touch on a number of these including\; issues with linked Electronic Health Record [EHR] da ta (including problems with ignoring data generating mechanisms)\, increas ing access to individual study data &\; use of federated analyses\, the explosion in data-driven health technologies (producing high-dimensional\ , high frequency data and the need to link such data to clinical/process o utcomes)\, and how agencies such as NICE\, in England &\; Wales\, evalu ate such technologies to inform health policy. \n \n \n\n \; DTEND:20230517T120000Z DTSTAMP:20260721T170852Z DTSTART:20230517T103000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Webinar: Discover Data Science UID:RFCALITEM639202505326267415 X-ALT-DESC;FMTTYPE=text/html:Date: Wednesday 17th May 2023
\nTime: \;11:30-13:00 BST
\nLocati on: Online
\nSpeakers: \;Carsten Henneg es \;(Sanofi)\, Munshi Imran Hossain \;(Cytel) a nd Prof. Keith Abrams \;(University of Warwick).
\n
\nWho is this event intended for? \;Statisticians wh o are interested in understanding the methods and applications of data sci ence across the pharmaceutical industry and health applications.
\nWhat is the benefit of attending? \;You will learn abou t where data science methods may influence the pharmaceutical industry and healthcare research gaining insight into how they might complement your c urrent work.
\n

Cost

\n

This webinar is free of charge to bo th Members and Non-Members of PSI.

\n

Registration

\n

To regis ter for this event\, please click here.

\n

Overview

\n

Data Science is a growing area of expe rtise in the pharmaceutical industry complementing traditional statistical methods that are more well-established. In this webinar\, we explore the application of data science methods in medicine\, taking in a range of per spectives. Three speakers from a pharmaceutical\, CRO and academic perspec tive talk about what Data Science in medicine means to them. \;The ses sion will include examples from their work and a panel discussion.

\nSpeaker details\n\n
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Speaker

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Biography

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Abstract

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Carstenedit
\n Carsten Henneges

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Carste n Henneges studied bioinformatics with main at molecular biology at the Eb erhard-Karls University of Tü\;bingen. He received a PhD in computer s cience for research in applied machine learning and data mining in Proteom ics and Metabolomics. He then worked for 8 years at Eli Lilly as project s tatistician supporting late phase trials and analyses across multiple ther apeutic areas. He received the certificate for Biometry in Medicine from t he GMDS in 2017. After a short period working for the Comprehensive Heart Failure Center in Wü\;rzburg and supporting the Early Phase Immuno-Onc ology team at Genentech\, he is employed by Sanofi at the mRNA center of e xcellence. Currently he has been an active member of the PSI Data Science SIG since its initiation in 2019.

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 \;

\n 		\n

How much and where is Software Development needed to be successful in Drug Development? As Data fuels this Industry\, it is inevitably a part of it. This presentation will try to shed some light onto the role and ne eds of Data Scientists in Pharma.

\n
\n

Imranedit
\n Munshi Imran Hossain

\n

 \;

\n 		\n

Munshi Imran Hossain is a Senior Research Consultant at the Therape utic Development Group at Cytel. He is a trained Biomedical engineer. Imra n has over 10 years of experience in the design and analysis of adaptive t rials. He has also been involved in working on data science problems. He h as worked on biomarker signatures for enrichment trials\, analysis of wear ables data for device trials\, analysis of multi-array gene expression dat a\, among others. Imran is also a member of the R Validation Hub where he is working on the risk assessment of R packages.

\n 		\n

Data s cience in healthcare has seen rapid growth because of the availability of large amounts of data of various kinds. Today\, companies have access to r eal-time data from mobile phones and wearable devices. They have access to gene expression data as well as many different biomarkers.
\n As part of the consulting group\, we are fortunate to have the opport unity to work on different kinds of problems. One of the most common probl ems that we encounter is the discovery of biomarker signatures that compan ies want to use for enrichment studies. We've also seen other interesting problems such as signal alignment and reliability when there is data from multiple sources.
\n Another important\, although oft-negl ected\, aspect is the reproducibility and explainability of results. Healt hcare is highly regulated\; the preference is for models whose inner worki ngs can be easily explained. The reproducibility of results is another cha llenge. This requires mature data and workflow pipelines that allow for ac cessing and processing large quantities of data in a reproducible environm ent.
\n In this talk\, I would like to reflect on some of the challenging problems and the challenges faced during the building of t he solution.

\n
\n

Keithedit
\n Prof. Keith Abrams

\n 		Keith Abrams is Pro fessor of Statistics &\; Data Science in the Department of Statistics a t the University of Warwick and a National Institute for Health Research ( NIHR) Senior Investigator Emeritus. He is also \; Honorary Professor i n the Centre for Health Economics at the University of York. His research centres around the development\, evaluation\, and application of (Bayesian ) statistical methods in Health Technology Assessment (HTA) and Health Dat a Science\, and is supported by EU/UKRI\, Health Data Research (HDR) UK\, Medical Research Council (MRC)\, National Institute for Health &\; care Research (NIHR) and industry. Prof Abrams has been extensively involved w ith the UK National Institute for Health &\; Care Excellence (NICE) sin ce its inception. He was a member of the NICE Technology Appraisals Commit tee for over 8 years\, and is currently a member of the NICE Diagnostics A dvisory Committee\, NICE Decision Support Unit (DSU) and NICE Technical Su pport Unit (TSU). He is a Fellow of the Royal Statistical Society\, and a Chartered Statistician. He has published widely in both substantive and me thodological areas including co-authoring books on Methods for Meta-Analys is in Medical Research\, Bayesian Approaches to Clinical Trials and Health care Evaluation\, and Evidence Synthesis for Decision Making in Healthcare \, in addition to co-editing a text on Methods for Evidence-based Healthca re. Prof Abrams has extensive experience over the last 25 years as a consu ltant to the pharmaceutical and life sciences sectors\, providing both met hodological and strategic HTA advice across a wide range of therapeutic ar eas\, as well as internationally to non-UK governments and reimbursement/H TA agencies. He is also a founding partner and director of Visible Analyti cs Limited &ndash\; an international HTA consultancy company headquartered in Oxford\, UK. The current explosion in data availability raises a number of issues and challenges as regards how they should be analysed. In this tal k I will touch on a number of these including\; issues with linked Electro nic Health Record [EHR] data (including problems with ignoring data genera ting mechanisms)\, increasing access to individual study data &\; use o f federated analyses\, the explosion in data-driven health technologies (p roducing high-dimensional\, high frequency data and the need to link such data to clinical/process outcomes)\, and how agencies such as NICE\, in En gland &\; Wales\, evaluate such technologies to inform health policy.
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