PSI Webinar: Discover Data Science

Date: Wednesday 17th May 2023
Time: 11:30-13:00 BST
Location: Online
Speakers: Carsten Henneges (Sanofi), Munshi Imran Hossain (Cytel) and Prof. Keith Abrams (University of Warwick).

Who is this event intended for? Statisticians who are interested in understanding the methods and applications of data science across the pharmaceutical industry and health applications.
What is the benefit of attending? You will learn about where data science methods may influence the pharmaceutical industry and healthcare research gaining insight into how they might complement your current work.


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


To register for this event, please click here.


Data Science is a growing area of expertise 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 perspectives. Three speakers from a pharmaceutical, CRO and academic perspective talk about what Data Science in medicine means to them. The session will include examples from their work and a panel discussion.

Speaker details




Carsten Henneges

Carsten Henneges studied bioinformatics with main at molecular biology at the Eberhard-Karls University of Tübingen. He received a PhD in computer science for research in applied machine learning and data mining in Proteomics and Metabolomics. He then worked for 8 years at Eli Lilly as project statistician supporting late phase trials and analyses across multiple therapeutic areas. He received the certificate for Biometry in Medicine from the GMDS in 2017. After a short period working for the Comprehensive Heart Failure Center in Würzburg and supporting the Early Phase Immuno-Oncology team at Genentech, he is employed by 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.


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 needs of Data Scientists in Pharma.

Munshi Imran Hossain


Munshi Imran Hossain is a Senior Research Consultant at the Therapeutic Development Group at Cytel. He is a trained Biomedical engineer. 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 problems. He has worked on biomarker signatures for enrichment trials, analysis of wearables data for device trials, analysis of multi-array gene expression data, among others. Imran is also a member of the R Validation Hub where he is working on the risk assessment of R packages.

Data science in healthcare has seen rapid growth because of the availability of large amounts of data of various kinds. Today, companies have access to real-time data from mobile phones and wearable devices. They have access to gene expression data as well as many different biomarkers.
As part of the consulting group, we are fortunate to have the opportunity to work on different kinds of problems. One of the most common problems that we encounter is the discovery of biomarker signatures that companies 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.
Another important, although oft-neglected, aspect is the reproducibility and explainability of results. Healthcare is highly regulated; the preference is for models whose inner workings can be easily explained. The reproducibility of results is another challenge. This requires mature data and workflow pipelines that allow for accessing and processing large quantities of data in a reproducible environment.
In this talk, I would like to reflect on some of the challenging problems and the challenges faced during the building of the solution.

Prof. Keith Abrams

Keith Abrams is Professor of Statistics & Data Science in the Department of Statistics at the University of Warwick and a National Institute for Health Research (NIHR) Senior Investigator Emeritus. He is also  Honorary Professor in 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 Data 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 with the UK National Institute for Health & Care Excellence (NICE) since its inception. He was a member of the NICE Technology Appraisals Committee for over 8 years, and is currently a member of the NICE Diagnostics Advisory Committee, NICE Decision Support Unit (DSU) and NICE Technical Support Unit (TSU). He is a Fellow of the Royal Statistical Society, and a Chartered Statistician. He has published widely in both substantive and methodological areas including co-authoring books on Methods for Meta-Analysis in Medical Research, Bayesian Approaches to Clinical Trials and Healthcare Evaluation, and Evidence Synthesis for Decision Making in Healthcare, in addition to co-editing a text on Methods for Evidence-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 range 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 – 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 talk I will touch on a number of these including; issues with linked Electronic Health Record [EHR] data (including problems with ignoring data generating mechanisms), increasing 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 outcomes), and how agencies such as NICE, in England & Wales, evaluate such technologies to inform health policy.


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