PSI Webinar: Discover Data Science

PSI Training Course: Introduction to Machine Learning

This course is aimed at clinical trial statisticians who are new to or with limited experience of machine learning. Attendees will learn about a range of topics in machine learning, including practical sessions in R.

Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection

This webinar will explore two different approaches to Correlates of Protection in vaccine development.

Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars

Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.

PSI Webinar: Drug Development in Women's Health

The event will open with an overview on drug development in women’s health from a clinician perspective. This talk is followed by talks about statistical challenges when planning IVF studies and analysing the menstrual cycles.

PSI Webinar: Surrogacy

This webinar will provide an overview of surrogacy for licensing and reimbursement. In turn, the need of extensions of the SPIRIT and CONSORT statement will be defined and outlined, with case studies to support.

Joint PSI/EFSPI Pre-Clinical SIG Webinar: Virtual Control Groups in Toxicity Studies

Lea Vaas will present how replacement of concurrent control animals by Virtual Control Groups (VCGs) in systemic toxicity studies may help in contributing to the 3R's principle of animal experimentation: Reduce, Refine, Replace.

Joint PSI/EFSPI Data Science SIG Webinar: Developing Digital Measures (Digital Biomarkers) in Drug Development – insights from Mobilise D consortium

We will share a brief overview of what Mobilise D is and why it is an important step stone in the development of digital biomarkers, and how Mobilise D outputs can be relevant for you.

PSI Career Young Virtual Event (Q2 2024)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Career Young Virtual Event (Q3 2024)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Introduction to Industry Training (ITIT) Course - 2024/2025

An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.

PSI Career Young Virtual Event (Q4 2024)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

Speaker	Biography	Abstract
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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