PSI Webinar: Discover Data Science

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.

AIMS SIG - Open-Source Lunch Bites

Topic: R Package Basics. Our monthly webinar series allows attendees to gain practical knowledge and skills in open-source coding and tools, with a focus on applications in the pharmaceutical industry. This month’s session, “R Package Basics,” will introduce the fundamentals of working with R packages—covering how to install, load, and manage them effectively to support data analysis and reproducible research. The session will provide a solid starting point, clarify common misconceptions, and offer valuable resources for continued learning.

PSI Mentoring 2026

Date: Ongoing 6 month cycle beginning late April/early May 2026 Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.

PSI Book Club Lunch and Learn: Communicating with Clarity and Confidence

If you have read Ros Atkins’ book The Art of Explanation or want to listen to the BBC’s ‘Communicator in Chief’, you are invited to join the PSI Book Club Lunch and Learn, to discuss the content and application with the author, Ros Atkins. Having written the book within the context of the news industry, Ros is keen to hear how we have applied the ideas as statisticians within drug development and clinical trials. There will be dedicated time during the webinar to ASK THE AUTHOR any questions – don’t miss out on this exclusive PSI Book Club event! Haven’t read the book yet? Pick up a copy today and join us. Explanation - identifying and communicating what we want to say - is described as an art, in the title of his book. However, the creativity comes from Ros’ discernment in identifying and describing a clear step-by-step process to follow and practice. Readers can learn Ros’ rules, developed and polished throughout his career as a journalist, to help communicate complex written or spoken information clearly.

PSI Training Course: Effective Leadership – the keys to growing your leadership capabilities

This course will consist of three online half-day workshops. The first will be aimed at building trust, the backbone of leadership and a key to becoming effective. This is key to building a solid foundation. The second will be on improving communication as a technical leader. This workshop will focus on communication strategies for different stakeholders and will involve tips on effective communication and how to develop the skills of active listening, coaching and what improv can teach us about good communication. The final workshop will bring these two components together to help leaders become more influential. This will also focus on how to use Steven Covey’s 7-Habits, in particular Habits 4, 5 and 6, which are called the habits of communication. The workshops will be interactive, allowing you to practice the concepts discussed. There will be plenty of time for questions and discussion. There will also be reflective time where you can think about what you are learning and how you might experiment with it.

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.

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.

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.

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.

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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