Susan Mayo
(FDA)

Susan is a senior mathematical statistician at the Food and Drug Administration, Center for Drug Evaluation’s Office of Biostatistics, with a demonstrated interest and impact in areas that help to make sound regulatory and drug development decisions: graphical design, drug safety and benefit-risk assessment, and the estimand framework. She has been with FDA for over 3 years, and previously worked as an industry statistician and internal company consultant in biotech and big pharma for a few more than that.

Making Impactful Graphs: Looking through the eyes of your audience
Anyone who has graphed data has discovered there is both an art and science to doing it well. Graphs take more effort to create than tables, and when constructed well, they have the potential for their audience to see deeper into the data when there are complexities a table may not be able to address. This talk aims to address some overlooked factors beyond the technical aspects of graphing data. What are the human brain’s visual superpowers? How can a graph be more impactful with its audience? The talk will conclude with some considerations for impactful use of interactive graphics. Susan’s talk will cover:

• Visual perception, and its relationship with statistical graphics design
• Pharma/regulatory interactions –a personal perspective
• Impactful use of interactive graphics

Jeremy Wildfire
(Gilead)

Jeremy Wildfire is a Director of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worked in clinical trial research for 15 years, first as a biostatistician on NIH funded asthma and allergy studies and more recently on cross-functional data science teams focused on creating open source tools that seek to improve the clinical trial analysis pipeline.

Building Open Source Tools for Safety Monitoring: Advancing Research Through Community Collaboration
The Interactive Safety Graphics workstream of the ASA-DIA Biopharm Safety Working Group is excited to introduce version 2 of the safetyGraphics R package. safetyGraphics is an interactive framework for evaluating clinical trial safety in R. Version 2 includes support for multiple data domains, reusable data pipelines and user-defined custom charts. This enhanced framework allows users to easily re-use both static and interactive charts on multiple studies. safetyGraphics includes several interactive graphics by default, including a chart for monitoring drug-induced liver injury that is paired with an in-depth clinical workflow. Charts using existing R packages can also be added to the framework via a straightforward mapping process. To ensure quality and accuracy, the package includes more than 300 automated unit tests and has been vetted through a beta testing process that included feedback from more than 20 clinicians and analysts.

The Interactive Safety Graphics group seeks to modernize clinical trial safety monitoring by building tools for data exploration and reporting in a highly collaborative open source environment. At present, our team includes clinical and technical representatives from the pharmaceutical industry, academia, and the FDA, and additional contributors are always welcome

Sheila Dickinson (Novartis)

Sheila Dickinson is a Global Benefit-Risk Lead, working in the Quantitative Safety and Epidemiology group at Novartis. Her responsibilities include promoting and facilitating the use of a structured benefit-risk approach by Novartis project teams. Sheila is also working on the topic of patient preference studies and is on the management board of the IMI PREFER project, which is working on developing guidelines about when and how to perform patient preference studies to support medical product decision-making.

Sheila holds a degree in mathematics from Imperial College, London and an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine. After joining Novartis in 1997, she worked as a statistician supporting projects in the various disease areas including both diabetes and malaria, before moving to the Quantitative Safety team in 2013.

Points to bear in mind for visual displays of benefit-risk data
Recent years have seen an increased focus on taking a structured approach to the description of benefit-risk in a Clinical Overview. This presentation will cover:

• The regulatory expectations about structured benefit-risk
• How these regulatory expectations influence our approach to visual displays of benefit-risk data
• Suggestions for addressing common issues when displaying benefit-risk data
• Example benefit-risk data display
• Comments on using an R-Shiny App as a tool to create benefit-risk figures

Matthias Trampisch (Boehringer Ingelheim)

Matthias works as a Safety Statistician in independent Safety Analysis Team (iSAT) at Boehringer Ingelheim. iSAT is specialized on analyzing ongoing trial data in an unblinded fashion supporting interim analysis, ad-hoc unblinding requests, or Data Monitoring Committees (DMCs).

Dynamic data visualization for Benefit/Risk Assessment DURING trial conduct - Insights based on DMC output for monitoring a trial in patients hospitalized with COVID-19
Benefit/Risk assessment is a fundamental part of any clinical trial report. However, during trial conduct, the analysis of Benefit/Risk may substantially differ from the final assessment due to various reasons: ongoing recruitment, incomplete/missing data, time points not (yet) available and so on.

This talk presents learnings from a recent Boehringer Ingelheim trial targeting to prevent Adult Respiratory Distress Syndrom (ARDS) in hospitalized COVID-19 patients. It focuses on a specific output created for the Data Monitoring Committees (DMC) which combined most of the pre-defined efficacy and safety endpoints in an interactive heat-plot. Sample data and code to generate the plot will be provided

Patrick Schloemer (Bayer)

Patrick received his PhD in Statistics from the University of Bremen, Germany, in 2014 for his work on group sequential and adaptive designs for three-arm non-inferiority trials. Since then he has been working at Bayer as a clinical statistician in the cardio-renal area with increasing responsibilities. Currently he acts as the Compound Statistician for a novel treatment for chronic kidney disease in type 2 diabetes that recently received FDA approval.

His methodological interests include group sequential and adaptive designs, multiple comparison procedures and recurrent events. In the past years he has been working on the application for an EMA “Qualification opinion of clinically interpretable treatment effect measures based on recurrent event endpoints that allow for efficient statistical analyses”. Besides this he has been actively involved in the development of the Data Insight Generation (D.I.G) concept at Bayer, which was recently piloted in a large Phase III trial to gain deeper insights into the study data by means of intelligent visualizations.

Data Insight Generation (D.I.G) – A concept to foster interactive and interdisciplinary data investigations using intelligent visualizations
In recent years various data visualization apps have been developed as part of Bayer’s Biostatistics Innovation Center (BIC), with the objective to provide insights that go beyond the classical Tables, Listings and Figures (TLFs) which are the basis for the submission dossier. The Data Insight Generation (D.I.G) concept has been developed to foster and streamline the use of these data visualization apps and position them as a central piece in the interpretation of clinical study data at Bayer. The D.I.G activities culminate in a two-day workshop shortly after TLF delivery in a dedicated, fully-equipped meeting room, where a cross-functional team uses intelligent visualizations and machine learning methods to gain broader and deeper insights into the study data. This talk will give an overview about the D.I.G concept and present first-hand experiences from the pilot D.I.G workshop that was recently held after the close-out of a large Phase III trial.

Charlotta Fruechtenicht (Roche)

Charlotta is a computational biologist by training and works as a senior data scientist in the Data, Analytics & Imaging team in the Pharma Development Personalized Healthcare department at Roche. Her interests lie in using fit-for-purpose analytics (including graphical design) to untap the wealth of multimodal data coming from healthcare care systems in the real world to support the development of new medicines.

Learnings from implementing good graphical principles in a ready-to-use R package
In this talk we will present key learnings from a cross-pharma collaboration tackling the problem of streamlining effective and efficient graphical communication. The application of good graphical principles to the output of statistical analyses, especially in R, can be time consuming and tedious and is thus oftentimes omitted. However, effective visualization is important to enable clear communication between Data Scientists and stakeholders, which makes it crucial to facilitate informed decision making. To enable easy-to-use, multi-purpose visualizations for clinical data, we collaborated across multiple Pharma companies and functions to develop the thoroughly tested R package visR. This new software-package, which is now available on CRAN, enables seamless integration of effective visualization (figures and tables) into analytics and reporting workflows.

Susan Mayo
(FDA)

Susan is a senior mathematical statistician at the Food and Drug Administration, Center for Drug Evaluation’s Office of Biostatistics, with a demonstrated interest and impact in areas that help to make sound regulatory and drug development decisions: graphical design, drug safety and benefit-risk assessment, and the estimand framework. She has been with FDA for over 3 years, and previously worked as an industry statistician and internal company consultant in biotech and big pharma for a few more than that.

Making Impactful Graphs: Looking through the eyes of your audience
Anyone who has graphed data has discovered there is both an art and science to doing it well. Graphs take more effort to create than tables, and when constructed well, they have the potential for their audience to see deeper into the data when there are complexities a table may not be able to address. This talk aims to address some overlooked factors beyond the technical aspects of graphing data. What are the human brain’s visual superpowers? How can a graph be more impactful with its audience? The talk will conclude with some considerations for impactful use of interactive graphics. Susan’s talk will cover:

• Visual perception, and its relationship with statistical graphics design
• Pharma/regulatory interactions –a personal perspective
• Impactful use of interactive graphics

Jeremy Wildfire
(Gilead)

Jeremy Wildfire is a Director of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worked in clinical trial research for 15 years, first as a biostatistician on NIH funded asthma and allergy studies and more recently on cross-functional data science teams focused on creating open source tools that seek to improve the clinical trial analysis pipeline.

Building Open Source Tools for Safety Monitoring: Advancing Research Through Community Collaboration
The Interactive Safety Graphics workstream of the ASA-DIA Biopharm Safety Working Group is excited to introduce version 2 of the safetyGraphics R package. safetyGraphics is an interactive framework for evaluating clinical trial safety in R. Version 2 includes support for multiple data domains, reusable data pipelines and user-defined custom charts. This enhanced framework allows users to easily re-use both static and interactive charts on multiple studies. safetyGraphics includes several interactive graphics by default, including a chart for monitoring drug-induced liver injury that is paired with an in-depth clinical workflow. Charts using existing R packages can also be added to the framework via a straightforward mapping process. To ensure quality and accuracy, the package includes more than 300 automated unit tests and has been vetted through a beta testing process that included feedback from more than 20 clinicians and analysts.

The Interactive Safety Graphics group seeks to modernize clinical trial safety monitoring by building tools for data exploration and reporting in a highly collaborative open source environment. At present, our team includes clinical and technical representatives from the pharmaceutical industry, academia, and the FDA, and additional contributors are always welcome

Sheila Dickinson (Novartis)

Sheila Dickinson is a Global Benefit-Risk Lead, working in the Quantitative Safety and Epidemiology group at Novartis. Her responsibilities include promoting and facilitating the use of a structured benefit-risk approach by Novartis project teams. Sheila is also working on the topic of patient preference studies and is on the management board of the IMI PREFER project, which is working on developing guidelines about when and how to perform patient preference studies to support medical product decision-making.

Sheila holds a degree in mathematics from Imperial College, London and an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine. After joining Novartis in 1997, she worked as a statistician supporting projects in the various disease areas including both diabetes and malaria, before moving to the Quantitative Safety team in 2013.

Points to bear in mind for visual displays of benefit-risk data
Recent years have seen an increased focus on taking a structured approach to the description of benefit-risk in a Clinical Overview. This presentation will cover:

• The regulatory expectations about structured benefit-risk
• How these regulatory expectations influence our approach to visual displays of benefit-risk data
• Suggestions for addressing common issues when displaying benefit-risk data
• Example benefit-risk data display
• Comments on using an R-Shiny App as a tool to create benefit-risk figures

Matthias Trampisch (Boehringer Ingelheim)

Matthias works as a Safety Statistician in independent Safety Analysis Team (iSAT) at Boehringer Ingelheim. iSAT is specialized on analyzing ongoing trial data in an unblinded fashion supporting interim analysis, ad-hoc unblinding requests, or Data Monitoring Committees (DMCs).

Dynamic data visualization for Benefit/Risk Assessment DURING trial conduct - Insights based on DMC output for monitoring a trial in patients hospitalized with COVID-19
Benefit/Risk assessment is a fundamental part of any clinical trial report. However, during trial conduct, the analysis of Benefit/Risk may substantially differ from the final assessment due to various reasons: ongoing recruitment, incomplete/missing data, time points not (yet) available and so on.

This talk presents learnings from a recent Boehringer Ingelheim trial targeting to prevent Adult Respiratory Distress Syndrom (ARDS) in hospitalized COVID-19 patients. It focuses on a specific output created for the Data Monitoring Committees (DMC) which combined most of the pre-defined efficacy and safety endpoints in an interactive heat-plot. Sample data and code to generate the plot will be provided

Patrick Schloemer (Bayer)

Patrick received his PhD in Statistics from the University of Bremen, Germany, in 2014 for his work on group sequential and adaptive designs for three-arm non-inferiority trials. Since then he has been working at Bayer as a clinical statistician in the cardio-renal area with increasing responsibilities. Currently he acts as the Compound Statistician for a novel treatment for chronic kidney disease in type 2 diabetes that recently received FDA approval.

His methodological interests include group sequential and adaptive designs, multiple comparison procedures and recurrent events. In the past years he has been working on the application for an EMA “Qualification opinion of clinically interpretable treatment effect measures based on recurrent event endpoints that allow for efficient statistical analyses”. Besides this he has been actively involved in the development of the Data Insight Generation (D.I.G) concept at Bayer, which was recently piloted in a large Phase III trial to gain deeper insights into the study data by means of intelligent visualizations.

Data Insight Generation (D.I.G) – A concept to foster interactive and interdisciplinary data investigations using intelligent visualizations
In recent years various data visualization apps have been developed as part of Bayer’s Biostatistics Innovation Center (BIC), with the objective to provide insights that go beyond the classical Tables, Listings and Figures (TLFs) which are the basis for the submission dossier. The Data Insight Generation (D.I.G) concept has been developed to foster and streamline the use of these data visualization apps and position them as a central piece in the interpretation of clinical study data at Bayer. The D.I.G activities culminate in a two-day workshop shortly after TLF delivery in a dedicated, fully-equipped meeting room, where a cross-functional team uses intelligent visualizations and machine learning methods to gain broader and deeper insights into the study data. This talk will give an overview about the D.I.G concept and present first-hand experiences from the pilot D.I.G workshop that was recently held after the close-out of a large Phase III trial.

Charlotta Fruechtenicht (Roche)

Charlotta is a computational biologist by training and works as a senior data scientist in the Data, Analytics & Imaging team in the Pharma Development Personalized Healthcare department at Roche. Her interests lie in using fit-for-purpose analytics (including graphical design) to untap the wealth of multimodal data coming from healthcare care systems in the real world to support the development of new medicines.

Learnings from implementing good graphical principles in a ready-to-use R package
In this talk we will present key learnings from a cross-pharma collaboration tackling the problem of streamlining effective and efficient graphical communication. The application of good graphical principles to the output of statistical analyses, especially in R, can be time consuming and tedious and is thus oftentimes omitted. However, effective visualization is important to enable clear communication between Data Scientists and stakeholders, which makes it crucial to facilitate informed decision making. To enable easy-to-use, multi-purpose visualizations for clinical data, we collaborated across multiple Pharma companies and functions to develop the thoroughly tested R package visR. This new software-package, which is now available on CRAN, enables seamless integration of effective visualization (figures and tables) into analytics and reporting workflows.

Susan Mayo
(FDA)

Susan is a senior mathematical statistician at the Food and Drug Administration, Center for Drug Evaluation’s Office of Biostatistics, with a demonstrated interest and impact in areas that help to make sound regulatory and drug development decisions: graphical design, drug safety and benefit-risk assessment, and the estimand framework. She has been with FDA for over 3 years, and previously worked as an industry statistician and internal company consultant in biotech and big pharma for a few more than that.

Making Impactful Graphs: Looking through the eyes of your audience
Anyone who has graphed data has discovered there is both an art and science to doing it well. Graphs take more effort to create than tables, and when constructed well, they have the potential for their audience to see deeper into the data when there are complexities a table may not be able to address. This talk aims to address some overlooked factors beyond the technical aspects of graphing data. What are the human brain’s visual superpowers? How can a graph be more impactful with its audience? The talk will conclude with some considerations for impactful use of interactive graphics. Susan’s talk will cover:

• Visual perception, and its relationship with statistical graphics design
• Pharma/regulatory interactions –a personal perspective
• Impactful use of interactive graphics

Jeremy Wildfire
(Gilead)

Jeremy Wildfire is a Director of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worked in clinical trial research for 15 years, first as a biostatistician on NIH funded asthma and allergy studies and more recently on cross-functional data science teams focused on creating open source tools that seek to improve the clinical trial analysis pipeline.

Building Open Source Tools for Safety Monitoring: Advancing Research Through Community Collaboration
The Interactive Safety Graphics workstream of the ASA-DIA Biopharm Safety Working Group is excited to introduce version 2 of the safetyGraphics R package. safetyGraphics is an interactive framework for evaluating clinical trial safety in R. Version 2 includes support for multiple data domains, reusable data pipelines and user-defined custom charts. This enhanced framework allows users to easily re-use both static and interactive charts on multiple studies. safetyGraphics includes several interactive graphics by default, including a chart for monitoring drug-induced liver injury that is paired with an in-depth clinical workflow. Charts using existing R packages can also be added to the framework via a straightforward mapping process. To ensure quality and accuracy, the package includes more than 300 automated unit tests and has been vetted through a beta testing process that included feedback from more than 20 clinicians and analysts.

The Interactive Safety Graphics group seeks to modernize clinical trial safety monitoring by building tools for data exploration and reporting in a highly collaborative open source environment. At present, our team includes clinical and technical representatives from the pharmaceutical industry, academia, and the FDA, and additional contributors are always welcome

Sheila Dickinson (Novartis)

Sheila Dickinson is a Global Benefit-Risk Lead, working in the Quantitative Safety and Epidemiology group at Novartis. Her responsibilities include promoting and facilitating the use of a structured benefit-risk approach by Novartis project teams. Sheila is also working on the topic of patient preference studies and is on the management board of the IMI PREFER project, which is working on developing guidelines about when and how to perform patient preference studies to support medical product decision-making.

Sheila holds a degree in mathematics from Imperial College, London and an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine. After joining Novartis in 1997, she worked as a statistician supporting projects in the various disease areas including both diabetes and malaria, before moving to the Quantitative Safety team in 2013.

Points to bear in mind for visual displays of benefit-risk data
Recent years have seen an increased focus on taking a structured approach to the description of benefit-risk in a Clinical Overview. This presentation will cover:

• The regulatory expectations about structured benefit-risk
• How these regulatory expectations influence our approach to visual displays of benefit-risk data
• Suggestions for addressing common issues when displaying benefit-risk data
• Example benefit-risk data display
• Comments on using an R-Shiny App as a tool to create benefit-risk figures

Matthias Trampisch (Boehringer Ingelheim)

Matthias works as a Safety Statistician in independent Safety Analysis Team (iSAT) at Boehringer Ingelheim. iSAT is specialized on analyzing ongoing trial data in an unblinded fashion supporting interim analysis, ad-hoc unblinding requests, or Data Monitoring Committees (DMCs).

Dynamic data visualization for Benefit/Risk Assessment DURING trial conduct - Insights based on DMC output for monitoring a trial in patients hospitalized with COVID-19
Benefit/Risk assessment is a fundamental part of any clinical trial report. However, during trial conduct, the analysis of Benefit/Risk may substantially differ from the final assessment due to various reasons: ongoing recruitment, incomplete/missing data, time points not (yet) available and so on.

This talk presents learnings from a recent Boehringer Ingelheim trial targeting to prevent Adult Respiratory Distress Syndrom (ARDS) in hospitalized COVID-19 patients. It focuses on a specific output created for the Data Monitoring Committees (DMC) which combined most of the pre-defined efficacy and safety endpoints in an interactive heat-plot. Sample data and code to generate the plot will be provided

Patrick Schloemer (Bayer)

Patrick received his PhD in Statistics from the University of Bremen, Germany, in 2014 for his work on group sequential and adaptive designs for three-arm non-inferiority trials. Since then he has been working at Bayer as a clinical statistician in the cardio-renal area with increasing responsibilities. Currently he acts as the Compound Statistician for a novel treatment for chronic kidney disease in type 2 diabetes that recently received FDA approval.

His methodological interests include group sequential and adaptive designs, multiple comparison procedures and recurrent events. In the past years he has been working on the application for an EMA “Qualification opinion of clinically interpretable treatment effect measures based on recurrent event endpoints that allow for efficient statistical analyses”. Besides this he has been actively involved in the development of the Data Insight Generation (D.I.G) concept at Bayer, which was recently piloted in a large Phase III trial to gain deeper insights into the study data by means of intelligent visualizations.

Data Insight Generation (D.I.G) – A concept to foster interactive and interdisciplinary data investigations using intelligent visualizations
In recent years various data visualization apps have been developed as part of Bayer’s Biostatistics Innovation Center (BIC), with the objective to provide insights that go beyond the classical Tables, Listings and Figures (TLFs) which are the basis for the submission dossier. The Data Insight Generation (D.I.G) concept has been developed to foster and streamline the use of these data visualization apps and position them as a central piece in the interpretation of clinical study data at Bayer. The D.I.G activities culminate in a two-day workshop shortly after TLF delivery in a dedicated, fully-equipped meeting room, where a cross-functional team uses intelligent visualizations and machine learning methods to gain broader and deeper insights into the study data. This talk will give an overview about the D.I.G concept and present first-hand experiences from the pilot D.I.G workshop that was recently held after the close-out of a large Phase III trial.

Charlotta Fruechtenicht (Roche)

Charlotta is a computational biologist by training and works as a senior data scientist in the Data, Analytics & Imaging team in the Pharma Development Personalized Healthcare department at Roche. Her interests lie in using fit-for-purpose analytics (including graphical design) to untap the wealth of multimodal data coming from healthcare care systems in the real world to support the development of new medicines.

Learnings from implementing good graphical principles in a ready-to-use R package
In this talk we will present key learnings from a cross-pharma collaboration tackling the problem of streamlining effective and efficient graphical communication. The application of good graphical principles to the output of statistical analyses, especially in R, can be time consuming and tedious and is thus oftentimes omitted. However, effective visualization is important to enable clear communication between Data Scientists and stakeholders, which makes it crucial to facilitate informed decision making. To enable easy-to-use, multi-purpose visualizations for clinical data, we collaborated across multiple Pharma companies and functions to develop the thoroughly tested R package visR. This new software-package, which is now available on CRAN, enables seamless integration of effective visualization (figures and tables) into analytics and reporting workflows.

Susan Mayo
(FDA)

Susan is a senior mathematical statistician at the Food and Drug Administration, Center for Drug Evaluation’s Office of Biostatistics, with a demonstrated interest and impact in areas that help to make sound regulatory and drug development decisions: graphical design, drug safety and benefit-risk assessment, and the estimand framework. She has been with FDA for over 3 years, and previously worked as an industry statistician and internal company consultant in biotech and big pharma for a few more than that.

Making Impactful Graphs: Looking through the eyes of your audience
Anyone who has graphed data has discovered there is both an art and science to doing it well. Graphs take more effort to create than tables, and when constructed well, they have the potential for their audience to see deeper into the data when there are complexities a table may not be able to address. This talk aims to address some overlooked factors beyond the technical aspects of graphing data. What are the human brain’s visual superpowers? How can a graph be more impactful with its audience? The talk will conclude with some considerations for impactful use of interactive graphics. Susan’s talk will cover:

• Visual perception, and its relationship with statistical graphics design
• Pharma/regulatory interactions –a personal perspective
• Impactful use of interactive graphics

Jeremy Wildfire
(Gilead)

Jeremy Wildfire is a Director of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worked in clinical trial research for 15 years, first as a biostatistician on NIH funded asthma and allergy studies and more recently on cross-functional data science teams focused on creating open source tools that seek to improve the clinical trial analysis pipeline.

Building Open Source Tools for Safety Monitoring: Advancing Research Through Community Collaboration
The Interactive Safety Graphics workstream of the ASA-DIA Biopharm Safety Working Group is excited to introduce version 2 of the safetyGraphics R package. safetyGraphics is an interactive framework for evaluating clinical trial safety in R. Version 2 includes support for multiple data domains, reusable data pipelines and user-defined custom charts. This enhanced framework allows users to easily re-use both static and interactive charts on multiple studies. safetyGraphics includes several interactive graphics by default, including a chart for monitoring drug-induced liver injury that is paired with an in-depth clinical workflow. Charts using existing R packages can also be added to the framework via a straightforward mapping process. To ensure quality and accuracy, the package includes more than 300 automated unit tests and has been vetted through a beta testing process that included feedback from more than 20 clinicians and analysts.

The Interactive Safety Graphics group seeks to modernize clinical trial safety monitoring by building tools for data exploration and reporting in a highly collaborative open source environment. At present, our team includes clinical and technical representatives from the pharmaceutical industry, academia, and the FDA, and additional contributors are always welcome

Sheila Dickinson (Novartis)

Sheila Dickinson is a Global Benefit-Risk Lead, working in the Quantitative Safety and Epidemiology group at Novartis. Her responsibilities include promoting and facilitating the use of a structured benefit-risk approach by Novartis project teams. Sheila is also working on the topic of patient preference studies and is on the management board of the IMI PREFER project, which is working on developing guidelines about when and how to perform patient preference studies to support medical product decision-making.

Sheila holds a degree in mathematics from Imperial College, London and an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine. After joining Novartis in 1997, she worked as a statistician supporting projects in the various disease areas including both diabetes and malaria, before moving to the Quantitative Safety team in 2013.

Points to bear in mind for visual displays of benefit-risk data
Recent years have seen an increased focus on taking a structured approach to the description of benefit-risk in a Clinical Overview. This presentation will cover:

• The regulatory expectations about structured benefit-risk
• How these regulatory expectations influence our approach to visual displays of benefit-risk data
• Suggestions for addressing common issues when displaying benefit-risk data
• Example benefit-risk data display
• Comments on using an R-Shiny App as a tool to create benefit-risk figures

Matthias Trampisch (Boehringer Ingelheim)

Matthias works as a Safety Statistician in independent Safety Analysis Team (iSAT) at Boehringer Ingelheim. iSAT is specialized on analyzing ongoing trial data in an unblinded fashion supporting interim analysis, ad-hoc unblinding requests, or Data Monitoring Committees (DMCs).

Dynamic data visualization for Benefit/Risk Assessment DURING trial conduct - Insights based on DMC output for monitoring a trial in patients hospitalized with COVID-19
Benefit/Risk assessment is a fundamental part of any clinical trial report. However, during trial conduct, the analysis of Benefit/Risk may substantially differ from the final assessment due to various reasons: ongoing recruitment, incomplete/missing data, time points not (yet) available and so on.

This talk presents learnings from a recent Boehringer Ingelheim trial targeting to prevent Adult Respiratory Distress Syndrom (ARDS) in hospitalized COVID-19 patients. It focuses on a specific output created for the Data Monitoring Committees (DMC) which combined most of the pre-defined efficacy and safety endpoints in an interactive heat-plot. Sample data and code to generate the plot will be provided

Patrick Schloemer (Bayer)

Patrick received his PhD in Statistics from the University of Bremen, Germany, in 2014 for his work on group sequential and adaptive designs for three-arm non-inferiority trials. Since then he has been working at Bayer as a clinical statistician in the cardio-renal area with increasing responsibilities. Currently he acts as the Compound Statistician for a novel treatment for chronic kidney disease in type 2 diabetes that recently received FDA approval.

His methodological interests include group sequential and adaptive designs, multiple comparison procedures and recurrent events. In the past years he has been working on the application for an EMA “Qualification opinion of clinically interpretable treatment effect measures based on recurrent event endpoints that allow for efficient statistical analyses”. Besides this he has been actively involved in the development of the Data Insight Generation (D.I.G) concept at Bayer, which was recently piloted in a large Phase III trial to gain deeper insights into the study data by means of intelligent visualizations.

Data Insight Generation (D.I.G) – A concept to foster interactive and interdisciplinary data investigations using intelligent visualizations
In recent years various data visualization apps have been developed as part of Bayer’s Biostatistics Innovation Center (BIC), with the objective to provide insights that go beyond the classical Tables, Listings and Figures (TLFs) which are the basis for the submission dossier. The Data Insight Generation (D.I.G) concept has been developed to foster and streamline the use of these data visualization apps and position them as a central piece in the interpretation of clinical study data at Bayer. The D.I.G activities culminate in a two-day workshop shortly after TLF delivery in a dedicated, fully-equipped meeting room, where a cross-functional team uses intelligent visualizations and machine learning methods to gain broader and deeper insights into the study data. This talk will give an overview about the D.I.G concept and present first-hand experiences from the pilot D.I.G workshop that was recently held after the close-out of a large Phase III trial.

Charlotta Fruechtenicht (Roche)

Charlotta is a computational biologist by training and works as a senior data scientist in the Data, Analytics & Imaging team in the Pharma Development Personalized Healthcare department at Roche. Her interests lie in using fit-for-purpose analytics (including graphical design) to untap the wealth of multimodal data coming from healthcare care systems in the real world to support the development of new medicines.

Learnings from implementing good graphical principles in a ready-to-use R package
In this talk we will present key learnings from a cross-pharma collaboration tackling the problem of streamlining effective and efficient graphical communication. The application of good graphical principles to the output of statistical analyses, especially in R, can be time consuming and tedious and is thus oftentimes omitted. However, effective visualization is important to enable clear communication between Data Scientists and stakeholders, which makes it crucial to facilitate informed decision making. To enable easy-to-use, multi-purpose visualizations for clinical data, we collaborated across multiple Pharma companies and functions to develop the thoroughly tested R package visR. This new software-package, which is now available on CRAN, enables seamless integration of effective visualization (figures and tables) into analytics and reporting workflows.

Susan Mayo
(FDA)

Susan is a senior mathematical statistician at the Food and Drug Administration, Center for Drug Evaluation’s Office of Biostatistics, with a demonstrated interest and impact in areas that help to make sound regulatory and drug development decisions: graphical design, drug safety and benefit-risk assessment, and the estimand framework. She has been with FDA for over 3 years, and previously worked as an industry statistician and internal company consultant in biotech and big pharma for a few more than that.

Making Impactful Graphs: Looking through the eyes of your audience
Anyone who has graphed data has discovered there is both an art and science to doing it well. Graphs take more effort to create than tables, and when constructed well, they have the potential for their audience to see deeper into the data when there are complexities a table may not be able to address. This talk aims to address some overlooked factors beyond the technical aspects of graphing data. What are the human brain’s visual superpowers? How can a graph be more impactful with its audience? The talk will conclude with some considerations for impactful use of interactive graphics. Susan’s talk will cover:

• Visual perception, and its relationship with statistical graphics design
• Pharma/regulatory interactions –a personal perspective
• Impactful use of interactive graphics

Jeremy Wildfire
(Gilead)

Jeremy Wildfire is a Director of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worked in clinical trial research for 15 years, first as a biostatistician on NIH funded asthma and allergy studies and more recently on cross-functional data science teams focused on creating open source tools that seek to improve the clinical trial analysis pipeline.

Building Open Source Tools for Safety Monitoring: Advancing Research Through Community Collaboration
The Interactive Safety Graphics workstream of the ASA-DIA Biopharm Safety Working Group is excited to introduce version 2 of the safetyGraphics R package. safetyGraphics is an interactive framework for evaluating clinical trial safety in R. Version 2 includes support for multiple data domains, reusable data pipelines and user-defined custom charts. This enhanced framework allows users to easily re-use both static and interactive charts on multiple studies. safetyGraphics includes several interactive graphics by default, including a chart for monitoring drug-induced liver injury that is paired with an in-depth clinical workflow. Charts using existing R packages can also be added to the framework via a straightforward mapping process. To ensure quality and accuracy, the package includes more than 300 automated unit tests and has been vetted through a beta testing process that included feedback from more than 20 clinicians and analysts.

The Interactive Safety Graphics group seeks to modernize clinical trial safety monitoring by building tools for data exploration and reporting in a highly collaborative open source environment. At present, our team includes clinical and technical representatives from the pharmaceutical industry, academia, and the FDA, and additional contributors are always welcome

Sheila Dickinson (Novartis)

Sheila Dickinson is a Global Benefit-Risk Lead, working in the Quantitative Safety and Epidemiology group at Novartis. Her responsibilities include promoting and facilitating the use of a structured benefit-risk approach by Novartis project teams. Sheila is also working on the topic of patient preference studies and is on the management board of the IMI PREFER project, which is working on developing guidelines about when and how to perform patient preference studies to support medical product decision-making.

Sheila holds a degree in mathematics from Imperial College, London and an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine. After joining Novartis in 1997, she worked as a statistician supporting projects in the various disease areas including both diabetes and malaria, before moving to the Quantitative Safety team in 2013.

Points to bear in mind for visual displays of benefit-risk data
Recent years have seen an increased focus on taking a structured approach to the description of benefit-risk in a Clinical Overview. This presentation will cover:

• The regulatory expectations about structured benefit-risk
• How these regulatory expectations influence our approach to visual displays of benefit-risk data
• Suggestions for addressing common issues when displaying benefit-risk data
• Example benefit-risk data display
• Comments on using an R-Shiny App as a tool to create benefit-risk figures

Matthias Trampisch (Boehringer Ingelheim)

Matthias works as a Safety Statistician in independent Safety Analysis Team (iSAT) at Boehringer Ingelheim. iSAT is specialized on analyzing ongoing trial data in an unblinded fashion supporting interim analysis, ad-hoc unblinding requests, or Data Monitoring Committees (DMCs).

Dynamic data visualization for Benefit/Risk Assessment DURING trial conduct - Insights based on DMC output for monitoring a trial in patients hospitalized with COVID-19
Benefit/Risk assessment is a fundamental part of any clinical trial report. However, during trial conduct, the analysis of Benefit/Risk may substantially differ from the final assessment due to various reasons: ongoing recruitment, incomplete/missing data, time points not (yet) available and so on.

This talk presents learnings from a recent Boehringer Ingelheim trial targeting to prevent Adult Respiratory Distress Syndrom (ARDS) in hospitalized COVID-19 patients. It focuses on a specific output created for the Data Monitoring Committees (DMC) which combined most of the pre-defined efficacy and safety endpoints in an interactive heat-plot. Sample data and code to generate the plot will be provided

Patrick Schloemer (Bayer)

Patrick received his PhD in Statistics from the University of Bremen, Germany, in 2014 for his work on group sequential and adaptive designs for three-arm non-inferiority trials. Since then he has been working at Bayer as a clinical statistician in the cardio-renal area with increasing responsibilities. Currently he acts as the Compound Statistician for a novel treatment for chronic kidney disease in type 2 diabetes that recently received FDA approval.

His methodological interests include group sequential and adaptive designs, multiple comparison procedures and recurrent events. In the past years he has been working on the application for an EMA “Qualification opinion of clinically interpretable treatment effect measures based on recurrent event endpoints that allow for efficient statistical analyses”. Besides this he has been actively involved in the development of the Data Insight Generation (D.I.G) concept at Bayer, which was recently piloted in a large Phase III trial to gain deeper insights into the study data by means of intelligent visualizations.

Data Insight Generation (D.I.G) – A concept to foster interactive and interdisciplinary data investigations using intelligent visualizations
In recent years various data visualization apps have been developed as part of Bayer’s Biostatistics Innovation Center (BIC), with the objective to provide insights that go beyond the classical Tables, Listings and Figures (TLFs) which are the basis for the submission dossier. The Data Insight Generation (D.I.G) concept has been developed to foster and streamline the use of these data visualization apps and position them as a central piece in the interpretation of clinical study data at Bayer. The D.I.G activities culminate in a two-day workshop shortly after TLF delivery in a dedicated, fully-equipped meeting room, where a cross-functional team uses intelligent visualizations and machine learning methods to gain broader and deeper insights into the study data. This talk will give an overview about the D.I.G concept and present first-hand experiences from the pilot D.I.G workshop that was recently held after the close-out of a large Phase III trial.

Charlotta Fruechtenicht (Roche)

Charlotta is a computational biologist by training and works as a senior data scientist in the Data, Analytics & Imaging team in the Pharma Development Personalized Healthcare department at Roche. Her interests lie in using fit-for-purpose analytics (including graphical design) to untap the wealth of multimodal data coming from healthcare care systems in the real world to support the development of new medicines.

Learnings from implementing good graphical principles in a ready-to-use R package
In this talk we will present key learnings from a cross-pharma collaboration tackling the problem of streamlining effective and efficient graphical communication. The application of good graphical principles to the output of statistical analyses, especially in R, can be time consuming and tedious and is thus oftentimes omitted. However, effective visualization is important to enable clear communication between Data Scientists and stakeholders, which makes it crucial to facilitate informed decision making. To enable easy-to-use, multi-purpose visualizations for clinical data, we collaborated across multiple Pharma companies and functions to develop the thoroughly tested R package visR. This new software-package, which is now available on CRAN, enables seamless integration of effective visualization (figures and tables) into analytics and reporting workflows.

Susan Mayo
(FDA)

Susan is a senior mathematical statistician at the Food and Drug Administration, Center for Drug Evaluation’s Office of Biostatistics, with a demonstrated interest and impact in areas that help to make sound regulatory and drug development decisions: graphical design, drug safety and benefit-risk assessment, and the estimand framework. She has been with FDA for over 3 years, and previously worked as an industry statistician and internal company consultant in biotech and big pharma for a few more than that.

Making Impactful Graphs: Looking through the eyes of your audience
Anyone who has graphed data has discovered there is both an art and science to doing it well. Graphs take more effort to create than tables, and when constructed well, they have the potential for their audience to see deeper into the data when there are complexities a table may not be able to address. This talk aims to address some overlooked factors beyond the technical aspects of graphing data. What are the human brain’s visual superpowers? How can a graph be more impactful with its audience? The talk will conclude with some considerations for impactful use of interactive graphics. Susan’s talk will cover:

• Visual perception, and its relationship with statistical graphics design
• Pharma/regulatory interactions –a personal perspective
• Impactful use of interactive graphics

Jeremy Wildfire
(Gilead)

Jeremy Wildfire is a Director of Biostatistics on the Advance Analytics team at Gilead. Jeremy has worked in clinical trial research for 15 years, first as a biostatistician on NIH funded asthma and allergy studies and more recently on cross-functional data science teams focused on creating open source tools that seek to improve the clinical trial analysis pipeline.

Building Open Source Tools for Safety Monitoring: Advancing Research Through Community Collaboration
The Interactive Safety Graphics workstream of the ASA-DIA Biopharm Safety Working Group is excited to introduce version 2 of the safetyGraphics R package. safetyGraphics is an interactive framework for evaluating clinical trial safety in R. Version 2 includes support for multiple data domains, reusable data pipelines and user-defined custom charts. This enhanced framework allows users to easily re-use both static and interactive charts on multiple studies. safetyGraphics includes several interactive graphics by default, including a chart for monitoring drug-induced liver injury that is paired with an in-depth clinical workflow. Charts using existing R packages can also be added to the framework via a straightforward mapping process. To ensure quality and accuracy, the package includes more than 300 automated unit tests and has been vetted through a beta testing process that included feedback from more than 20 clinicians and analysts.

The Interactive Safety Graphics group seeks to modernize clinical trial safety monitoring by building tools for data exploration and reporting in a highly collaborative open source environment. At present, our team includes clinical and technical representatives from the pharmaceutical industry, academia, and the FDA, and additional contributors are always welcome

Sheila Dickinson (Novartis)

Sheila Dickinson is a Global Benefit-Risk Lead, working in the Quantitative Safety and Epidemiology group at Novartis. Her responsibilities include promoting and facilitating the use of a structured benefit-risk approach by Novartis project teams. Sheila is also working on the topic of patient preference studies and is on the management board of the IMI PREFER project, which is working on developing guidelines about when and how to perform patient preference studies to support medical product decision-making.

Sheila holds a degree in mathematics from Imperial College, London and an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine. After joining Novartis in 1997, she worked as a statistician supporting projects in the various disease areas including both diabetes and malaria, before moving to the Quantitative Safety team in 2013.

Points to bear in mind for visual displays of benefit-risk data
Recent years have seen an increased focus on taking a structured approach to the description of benefit-risk in a Clinical Overview. This presentation will cover:

• The regulatory expectations about structured benefit-risk
• How these regulatory expectations influence our approach to visual displays of benefit-risk data
• Suggestions for addressing common issues when displaying benefit-risk data
• Example benefit-risk data display
• Comments on using an R-Shiny App as a tool to create benefit-risk figures

Matthias Trampisch (Boehringer Ingelheim)

Matthias works as a Safety Statistician in independent Safety Analysis Team (iSAT) at Boehringer Ingelheim. iSAT is specialized on analyzing ongoing trial data in an unblinded fashion supporting interim analysis, ad-hoc unblinding requests, or Data Monitoring Committees (DMCs).

Dynamic data visualization for Benefit/Risk Assessment DURING trial conduct - Insights based on DMC output for monitoring a trial in patients hospitalized with COVID-19
Benefit/Risk assessment is a fundamental part of any clinical trial report. However, during trial conduct, the analysis of Benefit/Risk may substantially differ from the final assessment due to various reasons: ongoing recruitment, incomplete/missing data, time points not (yet) available and so on.

This talk presents learnings from a recent Boehringer Ingelheim trial targeting to prevent Adult Respiratory Distress Syndrom (ARDS) in hospitalized COVID-19 patients. It focuses on a specific output created for the Data Monitoring Committees (DMC) which combined most of the pre-defined efficacy and safety endpoints in an interactive heat-plot. Sample data and code to generate the plot will be provided

Patrick Schloemer (Bayer)

Patrick received his PhD in Statistics from the University of Bremen, Germany, in 2014 for his work on group sequential and adaptive designs for three-arm non-inferiority trials. Since then he has been working at Bayer as a clinical statistician in the cardio-renal area with increasing responsibilities. Currently he acts as the Compound Statistician for a novel treatment for chronic kidney disease in type 2 diabetes that recently received FDA approval.

His methodological interests include group sequential and adaptive designs, multiple comparison procedures and recurrent events. In the past years he has been working on the application for an EMA “Qualification opinion of clinically interpretable treatment effect measures based on recurrent event endpoints that allow for efficient statistical analyses”. Besides this he has been actively involved in the development of the Data Insight Generation (D.I.G) concept at Bayer, which was recently piloted in a large Phase III trial to gain deeper insights into the study data by means of intelligent visualizations.

Data Insight Generation (D.I.G) – A concept to foster interactive and interdisciplinary data investigations using intelligent visualizations
In recent years various data visualization apps have been developed as part of Bayer’s Biostatistics Innovation Center (BIC), with the objective to provide insights that go beyond the classical Tables, Listings and Figures (TLFs) which are the basis for the submission dossier. The Data Insight Generation (D.I.G) concept has been developed to foster and streamline the use of these data visualization apps and position them as a central piece in the interpretation of clinical study data at Bayer. The D.I.G activities culminate in a two-day workshop shortly after TLF delivery in a dedicated, fully-equipped meeting room, where a cross-functional team uses intelligent visualizations and machine learning methods to gain broader and deeper insights into the study data. This talk will give an overview about the D.I.G concept and present first-hand experiences from the pilot D.I.G workshop that was recently held after the close-out of a large Phase III trial.

Charlotta Fruechtenicht (Roche)

Charlotta is a computational biologist by training and works as a senior data scientist in the Data, Analytics & Imaging team in the Pharma Development Personalized Healthcare department at Roche. Her interests lie in using fit-for-purpose analytics (including graphical design) to untap the wealth of multimodal data coming from healthcare care systems in the real world to support the development of new medicines.

Learnings from implementing good graphical principles in a ready-to-use R package
In this talk we will present key learnings from a cross-pharma collaboration tackling the problem of streamlining effective and efficient graphical communication. The application of good graphical principles to the output of statistical analyses, especially in R, can be time consuming and tedious and is thus oftentimes omitted. However, effective visualization is important to enable clear communication between Data Scientists and stakeholders, which makes it crucial to facilitate informed decision making. To enable easy-to-use, multi-purpose visualizations for clinical data, we collaborated across multiple Pharma companies and functions to develop the thoroughly tested R package visR. This new software-package, which is now available on CRAN, enables seamless integration of effective visualization (figures and tables) into analytics and reporting workflows.