Date: Tuesday 19th November 2024
Time: 14:00-15:30 GMT | 15:00-16:30 CET
Location: Online via Zoom
Speakers: Kaspar Rufibach, Susan Gruber, Florian Lasch
Who is this event intended for? Applied statisticians, and people genuinely interested in applying state-of-the-art statistical methodology.
What is the benefit of attending? Increased understanding and insights in causal inference principles and methodology.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
In this webinar, three speakers will share their perspective on the using of causal inference methodology in the analysis of RCT data. The audience will be presented with ideas and opportunities on why and how to apply causal inference principles / techniques in their work. And more importantly how causal approaches can help evaluating evidence for answers to causal-by-nature scientific questions.
First, Kaspar Rufibach (Merck) will share his perspectives on opportunities to apply causal methods. Next, Susan Gruber (TL revolution) will discuss targeted learning as a framework to address causal questions and the importance of sensitivity analyses. Finally, Florian Lasch (EMA) will discuss both the importance of the causal inference angle in determining estimands, and will discuss a case study.
The webinar will end with a panel discussion.
|
Speaker |
Biography |
Abstract |
|
|
Kaspar Rufibach is a biostatistician who is passionate about supporting statisticians and drug developers to continuously challenge the status quo, with the aim of improving the drug development process, making it more efficient, and enabling access. More on the oncology estimand WG: http://www.oncoestimand.org More on the EFSPI statistical methodology leaders group: https://efspieurope.github.io/efspi/methods/methods_intro.html More on Kaspar: http://www.kasparrufibach.ch |
I will start with providing a few examples of very valid scientific questions in drug development that typically ask for causal answers, but which are routinely answered in ad-hoc ways that rarely allow for a causal interpretation. Further reasons why I believe a clinical biostatistician needs to know about causal inference will be given. I will conclude with a call to apply and develop statistical and causal inference methodology to fill the gap between valid causal questions and routine ad-hoc answers.
|
|
|
Susan Gruber, co-founder of TL Revolution and Founder of Putnam Data Sciences, is a biostatistician and computer scientist specializing in causal inference and predictive modeling. Her work focuses on improving methods and tools for generating robust real-world evidence to support biopharmaceutical and medical decision-making through Targeted Learning. Her tmle R package on CRAN has over 70,000 downloads worldwide. |
Targeted Learning is a framework that combines causal inference, statistics, and machine learning to address complex issues in analyzing data from randomized controlled trials and studies that incorporate real-world data. This talk provides a high-level introduction to the Targeted Learning Estimation Roadmap, statistical analysis using Targeted Maximum Likelihood Estimation (TMLE), and the role of sensitivity analysis to assess the level of support for drawing a substantive conclusion from the study findings. |
|
|
Florian is a Biostatistician with a degree in mathematics and a PhD from Hannover Medical School. Florian works as a Biostatistics Specialist at the European Medicines Agency, providing scientific support to development and evaluation throughout all stages of marketing authorisation assessments of medicinal products, and leads the ACT EU Priority Action on Clinical Trial Methodologies and the EMA Estimands Implementation Group. |
The estimands framework facilitates the application of thinking and methodology developed in the causal inference community to the design and analysis of clinical trials. This presentation will reflect on the opportunities and challenges of applying causal inference methodology to clinical trials. A case study in Alzheimer’s Disease where the intercurrent event ‘initiation of symptomatic medication’ is handled with a hypothetical strategy will illustrate the key points. |
Date: Tuesday 19th November 2024
Time: 14:00-15:30 GMT | 15:00-16:30 CET
Location: Online via Zoom
Speakers: Kaspar Rufibach, Susan Gruber, Florian Lasch
Who is this event intended for? Applied statisticians, and people genuinely interested in applying state-of-the-art statistical methodology.
What is the benefit of attending? Increased understanding and insights in causal inference principles and methodology.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
In this webinar, three speakers will share their perspective on the using of causal inference methodology in the analysis of RCT data. The audience will be presented with ideas and opportunities on why and how to apply causal inference principles / techniques in their work. And more importantly how causal approaches can help evaluating evidence for answers to causal-by-nature scientific questions.
First, Kaspar Rufibach (Merck) will share his perspectives on opportunities to apply causal methods. Next, Susan Gruber (TL revolution) will discuss targeted learning as a framework to address causal questions and the importance of sensitivity analyses. Finally, Florian Lasch (EMA) will discuss both the importance of the causal inference angle in determining estimands, and will discuss a case study.
The webinar will end with a panel discussion.
|
Speaker |
Biography |
Abstract |
|
|
Kaspar Rufibach is a biostatistician who is passionate about supporting statisticians and drug developers to continuously challenge the status quo, with the aim of improving the drug development process, making it more efficient, and enabling access. More on the oncology estimand WG: http://www.oncoestimand.org More on the EFSPI statistical methodology leaders group: https://efspieurope.github.io/efspi/methods/methods_intro.html More on Kaspar: http://www.kasparrufibach.ch |
I will start with providing a few examples of very valid scientific questions in drug development that typically ask for causal answers, but which are routinely answered in ad-hoc ways that rarely allow for a causal interpretation. Further reasons why I believe a clinical biostatistician needs to know about causal inference will be given. I will conclude with a call to apply and develop statistical and causal inference methodology to fill the gap between valid causal questions and routine ad-hoc answers.
|
|
|
Susan Gruber, co-founder of TL Revolution and Founder of Putnam Data Sciences, is a biostatistician and computer scientist specializing in causal inference and predictive modeling. Her work focuses on improving methods and tools for generating robust real-world evidence to support biopharmaceutical and medical decision-making through Targeted Learning. Her tmle R package on CRAN has over 70,000 downloads worldwide. |
Targeted Learning is a framework that combines causal inference, statistics, and machine learning to address complex issues in analyzing data from randomized controlled trials and studies that incorporate real-world data. This talk provides a high-level introduction to the Targeted Learning Estimation Roadmap, statistical analysis using Targeted Maximum Likelihood Estimation (TMLE), and the role of sensitivity analysis to assess the level of support for drawing a substantive conclusion from the study findings. |
|
|
Florian is a Biostatistician with a degree in mathematics and a PhD from Hannover Medical School. Florian works as a Biostatistics Specialist at the European Medicines Agency, providing scientific support to development and evaluation throughout all stages of marketing authorisation assessments of medicinal products, and leads the ACT EU Priority Action on Clinical Trial Methodologies and the EMA Estimands Implementation Group. |
The estimands framework facilitates the application of thinking and methodology developed in the causal inference community to the design and analysis of clinical trials. This presentation will reflect on the opportunities and challenges of applying causal inference methodology to clinical trials. A case study in Alzheimer’s Disease where the intercurrent event ‘initiation of symptomatic medication’ is handled with a hypothetical strategy will illustrate the key points. |
Date: Tuesday 19th November 2024
Time: 14:00-15:30 GMT | 15:00-16:30 CET
Location: Online via Zoom
Speakers: Kaspar Rufibach, Susan Gruber, Florian Lasch
Who is this event intended for? Applied statisticians, and people genuinely interested in applying state-of-the-art statistical methodology.
What is the benefit of attending? Increased understanding and insights in causal inference principles and methodology.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
In this webinar, three speakers will share their perspective on the using of causal inference methodology in the analysis of RCT data. The audience will be presented with ideas and opportunities on why and how to apply causal inference principles / techniques in their work. And more importantly how causal approaches can help evaluating evidence for answers to causal-by-nature scientific questions.
First, Kaspar Rufibach (Merck) will share his perspectives on opportunities to apply causal methods. Next, Susan Gruber (TL revolution) will discuss targeted learning as a framework to address causal questions and the importance of sensitivity analyses. Finally, Florian Lasch (EMA) will discuss both the importance of the causal inference angle in determining estimands, and will discuss a case study.
The webinar will end with a panel discussion.
|
Speaker |
Biography |
Abstract |
|
|
Kaspar Rufibach is a biostatistician who is passionate about supporting statisticians and drug developers to continuously challenge the status quo, with the aim of improving the drug development process, making it more efficient, and enabling access. More on the oncology estimand WG: http://www.oncoestimand.org More on the EFSPI statistical methodology leaders group: https://efspieurope.github.io/efspi/methods/methods_intro.html More on Kaspar: http://www.kasparrufibach.ch |
I will start with providing a few examples of very valid scientific questions in drug development that typically ask for causal answers, but which are routinely answered in ad-hoc ways that rarely allow for a causal interpretation. Further reasons why I believe a clinical biostatistician needs to know about causal inference will be given. I will conclude with a call to apply and develop statistical and causal inference methodology to fill the gap between valid causal questions and routine ad-hoc answers.
|
|
|
Susan Gruber, co-founder of TL Revolution and Founder of Putnam Data Sciences, is a biostatistician and computer scientist specializing in causal inference and predictive modeling. Her work focuses on improving methods and tools for generating robust real-world evidence to support biopharmaceutical and medical decision-making through Targeted Learning. Her tmle R package on CRAN has over 70,000 downloads worldwide. |
Targeted Learning is a framework that combines causal inference, statistics, and machine learning to address complex issues in analyzing data from randomized controlled trials and studies that incorporate real-world data. This talk provides a high-level introduction to the Targeted Learning Estimation Roadmap, statistical analysis using Targeted Maximum Likelihood Estimation (TMLE), and the role of sensitivity analysis to assess the level of support for drawing a substantive conclusion from the study findings. |
|
|
Florian is a Biostatistician with a degree in mathematics and a PhD from Hannover Medical School. Florian works as a Biostatistics Specialist at the European Medicines Agency, providing scientific support to development and evaluation throughout all stages of marketing authorisation assessments of medicinal products, and leads the ACT EU Priority Action on Clinical Trial Methodologies and the EMA Estimands Implementation Group. |
The estimands framework facilitates the application of thinking and methodology developed in the causal inference community to the design and analysis of clinical trials. This presentation will reflect on the opportunities and challenges of applying causal inference methodology to clinical trials. A case study in Alzheimer’s Disease where the intercurrent event ‘initiation of symptomatic medication’ is handled with a hypothetical strategy will illustrate the key points. |
Date: Tuesday 19th November 2024
Time: 14:00-15:30 GMT | 15:00-16:30 CET
Location: Online via Zoom
Speakers: Kaspar Rufibach, Susan Gruber, Florian Lasch
Who is this event intended for? Applied statisticians, and people genuinely interested in applying state-of-the-art statistical methodology.
What is the benefit of attending? Increased understanding and insights in causal inference principles and methodology.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
In this webinar, three speakers will share their perspective on the using of causal inference methodology in the analysis of RCT data. The audience will be presented with ideas and opportunities on why and how to apply causal inference principles / techniques in their work. And more importantly how causal approaches can help evaluating evidence for answers to causal-by-nature scientific questions.
First, Kaspar Rufibach (Merck) will share his perspectives on opportunities to apply causal methods. Next, Susan Gruber (TL revolution) will discuss targeted learning as a framework to address causal questions and the importance of sensitivity analyses. Finally, Florian Lasch (EMA) will discuss both the importance of the causal inference angle in determining estimands, and will discuss a case study.
The webinar will end with a panel discussion.
|
Speaker |
Biography |
Abstract |
|
|
Kaspar Rufibach is a biostatistician who is passionate about supporting statisticians and drug developers to continuously challenge the status quo, with the aim of improving the drug development process, making it more efficient, and enabling access. More on the oncology estimand WG: http://www.oncoestimand.org More on the EFSPI statistical methodology leaders group: https://efspieurope.github.io/efspi/methods/methods_intro.html More on Kaspar: http://www.kasparrufibach.ch |
I will start with providing a few examples of very valid scientific questions in drug development that typically ask for causal answers, but which are routinely answered in ad-hoc ways that rarely allow for a causal interpretation. Further reasons why I believe a clinical biostatistician needs to know about causal inference will be given. I will conclude with a call to apply and develop statistical and causal inference methodology to fill the gap between valid causal questions and routine ad-hoc answers.
|
|
|
Susan Gruber, co-founder of TL Revolution and Founder of Putnam Data Sciences, is a biostatistician and computer scientist specializing in causal inference and predictive modeling. Her work focuses on improving methods and tools for generating robust real-world evidence to support biopharmaceutical and medical decision-making through Targeted Learning. Her tmle R package on CRAN has over 70,000 downloads worldwide. |
Targeted Learning is a framework that combines causal inference, statistics, and machine learning to address complex issues in analyzing data from randomized controlled trials and studies that incorporate real-world data. This talk provides a high-level introduction to the Targeted Learning Estimation Roadmap, statistical analysis using Targeted Maximum Likelihood Estimation (TMLE), and the role of sensitivity analysis to assess the level of support for drawing a substantive conclusion from the study findings. |
|
|
Florian is a Biostatistician with a degree in mathematics and a PhD from Hannover Medical School. Florian works as a Biostatistics Specialist at the European Medicines Agency, providing scientific support to development and evaluation throughout all stages of marketing authorisation assessments of medicinal products, and leads the ACT EU Priority Action on Clinical Trial Methodologies and the EMA Estimands Implementation Group. |
The estimands framework facilitates the application of thinking and methodology developed in the causal inference community to the design and analysis of clinical trials. This presentation will reflect on the opportunities and challenges of applying causal inference methodology to clinical trials. A case study in Alzheimer’s Disease where the intercurrent event ‘initiation of symptomatic medication’ is handled with a hypothetical strategy will illustrate the key points. |
Date: Tuesday 19th November 2024
Time: 14:00-15:30 GMT | 15:00-16:30 CET
Location: Online via Zoom
Speakers: Kaspar Rufibach, Susan Gruber, Florian Lasch
Who is this event intended for? Applied statisticians, and people genuinely interested in applying state-of-the-art statistical methodology.
What is the benefit of attending? Increased understanding and insights in causal inference principles and methodology.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
In this webinar, three speakers will share their perspective on the using of causal inference methodology in the analysis of RCT data. The audience will be presented with ideas and opportunities on why and how to apply causal inference principles / techniques in their work. And more importantly how causal approaches can help evaluating evidence for answers to causal-by-nature scientific questions.
First, Kaspar Rufibach (Merck) will share his perspectives on opportunities to apply causal methods. Next, Susan Gruber (TL revolution) will discuss targeted learning as a framework to address causal questions and the importance of sensitivity analyses. Finally, Florian Lasch (EMA) will discuss both the importance of the causal inference angle in determining estimands, and will discuss a case study.
The webinar will end with a panel discussion.
|
Speaker |
Biography |
Abstract |
|
|
Kaspar Rufibach is a biostatistician who is passionate about supporting statisticians and drug developers to continuously challenge the status quo, with the aim of improving the drug development process, making it more efficient, and enabling access. More on the oncology estimand WG: http://www.oncoestimand.org More on the EFSPI statistical methodology leaders group: https://efspieurope.github.io/efspi/methods/methods_intro.html More on Kaspar: http://www.kasparrufibach.ch |
I will start with providing a few examples of very valid scientific questions in drug development that typically ask for causal answers, but which are routinely answered in ad-hoc ways that rarely allow for a causal interpretation. Further reasons why I believe a clinical biostatistician needs to know about causal inference will be given. I will conclude with a call to apply and develop statistical and causal inference methodology to fill the gap between valid causal questions and routine ad-hoc answers.
|
|
|
Susan Gruber, co-founder of TL Revolution and Founder of Putnam Data Sciences, is a biostatistician and computer scientist specializing in causal inference and predictive modeling. Her work focuses on improving methods and tools for generating robust real-world evidence to support biopharmaceutical and medical decision-making through Targeted Learning. Her tmle R package on CRAN has over 70,000 downloads worldwide. |
Targeted Learning is a framework that combines causal inference, statistics, and machine learning to address complex issues in analyzing data from randomized controlled trials and studies that incorporate real-world data. This talk provides a high-level introduction to the Targeted Learning Estimation Roadmap, statistical analysis using Targeted Maximum Likelihood Estimation (TMLE), and the role of sensitivity analysis to assess the level of support for drawing a substantive conclusion from the study findings. |
|
|
Florian is a Biostatistician with a degree in mathematics and a PhD from Hannover Medical School. Florian works as a Biostatistics Specialist at the European Medicines Agency, providing scientific support to development and evaluation throughout all stages of marketing authorisation assessments of medicinal products, and leads the ACT EU Priority Action on Clinical Trial Methodologies and the EMA Estimands Implementation Group. |
The estimands framework facilitates the application of thinking and methodology developed in the causal inference community to the design and analysis of clinical trials. This presentation will reflect on the opportunities and challenges of applying causal inference methodology to clinical trials. A case study in Alzheimer’s Disease where the intercurrent event ‘initiation of symptomatic medication’ is handled with a hypothetical strategy will illustrate the key points. |
Date: Tuesday 19th November 2024
Time: 14:00-15:30 GMT | 15:00-16:30 CET
Location: Online via Zoom
Speakers: Kaspar Rufibach, Susan Gruber, Florian Lasch
Who is this event intended for? Applied statisticians, and people genuinely interested in applying state-of-the-art statistical methodology.
What is the benefit of attending? Increased understanding and insights in causal inference principles and methodology.
This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.
In this webinar, three speakers will share their perspective on the using of causal inference methodology in the analysis of RCT data. The audience will be presented with ideas and opportunities on why and how to apply causal inference principles / techniques in their work. And more importantly how causal approaches can help evaluating evidence for answers to causal-by-nature scientific questions.
First, Kaspar Rufibach (Merck) will share his perspectives on opportunities to apply causal methods. Next, Susan Gruber (TL revolution) will discuss targeted learning as a framework to address causal questions and the importance of sensitivity analyses. Finally, Florian Lasch (EMA) will discuss both the importance of the causal inference angle in determining estimands, and will discuss a case study.
The webinar will end with a panel discussion.
|
Speaker |
Biography |
Abstract |
|
|
Kaspar Rufibach is a biostatistician who is passionate about supporting statisticians and drug developers to continuously challenge the status quo, with the aim of improving the drug development process, making it more efficient, and enabling access. More on the oncology estimand WG: http://www.oncoestimand.org More on the EFSPI statistical methodology leaders group: https://efspieurope.github.io/efspi/methods/methods_intro.html More on Kaspar: http://www.kasparrufibach.ch |
I will start with providing a few examples of very valid scientific questions in drug development that typically ask for causal answers, but which are routinely answered in ad-hoc ways that rarely allow for a causal interpretation. Further reasons why I believe a clinical biostatistician needs to know about causal inference will be given. I will conclude with a call to apply and develop statistical and causal inference methodology to fill the gap between valid causal questions and routine ad-hoc answers.
|
|
|
Susan Gruber, co-founder of TL Revolution and Founder of Putnam Data Sciences, is a biostatistician and computer scientist specializing in causal inference and predictive modeling. Her work focuses on improving methods and tools for generating robust real-world evidence to support biopharmaceutical and medical decision-making through Targeted Learning. Her tmle R package on CRAN has over 70,000 downloads worldwide. |
Targeted Learning is a framework that combines causal inference, statistics, and machine learning to address complex issues in analyzing data from randomized controlled trials and studies that incorporate real-world data. This talk provides a high-level introduction to the Targeted Learning Estimation Roadmap, statistical analysis using Targeted Maximum Likelihood Estimation (TMLE), and the role of sensitivity analysis to assess the level of support for drawing a substantive conclusion from the study findings. |
|
|
Florian is a Biostatistician with a degree in mathematics and a PhD from Hannover Medical School. Florian works as a Biostatistics Specialist at the European Medicines Agency, providing scientific support to development and evaluation throughout all stages of marketing authorisation assessments of medicinal products, and leads the ACT EU Priority Action on Clinical Trial Methodologies and the EMA Estimands Implementation Group. |
The estimands framework facilitates the application of thinking and methodology developed in the causal inference community to the design and analysis of clinical trials. This presentation will reflect on the opportunities and challenges of applying causal inference methodology to clinical trials. A case study in Alzheimer’s Disease where the intercurrent event ‘initiation of symptomatic medication’ is handled with a hypothetical strategy will illustrate the key points. |
.png?sfvrsn=e7ecafdb_0&MaxWidth=200&MaxHeight=200&ScaleUp=false&Quality=High&Method=ResizeFitToAreaArguments&Signature=0A8FE0E627EB292A3E804E12BFD8E6DF "kaspar1 (002)")
