Date: Thursday 25th April 2024 Time: 15:00-17:00 BST Location: Online via Zoom Speakers: Dr. Erin Gabriel (University Of Copenhagen) and Dr. Melanie Prague (INRIA Bordeaux Sud-Oest Center).
Who is this event intended for? Statisticians, Mathematicians, Modelers, working in vaccine development. What is the benefit of attending? Attending this seminar on Correlates of Protection (CoPs) in vaccine development offers several benefits. Firstly, participants will gain insights into statistical models used to identify and validate CoPs, providing them with quantitative tools for assessing vaccine efficacy. Secondly, they will learn about mechanistic models that elucidate the underlying biological mechanisms driving immune responses, enhancing their understanding of vaccine-induced protection.
Cost
This event is free of charge to both Members of PSI and Non-Members.
This webinar will explore two different approaches to Correlates of Protection in vaccine development. Statistical models providing a quantitative framework for identification and validation of potential CoPs, and mechanistic models aiming to understand the biological mechanisms driving immune responses and protection. By integrating these two approaches, researchers can gain better insights into the immunological factors associated with vaccine efficacy and infection protection. This webinar will include talks from two renowned speakers who will discuss the principles, applications, and challenges of both statistical and mechanistic models, contributing to a better understanding of CoPs and optimizing vaccine clinical trial design.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
Erin Gabriel received her PhD from the University of Washington in 2012. She is an Associate Professor in Biostatistics at the University of Copenhagen. Her research focuses on biostatistical methods development and the proper application of methods to problems in the treatment and prevention of infectious diseases. She is currently working on methodological research in the areas of nonparametric causal bounds, designs, and estimation methods for emulated and randomized clinical trials for the evaluation of prediction-based decision rules, and surrogate evaluation. Her general statistical areas of interest are causal inference and randomized trials.
Statistical evaluation of correlates of protection: reasonable assumptions are the key.
There are a variety of statistical methods for assessing correlates of protection (CoP), the majority of which rely on strong assumptions. I will review the statistical methods for correlates of protection starting with Gilbert and Hudgens (2008), followed by Huang et al's semi-parametric methods, and then my own work which are all under the principal stratification framework. Current methods have moved beyond principal stratification to the controlled vaccine efficacy (VE) framework, which was used to validate CoP for the COVID-19 vaccines. The no unmeasured confounding assumption is needed under the controlled VE evaluation of correlates of protection, as it is an estimand similar to a mediation effect. It could be argued that the controlled VE framework is more robust as the no unmeasured confounding assumption is more common, accessible, and it can be more easily scrutinized by subject-matter experts. There are also more well-developed sensitivity analysis methods for that setting, one of which is nonparametric causal bounds. I will talk quickly about my paper in which we derive bounds for mediation effects, motivated by vaccine efficacy evaluation, and consider how they might be used for controlled vaccine efficacy effects.
Dr. Melanie Prague
Melanie Prague is a permanent researcher at Inria (University of Bordeaux, France) in the SISTM team (Statistics in Immunology and translational medicine) since October 2016. Since 2013, she holds a PhD in Biostatistics and Public Health from the University of Bordeaux, France. She also was a postdoctoral fellow during almost three years at Harvard School of Public Health (Boston, USA). Her research focuses on the development of statistical methods for treatment and prevention of infectious diseases. She develops both within-host and between-host models to accelerate the development of treatments and vaccines. Her main fundings are centered around applications on HIV, Ebola, Nipah and COVID-19
Definition of mechanistic correlates of protection for vaccine development.
Model informed drug development has potentially a great deal to offer for vaccine development and in particular in seeking methods to extrapolate effectiveness. Viral dynamics modeling to define mechanistic correlates of protection for vaccine development.
The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. The complete chains of causality and interrelationships between vaccination, immune responses, protection and clinical endpoints are likely to be considerably complex. In this work, we propose a model-based approach for identifying mechanistic correlates of protection against disease acquisition based on mathematical modeling of viral dynamics and data mining of immunological markers. We apply the method to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect.
Scientific Meetings
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
Date: Thursday 25th April 2024 Time: 15:00-17:00 BST Location: Online via Zoom Speakers: Dr. Erin Gabriel (University Of Copenhagen) and Dr. Melanie Prague (INRIA Bordeaux Sud-Oest Center).
Who is this event intended for? Statisticians, Mathematicians, Modelers, working in vaccine development. What is the benefit of attending? Attending this seminar on Correlates of Protection (CoPs) in vaccine development offers several benefits. Firstly, participants will gain insights into statistical models used to identify and validate CoPs, providing them with quantitative tools for assessing vaccine efficacy. Secondly, they will learn about mechanistic models that elucidate the underlying biological mechanisms driving immune responses, enhancing their understanding of vaccine-induced protection.
Cost
This event is free of charge to both Members of PSI and Non-Members.
This webinar will explore two different approaches to Correlates of Protection in vaccine development. Statistical models providing a quantitative framework for identification and validation of potential CoPs, and mechanistic models aiming to understand the biological mechanisms driving immune responses and protection. By integrating these two approaches, researchers can gain better insights into the immunological factors associated with vaccine efficacy and infection protection. This webinar will include talks from two renowned speakers who will discuss the principles, applications, and challenges of both statistical and mechanistic models, contributing to a better understanding of CoPs and optimizing vaccine clinical trial design.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
Erin Gabriel received her PhD from the University of Washington in 2012. She is an Associate Professor in Biostatistics at the University of Copenhagen. Her research focuses on biostatistical methods development and the proper application of methods to problems in the treatment and prevention of infectious diseases. She is currently working on methodological research in the areas of nonparametric causal bounds, designs, and estimation methods for emulated and randomized clinical trials for the evaluation of prediction-based decision rules, and surrogate evaluation. Her general statistical areas of interest are causal inference and randomized trials.
Statistical evaluation of correlates of protection: reasonable assumptions are the key.
There are a variety of statistical methods for assessing correlates of protection (CoP), the majority of which rely on strong assumptions. I will review the statistical methods for correlates of protection starting with Gilbert and Hudgens (2008), followed by Huang et al's semi-parametric methods, and then my own work which are all under the principal stratification framework. Current methods have moved beyond principal stratification to the controlled vaccine efficacy (VE) framework, which was used to validate CoP for the COVID-19 vaccines. The no unmeasured confounding assumption is needed under the controlled VE evaluation of correlates of protection, as it is an estimand similar to a mediation effect. It could be argued that the controlled VE framework is more robust as the no unmeasured confounding assumption is more common, accessible, and it can be more easily scrutinized by subject-matter experts. There are also more well-developed sensitivity analysis methods for that setting, one of which is nonparametric causal bounds. I will talk quickly about my paper in which we derive bounds for mediation effects, motivated by vaccine efficacy evaluation, and consider how they might be used for controlled vaccine efficacy effects.
Dr. Melanie Prague
Melanie Prague is a permanent researcher at Inria (University of Bordeaux, France) in the SISTM team (Statistics in Immunology and translational medicine) since October 2016. Since 2013, she holds a PhD in Biostatistics and Public Health from the University of Bordeaux, France. She also was a postdoctoral fellow during almost three years at Harvard School of Public Health (Boston, USA). Her research focuses on the development of statistical methods for treatment and prevention of infectious diseases. She develops both within-host and between-host models to accelerate the development of treatments and vaccines. Her main fundings are centered around applications on HIV, Ebola, Nipah and COVID-19
Definition of mechanistic correlates of protection for vaccine development.
Model informed drug development has potentially a great deal to offer for vaccine development and in particular in seeking methods to extrapolate effectiveness. Viral dynamics modeling to define mechanistic correlates of protection for vaccine development.
The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. The complete chains of causality and interrelationships between vaccination, immune responses, protection and clinical endpoints are likely to be considerably complex. In this work, we propose a model-based approach for identifying mechanistic correlates of protection against disease acquisition based on mathematical modeling of viral dynamics and data mining of immunological markers. We apply the method to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect.
Training Courses
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
Date: Thursday 25th April 2024 Time: 15:00-17:00 BST Location: Online via Zoom Speakers: Dr. Erin Gabriel (University Of Copenhagen) and Dr. Melanie Prague (INRIA Bordeaux Sud-Oest Center).
Who is this event intended for? Statisticians, Mathematicians, Modelers, working in vaccine development. What is the benefit of attending? Attending this seminar on Correlates of Protection (CoPs) in vaccine development offers several benefits. Firstly, participants will gain insights into statistical models used to identify and validate CoPs, providing them with quantitative tools for assessing vaccine efficacy. Secondly, they will learn about mechanistic models that elucidate the underlying biological mechanisms driving immune responses, enhancing their understanding of vaccine-induced protection.
Cost
This event is free of charge to both Members of PSI and Non-Members.
This webinar will explore two different approaches to Correlates of Protection in vaccine development. Statistical models providing a quantitative framework for identification and validation of potential CoPs, and mechanistic models aiming to understand the biological mechanisms driving immune responses and protection. By integrating these two approaches, researchers can gain better insights into the immunological factors associated with vaccine efficacy and infection protection. This webinar will include talks from two renowned speakers who will discuss the principles, applications, and challenges of both statistical and mechanistic models, contributing to a better understanding of CoPs and optimizing vaccine clinical trial design.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
Erin Gabriel received her PhD from the University of Washington in 2012. She is an Associate Professor in Biostatistics at the University of Copenhagen. Her research focuses on biostatistical methods development and the proper application of methods to problems in the treatment and prevention of infectious diseases. She is currently working on methodological research in the areas of nonparametric causal bounds, designs, and estimation methods for emulated and randomized clinical trials for the evaluation of prediction-based decision rules, and surrogate evaluation. Her general statistical areas of interest are causal inference and randomized trials.
Statistical evaluation of correlates of protection: reasonable assumptions are the key.
There are a variety of statistical methods for assessing correlates of protection (CoP), the majority of which rely on strong assumptions. I will review the statistical methods for correlates of protection starting with Gilbert and Hudgens (2008), followed by Huang et al's semi-parametric methods, and then my own work which are all under the principal stratification framework. Current methods have moved beyond principal stratification to the controlled vaccine efficacy (VE) framework, which was used to validate CoP for the COVID-19 vaccines. The no unmeasured confounding assumption is needed under the controlled VE evaluation of correlates of protection, as it is an estimand similar to a mediation effect. It could be argued that the controlled VE framework is more robust as the no unmeasured confounding assumption is more common, accessible, and it can be more easily scrutinized by subject-matter experts. There are also more well-developed sensitivity analysis methods for that setting, one of which is nonparametric causal bounds. I will talk quickly about my paper in which we derive bounds for mediation effects, motivated by vaccine efficacy evaluation, and consider how they might be used for controlled vaccine efficacy effects.
Dr. Melanie Prague
Melanie Prague is a permanent researcher at Inria (University of Bordeaux, France) in the SISTM team (Statistics in Immunology and translational medicine) since October 2016. Since 2013, she holds a PhD in Biostatistics and Public Health from the University of Bordeaux, France. She also was a postdoctoral fellow during almost three years at Harvard School of Public Health (Boston, USA). Her research focuses on the development of statistical methods for treatment and prevention of infectious diseases. She develops both within-host and between-host models to accelerate the development of treatments and vaccines. Her main fundings are centered around applications on HIV, Ebola, Nipah and COVID-19
Definition of mechanistic correlates of protection for vaccine development.
Model informed drug development has potentially a great deal to offer for vaccine development and in particular in seeking methods to extrapolate effectiveness. Viral dynamics modeling to define mechanistic correlates of protection for vaccine development.
The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. The complete chains of causality and interrelationships between vaccination, immune responses, protection and clinical endpoints are likely to be considerably complex. In this work, we propose a model-based approach for identifying mechanistic correlates of protection against disease acquisition based on mathematical modeling of viral dynamics and data mining of immunological markers. We apply the method to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect.
Journal Club
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
Date: Thursday 25th April 2024 Time: 15:00-17:00 BST Location: Online via Zoom Speakers: Dr. Erin Gabriel (University Of Copenhagen) and Dr. Melanie Prague (INRIA Bordeaux Sud-Oest Center).
Who is this event intended for? Statisticians, Mathematicians, Modelers, working in vaccine development. What is the benefit of attending? Attending this seminar on Correlates of Protection (CoPs) in vaccine development offers several benefits. Firstly, participants will gain insights into statistical models used to identify and validate CoPs, providing them with quantitative tools for assessing vaccine efficacy. Secondly, they will learn about mechanistic models that elucidate the underlying biological mechanisms driving immune responses, enhancing their understanding of vaccine-induced protection.
Cost
This event is free of charge to both Members of PSI and Non-Members.
This webinar will explore two different approaches to Correlates of Protection in vaccine development. Statistical models providing a quantitative framework for identification and validation of potential CoPs, and mechanistic models aiming to understand the biological mechanisms driving immune responses and protection. By integrating these two approaches, researchers can gain better insights into the immunological factors associated with vaccine efficacy and infection protection. This webinar will include talks from two renowned speakers who will discuss the principles, applications, and challenges of both statistical and mechanistic models, contributing to a better understanding of CoPs and optimizing vaccine clinical trial design.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
Erin Gabriel received her PhD from the University of Washington in 2012. She is an Associate Professor in Biostatistics at the University of Copenhagen. Her research focuses on biostatistical methods development and the proper application of methods to problems in the treatment and prevention of infectious diseases. She is currently working on methodological research in the areas of nonparametric causal bounds, designs, and estimation methods for emulated and randomized clinical trials for the evaluation of prediction-based decision rules, and surrogate evaluation. Her general statistical areas of interest are causal inference and randomized trials.
Statistical evaluation of correlates of protection: reasonable assumptions are the key.
There are a variety of statistical methods for assessing correlates of protection (CoP), the majority of which rely on strong assumptions. I will review the statistical methods for correlates of protection starting with Gilbert and Hudgens (2008), followed by Huang et al's semi-parametric methods, and then my own work which are all under the principal stratification framework. Current methods have moved beyond principal stratification to the controlled vaccine efficacy (VE) framework, which was used to validate CoP for the COVID-19 vaccines. The no unmeasured confounding assumption is needed under the controlled VE evaluation of correlates of protection, as it is an estimand similar to a mediation effect. It could be argued that the controlled VE framework is more robust as the no unmeasured confounding assumption is more common, accessible, and it can be more easily scrutinized by subject-matter experts. There are also more well-developed sensitivity analysis methods for that setting, one of which is nonparametric causal bounds. I will talk quickly about my paper in which we derive bounds for mediation effects, motivated by vaccine efficacy evaluation, and consider how they might be used for controlled vaccine efficacy effects.
Dr. Melanie Prague
Melanie Prague is a permanent researcher at Inria (University of Bordeaux, France) in the SISTM team (Statistics in Immunology and translational medicine) since October 2016. Since 2013, she holds a PhD in Biostatistics and Public Health from the University of Bordeaux, France. She also was a postdoctoral fellow during almost three years at Harvard School of Public Health (Boston, USA). Her research focuses on the development of statistical methods for treatment and prevention of infectious diseases. She develops both within-host and between-host models to accelerate the development of treatments and vaccines. Her main fundings are centered around applications on HIV, Ebola, Nipah and COVID-19
Definition of mechanistic correlates of protection for vaccine development.
Model informed drug development has potentially a great deal to offer for vaccine development and in particular in seeking methods to extrapolate effectiveness. Viral dynamics modeling to define mechanistic correlates of protection for vaccine development.
The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. The complete chains of causality and interrelationships between vaccination, immune responses, protection and clinical endpoints are likely to be considerably complex. In this work, we propose a model-based approach for identifying mechanistic correlates of protection against disease acquisition based on mathematical modeling of viral dynamics and data mining of immunological markers. We apply the method to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect.
Webinars
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
Date: Thursday 25th April 2024 Time: 15:00-17:00 BST Location: Online via Zoom Speakers: Dr. Erin Gabriel (University Of Copenhagen) and Dr. Melanie Prague (INRIA Bordeaux Sud-Oest Center).
Who is this event intended for? Statisticians, Mathematicians, Modelers, working in vaccine development. What is the benefit of attending? Attending this seminar on Correlates of Protection (CoPs) in vaccine development offers several benefits. Firstly, participants will gain insights into statistical models used to identify and validate CoPs, providing them with quantitative tools for assessing vaccine efficacy. Secondly, they will learn about mechanistic models that elucidate the underlying biological mechanisms driving immune responses, enhancing their understanding of vaccine-induced protection.
Cost
This event is free of charge to both Members of PSI and Non-Members.
This webinar will explore two different approaches to Correlates of Protection in vaccine development. Statistical models providing a quantitative framework for identification and validation of potential CoPs, and mechanistic models aiming to understand the biological mechanisms driving immune responses and protection. By integrating these two approaches, researchers can gain better insights into the immunological factors associated with vaccine efficacy and infection protection. This webinar will include talks from two renowned speakers who will discuss the principles, applications, and challenges of both statistical and mechanistic models, contributing to a better understanding of CoPs and optimizing vaccine clinical trial design.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
Erin Gabriel received her PhD from the University of Washington in 2012. She is an Associate Professor in Biostatistics at the University of Copenhagen. Her research focuses on biostatistical methods development and the proper application of methods to problems in the treatment and prevention of infectious diseases. She is currently working on methodological research in the areas of nonparametric causal bounds, designs, and estimation methods for emulated and randomized clinical trials for the evaluation of prediction-based decision rules, and surrogate evaluation. Her general statistical areas of interest are causal inference and randomized trials.
Statistical evaluation of correlates of protection: reasonable assumptions are the key.
There are a variety of statistical methods for assessing correlates of protection (CoP), the majority of which rely on strong assumptions. I will review the statistical methods for correlates of protection starting with Gilbert and Hudgens (2008), followed by Huang et al's semi-parametric methods, and then my own work which are all under the principal stratification framework. Current methods have moved beyond principal stratification to the controlled vaccine efficacy (VE) framework, which was used to validate CoP for the COVID-19 vaccines. The no unmeasured confounding assumption is needed under the controlled VE evaluation of correlates of protection, as it is an estimand similar to a mediation effect. It could be argued that the controlled VE framework is more robust as the no unmeasured confounding assumption is more common, accessible, and it can be more easily scrutinized by subject-matter experts. There are also more well-developed sensitivity analysis methods for that setting, one of which is nonparametric causal bounds. I will talk quickly about my paper in which we derive bounds for mediation effects, motivated by vaccine efficacy evaluation, and consider how they might be used for controlled vaccine efficacy effects.
Dr. Melanie Prague
Melanie Prague is a permanent researcher at Inria (University of Bordeaux, France) in the SISTM team (Statistics in Immunology and translational medicine) since October 2016. Since 2013, she holds a PhD in Biostatistics and Public Health from the University of Bordeaux, France. She also was a postdoctoral fellow during almost three years at Harvard School of Public Health (Boston, USA). Her research focuses on the development of statistical methods for treatment and prevention of infectious diseases. She develops both within-host and between-host models to accelerate the development of treatments and vaccines. Her main fundings are centered around applications on HIV, Ebola, Nipah and COVID-19
Definition of mechanistic correlates of protection for vaccine development.
Model informed drug development has potentially a great deal to offer for vaccine development and in particular in seeking methods to extrapolate effectiveness. Viral dynamics modeling to define mechanistic correlates of protection for vaccine development.
The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. The complete chains of causality and interrelationships between vaccination, immune responses, protection and clinical endpoints are likely to be considerably complex. In this work, we propose a model-based approach for identifying mechanistic correlates of protection against disease acquisition based on mathematical modeling of viral dynamics and data mining of immunological markers. We apply the method to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect.
Careers Meetings
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
Date: Thursday 25th April 2024 Time: 15:00-17:00 BST Location: Online via Zoom Speakers: Dr. Erin Gabriel (University Of Copenhagen) and Dr. Melanie Prague (INRIA Bordeaux Sud-Oest Center).
Who is this event intended for? Statisticians, Mathematicians, Modelers, working in vaccine development. What is the benefit of attending? Attending this seminar on Correlates of Protection (CoPs) in vaccine development offers several benefits. Firstly, participants will gain insights into statistical models used to identify and validate CoPs, providing them with quantitative tools for assessing vaccine efficacy. Secondly, they will learn about mechanistic models that elucidate the underlying biological mechanisms driving immune responses, enhancing their understanding of vaccine-induced protection.
Cost
This event is free of charge to both Members of PSI and Non-Members.
This webinar will explore two different approaches to Correlates of Protection in vaccine development. Statistical models providing a quantitative framework for identification and validation of potential CoPs, and mechanistic models aiming to understand the biological mechanisms driving immune responses and protection. By integrating these two approaches, researchers can gain better insights into the immunological factors associated with vaccine efficacy and infection protection. This webinar will include talks from two renowned speakers who will discuss the principles, applications, and challenges of both statistical and mechanistic models, contributing to a better understanding of CoPs and optimizing vaccine clinical trial design.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
Erin Gabriel received her PhD from the University of Washington in 2012. She is an Associate Professor in Biostatistics at the University of Copenhagen. Her research focuses on biostatistical methods development and the proper application of methods to problems in the treatment and prevention of infectious diseases. She is currently working on methodological research in the areas of nonparametric causal bounds, designs, and estimation methods for emulated and randomized clinical trials for the evaluation of prediction-based decision rules, and surrogate evaluation. Her general statistical areas of interest are causal inference and randomized trials.
Statistical evaluation of correlates of protection: reasonable assumptions are the key.
There are a variety of statistical methods for assessing correlates of protection (CoP), the majority of which rely on strong assumptions. I will review the statistical methods for correlates of protection starting with Gilbert and Hudgens (2008), followed by Huang et al's semi-parametric methods, and then my own work which are all under the principal stratification framework. Current methods have moved beyond principal stratification to the controlled vaccine efficacy (VE) framework, which was used to validate CoP for the COVID-19 vaccines. The no unmeasured confounding assumption is needed under the controlled VE evaluation of correlates of protection, as it is an estimand similar to a mediation effect. It could be argued that the controlled VE framework is more robust as the no unmeasured confounding assumption is more common, accessible, and it can be more easily scrutinized by subject-matter experts. There are also more well-developed sensitivity analysis methods for that setting, one of which is nonparametric causal bounds. I will talk quickly about my paper in which we derive bounds for mediation effects, motivated by vaccine efficacy evaluation, and consider how they might be used for controlled vaccine efficacy effects.
Dr. Melanie Prague
Melanie Prague is a permanent researcher at Inria (University of Bordeaux, France) in the SISTM team (Statistics in Immunology and translational medicine) since October 2016. Since 2013, she holds a PhD in Biostatistics and Public Health from the University of Bordeaux, France. She also was a postdoctoral fellow during almost three years at Harvard School of Public Health (Boston, USA). Her research focuses on the development of statistical methods for treatment and prevention of infectious diseases. She develops both within-host and between-host models to accelerate the development of treatments and vaccines. Her main fundings are centered around applications on HIV, Ebola, Nipah and COVID-19
Definition of mechanistic correlates of protection for vaccine development.
Model informed drug development has potentially a great deal to offer for vaccine development and in particular in seeking methods to extrapolate effectiveness. Viral dynamics modeling to define mechanistic correlates of protection for vaccine development.
The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. The complete chains of causality and interrelationships between vaccination, immune responses, protection and clinical endpoints are likely to be considerably complex. In this work, we propose a model-based approach for identifying mechanistic correlates of protection against disease acquisition based on mathematical modeling of viral dynamics and data mining of immunological markers. We apply the method to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect.
Upcoming Events
PSI Mentoring 2025
Date: Ongoing 6 month cycle beginning late April/early May 2024
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 Training Course: Mixed Models and Repeated Measures
This course is presented through lectures and practical sessions using SAS code. It is suitable for statisticians working on clinical trials, who already have a good understanding of linear and generalised linear models.
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.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This is an interactive online training workshop providing an in-depth review of the estimand framework as laid out by ICH E9(R1) addendum with inputs from estimand experts, case studies, quizzes and opportunity for discussions. You will develop an estimand in a therapeutic area of interest to your company. In an online break-out room, you will join a series of team discussions to implement the estimand framework in a case study, aligning estimands, design, conduct, analysis, (assumptions + sensitivity analyses) to the clinical objective and therapeutic setting.
Maths Meets Medicine: Exploring Careers in the Pharmaceutical Industry
This session will showcase how careers in pharmaceutical statistics can be both rewarding and impactful, with a focus on how mathematics is integral to the development of medicines. Students will hear from industry experts, explore diverse career paths, and learn why continuing to study math is key to unlocking exciting opportunities in the healthcare sector.
Dissolution Testing: Time for Statistical (r)Evolution
Webinar dedicated to the topic of dissolution of oral solid dosage forms; opportunity to hear from statisticians working in the CMC field, with open question and answers.
In addition, the CMC Statistical Network Europe special interest group will discuss advocacy opportunities, have your say to contribute to the future direction.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
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