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

Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars

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

AIMS SIG - Open-Source Lunch Bites

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

PSI Mentoring 2026

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

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

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

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

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

Speaker	Biography	Abstract
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.

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.

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.

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.

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.

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