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

PSI Introduction to Industry Training (ITIT) Course - 2025/2026

An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.

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.

Urgent Meeting: Medical Statistician Apprenticeship Scheme

The UK government have recently announced that level 7 apprenticeships must be fully funded by the employer from January 2026, for any apprentice over the age of 21. With funding for MSc's at an all time low, and universities courses facing closures, the apprenticeship scheme remains as important as ever, as a tool to encourage new statisticians into our industry. In this dedicated meeting, Valerie Millar (chair of the apprenticeship scheme) will provide full updates on the government changes and seek feedback and ideas from employers, universities and apprentices on how to keep this scheme successfully running for many years to come.

PSI Webinar: Methodology and first results of the iRISE (improving Reproducibility In SciencE) consortium

This 1-hour webinar will be an opportunity to hear about the methodology and first results of the iRISE consortium. iRISE is working towards a better understanding of reproducibility and the interventions that work to improve it. At the end of the presentation there will also be the opportunity to ask questions.

One-day Event: Change Management for Moving to R/Open-Source

This one-day event focuses on the comprehensive management of transitioning to R/Open-Source, addressing the challenges and providing actionable insights. Attendees will participate in sessions covering essential topics such as training best practices, creating strategic plans, making the case to senior management, and managing both statistical and programming aspects of the transition.

PSI Vaccines SIG Webinar: Challenges in Estimating Vaccine Effectiveness Against Progression to Severe Disease

This is a free webinar on Challenges in Estimating Vaccine Effectiveness Against Progression to Severe Disease.

PSI Book Club - The Art of Explanation: How to Communicate with Clarity and Confidence

Develop your non-technical skills by reading The Art of Explanation by Ros Atkins and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply skills from the book in-between sessions.

PSI Training Course: Pediatric Extrapolation

This course is aimed at biostatisticians with no or some pediatric drug development experience who are interested to further their understanding. We will give you an introduction to the pediatric drug development landscape. This will include identifying the key regulations and processes governing pediatric development, a discussion on the needs and challenges when conducting pediatric research and a focus on the ways to overcome these challenges from a statistical perspective.

PSI Career Young Virtual Event (Q3 2025)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

EFSPI/PSI Causal Inference SIG Webinar: Instrumental Variable Methods

The webinar is targeted at statisticians working in the pharmaceutical industry, and the objective is to 1) provide a basic understanding of IV methodology including how it relates to causal inference, and 2) present two inspirational pharma-relevant applications.

PSI Career Young Virtual Event (Q4 2025)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

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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