Date: Tuesday 18th November 2025 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Davit Sargsyan
Who is this event intended for?: Statisticians and Scientists involved or interested in randomization techniques in pre-clinical and early clinical studies.
What is the benefit of attending?: Hearing about modern randomization methods.
Cost
This webinar is free to both Members of PSI and Non-Members.
In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Speaker details
Speaker
Biography
Abstract
Davit Sargsyan, Johnson & Johnson
Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.
Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Scientific Meetings
Pre-Clinical SIG Webinar: Modern Algorithms for Animal Randomization in Preclinical Studies
Date: Tuesday 18th November 2025 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Davit Sargsyan
Who is this event intended for?: Statisticians and Scientists involved or interested in randomization techniques in pre-clinical and early clinical studies.
What is the benefit of attending?: Hearing about modern randomization methods.
Cost
This webinar is free to both Members of PSI and Non-Members.
In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Speaker details
Speaker
Biography
Abstract
Davit Sargsyan, Johnson & Johnson
Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.
Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Training Courses
Pre-Clinical SIG Webinar: Modern Algorithms for Animal Randomization in Preclinical Studies
Date: Tuesday 18th November 2025 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Davit Sargsyan
Who is this event intended for?: Statisticians and Scientists involved or interested in randomization techniques in pre-clinical and early clinical studies.
What is the benefit of attending?: Hearing about modern randomization methods.
Cost
This webinar is free to both Members of PSI and Non-Members.
In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Speaker details
Speaker
Biography
Abstract
Davit Sargsyan, Johnson & Johnson
Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.
Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Journal Club
Pre-Clinical SIG Webinar: Modern Algorithms for Animal Randomization in Preclinical Studies
Date: Tuesday 18th November 2025 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Davit Sargsyan
Who is this event intended for?: Statisticians and Scientists involved or interested in randomization techniques in pre-clinical and early clinical studies.
What is the benefit of attending?: Hearing about modern randomization methods.
Cost
This webinar is free to both Members of PSI and Non-Members.
In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Speaker details
Speaker
Biography
Abstract
Davit Sargsyan, Johnson & Johnson
Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.
Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Webinars
Pre-Clinical SIG Webinar: Modern Algorithms for Animal Randomization in Preclinical Studies
Date: Tuesday 18th November 2025 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Davit Sargsyan
Who is this event intended for?: Statisticians and Scientists involved or interested in randomization techniques in pre-clinical and early clinical studies.
What is the benefit of attending?: Hearing about modern randomization methods.
Cost
This webinar is free to both Members of PSI and Non-Members.
In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Speaker details
Speaker
Biography
Abstract
Davit Sargsyan, Johnson & Johnson
Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.
Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Careers Meetings
Pre-Clinical SIG Webinar: Modern Algorithms for Animal Randomization in Preclinical Studies
Date: Tuesday 18th November 2025 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Davit Sargsyan
Who is this event intended for?: Statisticians and Scientists involved or interested in randomization techniques in pre-clinical and early clinical studies.
What is the benefit of attending?: Hearing about modern randomization methods.
Cost
This webinar is free to both Members of PSI and Non-Members.
In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Speaker details
Speaker
Biography
Abstract
Davit Sargsyan, Johnson & Johnson
Davit Sargsyan is an associate director in a nonclinical statistics group at J&J Innovative Medicine, mainly supporting Immunology Discover R&D. He received an MS in statistics and completed his PhD in pharmaceutical sciences at Rutgers University. To date, Davit has co-published over 70 scientific articles in peer-reviewed journals on topics ranging from clinical outcomes of cardiovascular patients using a state-wide hospital admission data registry to natural compounds testing in in vivo and in vitro models. Davit’s research at Rutgers School of Pharmacy concentrated on computational methods and visualization of omics data studying the effect of dietary phytochemicals on epigenome, transcriptome and microbiome.
Modern Algorithms for Animal Randomization in Preclinical Studies In both, clinical and preclinical studies randomizing subjects to treatment groups is a key part of the study design. This step minimizes the risk of confounding. For large studies, complete randomization is often enough to ensure that treatment groups are similar. However, for small clinical trials and especially for in vivo studies this approach is riskier. More elaborate randomization techniques can be applied for those studies such as randomized block design. However, the best results are achieved when algorithmic approach to subject allocation is taken. One such approach is based on genetic algorithm that is rooted in Theory of Evolution. The methodology minimizes the fitness function criteria for partitions of a dataset into balanced subgroups. The performance of the algorithm was compared to random allocation and the exhaustive search using synthetic and real-world data. The results showed that experimental groups created by this algorithm were more homogeneous compared those created by exhaustive search. Additionally, this algorithm is significantly less expensive computationally compared to the exhaustive search, and the efficiency gains increase rapidly as the number of subjects and design factors increase. We will discuss the theory behind this approach as well as the extensions and variations of the algorithm such as simulated annealing.
Upcoming Events
PSI Book Club: Change: How organisations achieve hard-to-imagine results in uncertain and volatile times
Organizations have to adapt to the transforming landscape of our industry to ensure they continue to be successful in the future. Many of us are feeling the impact of organizational change. By reading John P Kotter’s book we can understand about organizational change and learn how to thrive, rather than just survive, through change.
Change, by John P Kotter (and his team), is a summary of all that he has learned over his decades of research and leading change. His book describes why many current approaches to change are inadequate and explains why new solutions need to give people a voice and a role in a new, change-embracing organization.
Develop your understanding of organisational change and become empowered to be part of your organisation’s change, by reading Change by John P Kotter and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply knowledge from the book in-between sessions.
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.
PSI Training Course: Propensity Scores: Practical Application in Non-randomised Studies
The course will introduce the topic of propensity scores and the use of external data. Covering the topics of matching and weighting as well as more advance topics of high dimension propensity scores, multi-valued treatments, double robustness and time-varying scenarios. There will be the opportunity to participate in some hands on practical exercises in R.
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.
Making Estimands Work - Practical Approaches with Implementation in Data Standards
This event will discuss the impact of the estimand framework on data flow and data handling for analysis datasets (ADaM). What are the considerations to produce data sets suitable for estimation of estimands and traceability of the occurrence of intercurrent events. This webinar will include tips for best practice, based on the White paper Implementation of ICH E9(R1) Estimands Framework using Data Standards.
PSI/EFSPI Biomarker SIG Webinar: Statistical Methods for ctDNA Data
This webinar will describe what ctDNA is and what the key challenges are for analysing it. We will then summarise and review methods that were published over the last few years for analysing ctDNA data.
The morning will be dedicated to networking opportunities, helping attendees build connections with peers and professionals. In the afternoon, participants can attend a series of talks and workshops, including a career panel featuring speakers from both academia and industry, offering insights into various career paths. There will also be a statistical workshop, as well as the opportunity to learn about and debate some of the hot topics in the world of medical statistics. To wrap up the day, we’re exploring interest in an informal after-work networking event for those keen to continue the conversation.
Joint PSI/EFSPI, Phuse, ASA Safety Working Group Webinar Series: Overview of safety analysis and implementation process of safety surveillance in ongoing studies
The session will have three speakers and a discussant. This session will be the first in a series of webinars around safety in clinical trials. The series is co-organized by PSI, ASA-Biopharm safety working group and PHUSE.
This two-afternoon virtual course provides a practical introduction to adaptive clinical trials, focusing on the concepts, applications, and regulatory principles outlined in ICH E20 through real-world examples and case studies.
PSI Vaccines SIG Webinar: Statistical Immune Correlate Analysis of Hemagglutination Inhibition Titers for an mRNA-based Influenza Vaccine
This 1-hour webinar is designed for statisticians with an interest in vaccine development, to learn about the recent application of statistical immune correlate analysis in vaccine development. Specifically, an application of such analyses to evaluate hemagglutination inhibition titers for an influenza vaccine
PSI Careers - MEDMathS: Medicine Empowered by Data, Maths and Statistics
Date: Wednesday 4th November 2026
A careers talk about medical statistics and how it plays a crucial role in developing new medicines. Learn about the field of medical statistics and how it plays a crucial role in developing groundbreaking new medicines, vaccines and healthcare products.
Date: 18 November 2026
This is an excellent opportunity for students to find out more about the field of medical statistics, talk to people from different organisations and make contacts for the future. All students currently studying for a mathematics or statistics-related BSc, MSc or PhD are invited to attend, and we welcome interest from exhibitors too.
Aligning Estimation with the Clinical Question of Interest
This webinar will focus on aligning estimation to estimands. How do different strategies for handling intercurrent events and missing data handling influence the choice of estimation method, with illustration using a case study.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
PSI Careers Coaching Series: Peer Support Initiative
This 10-month peer support programme is aimed at professionals with five or more years of experience who are seeking structured development and connection with peers at a similar career stage. Through 5 bi-monthly topics, participants will engage in coach-led presentations, small-group discussions, and regular progress reviews focused on achieving tangible outcomes.
In this vital role you will generate evidence to meet Health Technology Assessment (HTA) requirements, while ensuring that the analyses performed meet required standards and are robust and valid.
Bristol Myers Squibb - Director, Statistical Methodology and Innovation
Lead the development of innovative statistical methods, provides expert consulting, oversees tools and software, and mentors team members while collaborating cross-functionally to address complex drug development challenges.
Phastar – Associate Director, Statistics – Real World Evidence (RWE)
As an Associate Director, Statistics (RWE) you will focus on supporting one of our key pharmaceutical clients within our FSP function. This will be a hands-on technical role, with oversight responsibilities and cross-functional exposure.
Phastar – Principal Statistician – Real World Evidence (RWE)
We are seeking an experienced Statistician to join our FSP team to support one of our fantastic and well-known global pharmaceutical clients, at Principal level.
We are seeking an experienced Statistician to join our FSP team to support one of our fantastic and well-known global pharmaceutical clients, at either Senior or Principal levels.
This position is deal for a statistician who values ownership, collaboration, and using data to enable confident development decisions and to support regulatory submissions.
