Statisticians in the Age of AI: On Route to Strategic Partnership

PSI Introduction to Industry Training (ITIT) Course - 2026/2027

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.

AIMS SIG - Open-Source Lunch Bites

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, “Graphics Basics,” will introduce the fundamentals of producing graphics using the ggplot2 package.

Integrating large- to high-dimension data in mechanistic model: Application to modeling response to vaccination?

This is a webinar on mechanistic modelling of vaccine responses with Large- and High-Dimensional Data.

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.

Enhancing Clinical Study Reporting with the Estimand Framework

Join us for an insightful webinar where we explore practical strategies for applying the estimand framework in clinical study reporting. Drawing on real-world experiences and case studies, we will share recommendations to help you: • Understand the role of estimands in improving transparency and interpretation of trial results. • Navigate common challenges in implementing the framework during reporting. • Apply best practices to enhance regulatory submissions, webposting in public registries (clinicaltrials.gov/CTIS), and scientific publications. Whether you are involved in clinical trial design, data analysis, or regulatory submissions, this session will provide actionable guidance to realize the full potential of the estimand framework.

PSI Book Club: The Art of Explanation - Podcast

The Book Club session will discuss a podcast episode where the host of the Power Hour, Adrienne Herbert, chats with Ros about his book, and the secrets that he learned from years of working in high-pressure newsrooms, and the ten elements of a good explanation and the seven steps you need to take to express yourself with clarity and impact.

PSI Career Young Virtual Event (Q2 2026)

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 Book Club: The AI Con – Joint with ASA Book Club

The Guardian described the authors of this book as refreshingly sarcastic! What is sold to us as AI, they announce, is just "a bill of goods": "A few major well-placed players are poised to accumulate significant wealth by extracting value from other people's creative work, personal data, or labour, and replacing quality services with artificial facsimiles."

PSI Book Club: Another Door Opens – Book Club Special Event

This is a Book Club Special Event in response to the changes in our industry and as a supportive move to create community and connection for those navigating redundancy and uncertainty. Read the book in advance of the book club session then join the zoom call to discuss ideas. There will be breakout groups to connect with others, exchange experiences of how the book has helped, and offer support.

PSI Career Young Virtual Event (Q3 2026)

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 Career Young Virtual Event (Q4 2026)

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
Tina Lang, PhD Bayer Pharmaceuticals AG	Tina Lang studied statistics at TU Dortmund where she also earned her PhD. She joined Bayer in 2009 as a preclinical statistician, supporting in vitro and in vivo experiments across the indication portfolio. Additionally, she is highly invested in elevating statistical literacy within the company and leads several training initiatives. In 2024 she transferred to the department of Data Science & Artificial Intelligence, where she has more insights into AI tools and their use within the drug development process.	Quality in Preclinical Research - The Importance of Being Human Preclinical experiments form the empirical foundation of translational medicine by assessing the feasibility, safety, and efficacy of new therapeutic approaches. Yet, unlike the highly regulated standards of clinical trials, preclinical research often exhibits substantial methodological heterogeneity, leading to concerns about reproducibility, bias, and the robustness of conclusions. These challenges are further compounded by the emerging use of artificial intelligence (AI). While AI has the potential to increase efficiency and support data analysis, uncontrolled or poorly understood applications can amplify existing weaknesses in study quality. In this presentation, we discuss the critical importance of human judgment, statistical rigor, and transparent study design for ensuring reliable preclinical evidence. We examine key dimensions of analytical quality across study design, data generation, and evaluation, and consider how AI - particularly large language models (LLMs) and foundation models - can support, rather than undermine, methodological integrity. From a biometrical perspective, safeguards for uncertainty quantification, error control, and interpretability are essential when integrating AI into preclinical research. Ultimately, methodological progress depends on a careful balance between human expertise, statistical inference, and computational tools. Illustrative examples highlight both opportunities and pitfalls at the interface of biostatistics, data science, and translational medicine, emphasizing that maintaining human oversight is crucial for generating reproducible and interpretable evidence.
Christian Schmid, PhD F. Hoffmann – La Roche AG	Christian Schmid is a Senior Statistical Scientist at Roche and a founding member of the CMC Statistical Network Europe, where he has served on the steering committee since its inception. In his role, he primarily supports Roche’s commercial manufacturing and has recently expanded his work to include several AI validation initiatives. He earned his PhD in Statistics from The Pennsylvania State University, with a specialization in computational methods.	Validating GenAI (Generative Artificial IntelligenceI) in GxP (Good x Practice) environments requires moving beyond traditional "exact match" testing to a framework built on statistical confidence and risk management This presentation proposes a validation framework for GenAI applications, where rigorous testing strategies are used to quantify the reliability of non-deterministic outputs. We will explore technical controls for risk mitigation, specifically the use of Retrieval-Augmented Generation (RAG) architectures and parameter adjustments, such as temperature and top-p, to control output variability. The session will cover specific methods for combining automated scoring with structured Subject Matter Expert (SME) review to evaluate correctness and detect hallucinations, ensuring that probabilistic systems meet the safety standards required for regulated healthcare applications.

Speaker	Biography	Abstract
Tina Lang, PhD Bayer Pharmaceuticals AG	Tina Lang studied statistics at TU Dortmund where she also earned her PhD. She joined Bayer in 2009 as a preclinical statistician, supporting in vitro and in vivo experiments across the indication portfolio. Additionally, she is highly invested in elevating statistical literacy within the company and leads several training initiatives. In 2024 she transferred to the department of Data Science & Artificial Intelligence, where she has more insights into AI tools and their use within the drug development process.	Quality in Preclinical Research - The Importance of Being Human Preclinical experiments form the empirical foundation of translational medicine by assessing the feasibility, safety, and efficacy of new therapeutic approaches. Yet, unlike the highly regulated standards of clinical trials, preclinical research often exhibits substantial methodological heterogeneity, leading to concerns about reproducibility, bias, and the robustness of conclusions. These challenges are further compounded by the emerging use of artificial intelligence (AI). While AI has the potential to increase efficiency and support data analysis, uncontrolled or poorly understood applications can amplify existing weaknesses in study quality. In this presentation, we discuss the critical importance of human judgment, statistical rigor, and transparent study design for ensuring reliable preclinical evidence. We examine key dimensions of analytical quality across study design, data generation, and evaluation, and consider how AI - particularly large language models (LLMs) and foundation models - can support, rather than undermine, methodological integrity. From a biometrical perspective, safeguards for uncertainty quantification, error control, and interpretability are essential when integrating AI into preclinical research. Ultimately, methodological progress depends on a careful balance between human expertise, statistical inference, and computational tools. Illustrative examples highlight both opportunities and pitfalls at the interface of biostatistics, data science, and translational medicine, emphasizing that maintaining human oversight is crucial for generating reproducible and interpretable evidence.
Christian Schmid, PhD F. Hoffmann – La Roche AG	Christian Schmid is a Senior Statistical Scientist at Roche and a founding member of the CMC Statistical Network Europe, where he has served on the steering committee since its inception. In his role, he primarily supports Roche’s commercial manufacturing and has recently expanded his work to include several AI validation initiatives. He earned his PhD in Statistics from The Pennsylvania State University, with a specialization in computational methods.	Validating GenAI (Generative Artificial IntelligenceI) in GxP (Good x Practice) environments requires moving beyond traditional "exact match" testing to a framework built on statistical confidence and risk management This presentation proposes a validation framework for GenAI applications, where rigorous testing strategies are used to quantify the reliability of non-deterministic outputs. We will explore technical controls for risk mitigation, specifically the use of Retrieval-Augmented Generation (RAG) architectures and parameter adjustments, such as temperature and top-p, to control output variability. The session will cover specific methods for combining automated scoring with structured Subject Matter Expert (SME) review to evaluate correctness and detect hallucinations, ensuring that probabilistic systems meet the safety standards required for regulated healthcare applications.

Speaker	Biography	Abstract
Tina Lang, PhD Bayer Pharmaceuticals AG	Tina Lang studied statistics at TU Dortmund where she also earned her PhD. She joined Bayer in 2009 as a preclinical statistician, supporting in vitro and in vivo experiments across the indication portfolio. Additionally, she is highly invested in elevating statistical literacy within the company and leads several training initiatives. In 2024 she transferred to the department of Data Science & Artificial Intelligence, where she has more insights into AI tools and their use within the drug development process.	Quality in Preclinical Research - The Importance of Being Human Preclinical experiments form the empirical foundation of translational medicine by assessing the feasibility, safety, and efficacy of new therapeutic approaches. Yet, unlike the highly regulated standards of clinical trials, preclinical research often exhibits substantial methodological heterogeneity, leading to concerns about reproducibility, bias, and the robustness of conclusions. These challenges are further compounded by the emerging use of artificial intelligence (AI). While AI has the potential to increase efficiency and support data analysis, uncontrolled or poorly understood applications can amplify existing weaknesses in study quality. In this presentation, we discuss the critical importance of human judgment, statistical rigor, and transparent study design for ensuring reliable preclinical evidence. We examine key dimensions of analytical quality across study design, data generation, and evaluation, and consider how AI - particularly large language models (LLMs) and foundation models - can support, rather than undermine, methodological integrity. From a biometrical perspective, safeguards for uncertainty quantification, error control, and interpretability are essential when integrating AI into preclinical research. Ultimately, methodological progress depends on a careful balance between human expertise, statistical inference, and computational tools. Illustrative examples highlight both opportunities and pitfalls at the interface of biostatistics, data science, and translational medicine, emphasizing that maintaining human oversight is crucial for generating reproducible and interpretable evidence.
Christian Schmid, PhD F. Hoffmann – La Roche AG	Christian Schmid is a Senior Statistical Scientist at Roche and a founding member of the CMC Statistical Network Europe, where he has served on the steering committee since its inception. In his role, he primarily supports Roche’s commercial manufacturing and has recently expanded his work to include several AI validation initiatives. He earned his PhD in Statistics from The Pennsylvania State University, with a specialization in computational methods.	Validating GenAI (Generative Artificial IntelligenceI) in GxP (Good x Practice) environments requires moving beyond traditional "exact match" testing to a framework built on statistical confidence and risk management This presentation proposes a validation framework for GenAI applications, where rigorous testing strategies are used to quantify the reliability of non-deterministic outputs. We will explore technical controls for risk mitigation, specifically the use of Retrieval-Augmented Generation (RAG) architectures and parameter adjustments, such as temperature and top-p, to control output variability. The session will cover specific methods for combining automated scoring with structured Subject Matter Expert (SME) review to evaluate correctness and detect hallucinations, ensuring that probabilistic systems meet the safety standards required for regulated healthcare applications.

Speaker	Biography	Abstract
Tina Lang, PhD Bayer Pharmaceuticals AG	Tina Lang studied statistics at TU Dortmund where she also earned her PhD. She joined Bayer in 2009 as a preclinical statistician, supporting in vitro and in vivo experiments across the indication portfolio. Additionally, she is highly invested in elevating statistical literacy within the company and leads several training initiatives. In 2024 she transferred to the department of Data Science & Artificial Intelligence, where she has more insights into AI tools and their use within the drug development process.	Quality in Preclinical Research - The Importance of Being Human Preclinical experiments form the empirical foundation of translational medicine by assessing the feasibility, safety, and efficacy of new therapeutic approaches. Yet, unlike the highly regulated standards of clinical trials, preclinical research often exhibits substantial methodological heterogeneity, leading to concerns about reproducibility, bias, and the robustness of conclusions. These challenges are further compounded by the emerging use of artificial intelligence (AI). While AI has the potential to increase efficiency and support data analysis, uncontrolled or poorly understood applications can amplify existing weaknesses in study quality. In this presentation, we discuss the critical importance of human judgment, statistical rigor, and transparent study design for ensuring reliable preclinical evidence. We examine key dimensions of analytical quality across study design, data generation, and evaluation, and consider how AI - particularly large language models (LLMs) and foundation models - can support, rather than undermine, methodological integrity. From a biometrical perspective, safeguards for uncertainty quantification, error control, and interpretability are essential when integrating AI into preclinical research. Ultimately, methodological progress depends on a careful balance between human expertise, statistical inference, and computational tools. Illustrative examples highlight both opportunities and pitfalls at the interface of biostatistics, data science, and translational medicine, emphasizing that maintaining human oversight is crucial for generating reproducible and interpretable evidence.
Christian Schmid, PhD F. Hoffmann – La Roche AG	Christian Schmid is a Senior Statistical Scientist at Roche and a founding member of the CMC Statistical Network Europe, where he has served on the steering committee since its inception. In his role, he primarily supports Roche’s commercial manufacturing and has recently expanded his work to include several AI validation initiatives. He earned his PhD in Statistics from The Pennsylvania State University, with a specialization in computational methods.	Validating GenAI (Generative Artificial IntelligenceI) in GxP (Good x Practice) environments requires moving beyond traditional "exact match" testing to a framework built on statistical confidence and risk management This presentation proposes a validation framework for GenAI applications, where rigorous testing strategies are used to quantify the reliability of non-deterministic outputs. We will explore technical controls for risk mitigation, specifically the use of Retrieval-Augmented Generation (RAG) architectures and parameter adjustments, such as temperature and top-p, to control output variability. The session will cover specific methods for combining automated scoring with structured Subject Matter Expert (SME) review to evaluate correctness and detect hallucinations, ensuring that probabilistic systems meet the safety standards required for regulated healthcare applications.

Speaker	Biography	Abstract
Tina Lang, PhD Bayer Pharmaceuticals AG	Tina Lang studied statistics at TU Dortmund where she also earned her PhD. She joined Bayer in 2009 as a preclinical statistician, supporting in vitro and in vivo experiments across the indication portfolio. Additionally, she is highly invested in elevating statistical literacy within the company and leads several training initiatives. In 2024 she transferred to the department of Data Science & Artificial Intelligence, where she has more insights into AI tools and their use within the drug development process.	Quality in Preclinical Research - The Importance of Being Human Preclinical experiments form the empirical foundation of translational medicine by assessing the feasibility, safety, and efficacy of new therapeutic approaches. Yet, unlike the highly regulated standards of clinical trials, preclinical research often exhibits substantial methodological heterogeneity, leading to concerns about reproducibility, bias, and the robustness of conclusions. These challenges are further compounded by the emerging use of artificial intelligence (AI). While AI has the potential to increase efficiency and support data analysis, uncontrolled or poorly understood applications can amplify existing weaknesses in study quality. In this presentation, we discuss the critical importance of human judgment, statistical rigor, and transparent study design for ensuring reliable preclinical evidence. We examine key dimensions of analytical quality across study design, data generation, and evaluation, and consider how AI - particularly large language models (LLMs) and foundation models - can support, rather than undermine, methodological integrity. From a biometrical perspective, safeguards for uncertainty quantification, error control, and interpretability are essential when integrating AI into preclinical research. Ultimately, methodological progress depends on a careful balance between human expertise, statistical inference, and computational tools. Illustrative examples highlight both opportunities and pitfalls at the interface of biostatistics, data science, and translational medicine, emphasizing that maintaining human oversight is crucial for generating reproducible and interpretable evidence.
Christian Schmid, PhD F. Hoffmann – La Roche AG	Christian Schmid is a Senior Statistical Scientist at Roche and a founding member of the CMC Statistical Network Europe, where he has served on the steering committee since its inception. In his role, he primarily supports Roche’s commercial manufacturing and has recently expanded his work to include several AI validation initiatives. He earned his PhD in Statistics from The Pennsylvania State University, with a specialization in computational methods.	Validating GenAI (Generative Artificial IntelligenceI) in GxP (Good x Practice) environments requires moving beyond traditional "exact match" testing to a framework built on statistical confidence and risk management This presentation proposes a validation framework for GenAI applications, where rigorous testing strategies are used to quantify the reliability of non-deterministic outputs. We will explore technical controls for risk mitigation, specifically the use of Retrieval-Augmented Generation (RAG) architectures and parameter adjustments, such as temperature and top-p, to control output variability. The session will cover specific methods for combining automated scoring with structured Subject Matter Expert (SME) review to evaluate correctness and detect hallucinations, ensuring that probabilistic systems meet the safety standards required for regulated healthcare applications.

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