Date: Tuesday 3rd February 2026 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Weiliang Qiu (Sanofi)
Who is this event intended for?: Statisticians and Scientists involved or interested in Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity.
What is the benefit of attending?: Be able to choose the most appropriate method on Heteroscedastic data
Cost
This webinar is free to both Members of PSI and Non-Members.
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Speaker details
Speaker
Biography
Abstract
Weiliang Qiu, Sanofi
Weiliang Qiu is a Non-Clinical Efficacy and Safety Statistician Expert Leader at Sanofi, passionate about leveraging statistical expertise to improve patients’ lives. He earned his Ph.D. in Statistics from the University of British Columbia in 2004 and spent 14 years at Brigham and Women’s Hospital/Harvard Medical School, contributing to impactful research.
Since joining Sanofi’s Non-Clinical Efficacy and Safety (NCES) team in 2018, Weiliang has provided statistical support for non-clinical studies across diverse therapeutic areas, including translational sciences, rare and neurological diseases, immunology and inflammation, immuno-oncology, and gene therapy. In addition to supporting these studies, he collaborates closely with teammates in NCES to design and implement innovative statistical methodologies that enhance data analysis and drive scientific insights.
Modern Algorithms for Animal Randomization in Preclinical Studies
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Scientific Meetings
Pre-Clinical SIG Webinar: Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity
Date: Tuesday 3rd February 2026 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Weiliang Qiu (Sanofi)
Who is this event intended for?: Statisticians and Scientists involved or interested in Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity.
What is the benefit of attending?: Be able to choose the most appropriate method on Heteroscedastic data
Cost
This webinar is free to both Members of PSI and Non-Members.
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Speaker details
Speaker
Biography
Abstract
Weiliang Qiu, Sanofi
Weiliang Qiu is a Non-Clinical Efficacy and Safety Statistician Expert Leader at Sanofi, passionate about leveraging statistical expertise to improve patients’ lives. He earned his Ph.D. in Statistics from the University of British Columbia in 2004 and spent 14 years at Brigham and Women’s Hospital/Harvard Medical School, contributing to impactful research.
Since joining Sanofi’s Non-Clinical Efficacy and Safety (NCES) team in 2018, Weiliang has provided statistical support for non-clinical studies across diverse therapeutic areas, including translational sciences, rare and neurological diseases, immunology and inflammation, immuno-oncology, and gene therapy. In addition to supporting these studies, he collaborates closely with teammates in NCES to design and implement innovative statistical methodologies that enhance data analysis and drive scientific insights.
Modern Algorithms for Animal Randomization in Preclinical Studies
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Training Courses
Pre-Clinical SIG Webinar: Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity
Date: Tuesday 3rd February 2026 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Weiliang Qiu (Sanofi)
Who is this event intended for?: Statisticians and Scientists involved or interested in Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity.
What is the benefit of attending?: Be able to choose the most appropriate method on Heteroscedastic data
Cost
This webinar is free to both Members of PSI and Non-Members.
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Speaker details
Speaker
Biography
Abstract
Weiliang Qiu, Sanofi
Weiliang Qiu is a Non-Clinical Efficacy and Safety Statistician Expert Leader at Sanofi, passionate about leveraging statistical expertise to improve patients’ lives. He earned his Ph.D. in Statistics from the University of British Columbia in 2004 and spent 14 years at Brigham and Women’s Hospital/Harvard Medical School, contributing to impactful research.
Since joining Sanofi’s Non-Clinical Efficacy and Safety (NCES) team in 2018, Weiliang has provided statistical support for non-clinical studies across diverse therapeutic areas, including translational sciences, rare and neurological diseases, immunology and inflammation, immuno-oncology, and gene therapy. In addition to supporting these studies, he collaborates closely with teammates in NCES to design and implement innovative statistical methodologies that enhance data analysis and drive scientific insights.
Modern Algorithms for Animal Randomization in Preclinical Studies
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Journal Club
Pre-Clinical SIG Webinar: Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity
Date: Tuesday 3rd February 2026 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Weiliang Qiu (Sanofi)
Who is this event intended for?: Statisticians and Scientists involved or interested in Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity.
What is the benefit of attending?: Be able to choose the most appropriate method on Heteroscedastic data
Cost
This webinar is free to both Members of PSI and Non-Members.
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Speaker details
Speaker
Biography
Abstract
Weiliang Qiu, Sanofi
Weiliang Qiu is a Non-Clinical Efficacy and Safety Statistician Expert Leader at Sanofi, passionate about leveraging statistical expertise to improve patients’ lives. He earned his Ph.D. in Statistics from the University of British Columbia in 2004 and spent 14 years at Brigham and Women’s Hospital/Harvard Medical School, contributing to impactful research.
Since joining Sanofi’s Non-Clinical Efficacy and Safety (NCES) team in 2018, Weiliang has provided statistical support for non-clinical studies across diverse therapeutic areas, including translational sciences, rare and neurological diseases, immunology and inflammation, immuno-oncology, and gene therapy. In addition to supporting these studies, he collaborates closely with teammates in NCES to design and implement innovative statistical methodologies that enhance data analysis and drive scientific insights.
Modern Algorithms for Animal Randomization in Preclinical Studies
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Webinars
Pre-Clinical SIG Webinar: Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity
Date: Tuesday 3rd February 2026 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Weiliang Qiu (Sanofi)
Who is this event intended for?: Statisticians and Scientists involved or interested in Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity.
What is the benefit of attending?: Be able to choose the most appropriate method on Heteroscedastic data
Cost
This webinar is free to both Members of PSI and Non-Members.
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Speaker details
Speaker
Biography
Abstract
Weiliang Qiu, Sanofi
Weiliang Qiu is a Non-Clinical Efficacy and Safety Statistician Expert Leader at Sanofi, passionate about leveraging statistical expertise to improve patients’ lives. He earned his Ph.D. in Statistics from the University of British Columbia in 2004 and spent 14 years at Brigham and Women’s Hospital/Harvard Medical School, contributing to impactful research.
Since joining Sanofi’s Non-Clinical Efficacy and Safety (NCES) team in 2018, Weiliang has provided statistical support for non-clinical studies across diverse therapeutic areas, including translational sciences, rare and neurological diseases, immunology and inflammation, immuno-oncology, and gene therapy. In addition to supporting these studies, he collaborates closely with teammates in NCES to design and implement innovative statistical methodologies that enhance data analysis and drive scientific insights.
Modern Algorithms for Animal Randomization in Preclinical Studies
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Careers Meetings
Pre-Clinical SIG Webinar: Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity
Date: Tuesday 3rd February 2026 Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US) Location: Online via Zoom Speakers: Weiliang Qiu (Sanofi)
Who is this event intended for?: Statisticians and Scientists involved or interested in Multi-Group Comparison of Means for Single-Factor Experiment with Small Sample Size Under Normality and Heteroscedasticity.
What is the benefit of attending?: Be able to choose the most appropriate method on Heteroscedastic data
Cost
This webinar is free to both Members of PSI and Non-Members.
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Speaker details
Speaker
Biography
Abstract
Weiliang Qiu, Sanofi
Weiliang Qiu is a Non-Clinical Efficacy and Safety Statistician Expert Leader at Sanofi, passionate about leveraging statistical expertise to improve patients’ lives. He earned his Ph.D. in Statistics from the University of British Columbia in 2004 and spent 14 years at Brigham and Women’s Hospital/Harvard Medical School, contributing to impactful research.
Since joining Sanofi’s Non-Clinical Efficacy and Safety (NCES) team in 2018, Weiliang has provided statistical support for non-clinical studies across diverse therapeutic areas, including translational sciences, rare and neurological diseases, immunology and inflammation, immuno-oncology, and gene therapy. In addition to supporting these studies, he collaborates closely with teammates in NCES to design and implement innovative statistical methodologies that enhance data analysis and drive scientific insights.
Modern Algorithms for Animal Randomization in Preclinical Studies
The design of single-factor experiment is commonly used to compare multiple groups in non-clinical studies, where group sizes are generally small and groups usually have different variances. The classical F test tends to inflate type I error rate in this scenario. Many alternative tests have been proposed in literature to manage heterogeneity of variance. Several papers compared these alternative tests, among which is heterogeneous-variance mixed-effects model using Satterthwaite approximation of degree of freedom. The mixed-effects model approach has at least 2 advantages over other tests: (1) allowing different group variance structures (e.g., homogeneous-variance or heterogeneous-variance); and (2) the capacity to do model diagnosis via residual analysis. In this presentation, we evaluate both Type I error rates and powers of the 13 tests investigated in Pham et al. (2020) spanning ANOVA-based tests, structured means modeling (SMM), and mixed-effects models—under diverse conditions, including (un)equal variances, (non-)normal distributions, and (un)balanced designs. Two additional mixed-effects models (homogeneous-variance mixed-effects model and adaptive mixed-effects model) are introduced and assessed alongside the 13 tests. We also consider the Kenward-Roger approximation of degrees of freedom for the 3 mixed-effects models, which generally offers more reliable type I error rate than the Satterthwaite approximation. Some recommendations about analysis for data from single-factor experiments will finally be given.
Upcoming Events
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.
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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.
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This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
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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.
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.
Join our Health Technology Assessment (HTA) European Special Interest Group (ESIG) for a webinar on the strategic role of statisticians in the Joint Clinical Assessment (JCA). The introduction of the JCA marks a new era for evidence generation and market access in Europe. As HTA requirements become more harmonized and methodologically demanding, the role of statisticians has evolved far beyond data analysis. Today, statistical expertise is central to shaping clinical development strategies, designing robust comparative evidence, and ensuring that submissions withstand the scrutiny of EU-level assessors. In this webinar, we explore how statisticians contribute strategically to successful JCA outcomes.
Statisticians in the Age of AI: On Route to Strategic Partnership
A 90-minute webinar featuring two case studies from Bayer and Roche demonstrating how statisticians successfully integrated into AI programs, followed by interactive discussion on strategies for elevating statistical expertise in the AI era.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
GSK - Statistics Director - Vaccines and Infectious Disease
We are seeking an experienced and visionary Statistics Director to join our Team and lead strategic statistical innovation across GSK’s Vaccines and Infectious Disease portfolio.
As a Senior Biostatistician I at ICON, you will play a pivotal role in designing and analyzing clinical trials, interpreting complex medical data, and contributing to the advancement of innovative treatments and therapies.
As a Statistical Scientist at ICON, you will play a pivotal role in designing and analyzing clinical trials, interpreting complex medical data, and contributing to the advancement of innovative treatments and therapies.
We have an exciting opportunity for an Associate Director, Biostatistics to join a passionate team within Advanced Quantitative Sciences – Full Development.
: We have an exciting opportunity for an Associate Director (AD), Statistical Programming, to join a passionate team within Advanced Quantitative Sciences- Development.
Novartis - Senior Principal Statistical Programmer
We have an exciting opportunity for a Senior Principal Statistical Programmer, to join a passionate team within Advanced Quantitative Sciences – Development.
Pierre Fabre - Clinical Development Safety Statistics Expert M/F
We are seeking a highly skilled and proactive Clinical Development Safety Statistics Expert to join our Biometry Department and the Biometry Leadership Team based in Toulouse (31, Oncopole) or Boulogne (92).
Pierre Fabre - Lead Statistician – Real World Evidence -CDI- M/F
Pierre Fabre Laboratories are hiring a highly skilled and experienced Lead Statistician – Real World Evidence (RWE) to join the Biometry Department, part of the Data Science & Biometry Department, based in Toulouse (Oncopôle) or Boulogne.
Pierre Fabre - Lead Statistician- Clinical Trials M/F
We are seeking a highly skilled and experienced Lead Statistician in Clinical Trials to join our Biometry Department based in Toulouse (31, Oncopole) or Boulogne (92).
We are looking for Senior Statistical Programmers in the UK to join Veramed, where you'll deliver high-impact programming solutions in an FSP-style capacity, while advancing your career in a supportive, growth-driven environment.
