PSI Webinar: Tipping Point Analyses - Introduction & Case Studies

Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars

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

AIMS SIG - Open-Source Lunch Bites

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

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

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

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

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

Speaker	Biography	Abstract
Juan Abellan	Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.	Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.
Kevin Ding	Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.	Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker	Biography	Abstract
Juan Abellan	Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.	Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.
Kevin Ding	Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.	Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker	Biography	Abstract
Juan Abellan	Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.	Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.
Kevin Ding	Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.	Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker	Biography	Abstract
Juan Abellan	Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.	Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.
Kevin Ding	Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.	Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

Speaker	Biography	Abstract
Juan Abellan	Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.	Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed.
Kevin Ding	Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.	Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective.

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