Defining the estimand of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. The need for more precise specifications of estimands is highlighted in the draft addendum ICH E9(R1) which was published for public consultation in August 2017. Although not explicitly mentioned in ICH E9(R1), the addendum brings causal reasoning – besides randomization and ITT – into our world of pharmaceutical statistics. In this webinar, we will discuss the link between the ICH E9(R1) and causal inference. Furthermore, per protocol analyses will be discussed from a causal inference perspective and a case study where a principal strata estimand was investigated will be presented.
For more information please click here.
Baldur Magnusson
Novartis Pharma AG
Using principal stratification to address post-randomization events: A case study
In a randomized controlled trial, occurrence of post-randomization events associated with treatment and the primary endpoint may complicate the interpretation of the overall treatment effect. In this presentation, we discuss how these events may be accounted for at the estimand and the estimator level in the context of a recent case study. We define a principal stratification estimand derived from the scientific question of interest. Consideration is given to identifying assumptions, model-based derivation of an estimator, handling of covariates and missing data. We also discuss the role of sensitivity analyses.
Please click here to view the slides.
Daniel Scharfstein
Professor of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Estimands and Causal Inference
Recently, the ICH proposed an addendum to the E9 Guidance: Statistical Principles for Clinical Trials. This addendum is focused on estimands and sensitivity analysis for randomized trials with intercurrent events. In this webinar, I will discuss the potential outcomes framework for causal inference and use it to formally define estimands that address different types of intercurrent events. I will then discuss the assumptions required to identify these estimands from the observable data and discuss the important role of sensitivity analysis.
Please click here to view the slides.
Wanjie Sun
FDA/CDER/OB/DBVIII
Estimating Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Treatment Noncompliance and Missing Data
In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (i.e., completers and compliers in general). The FDA Missing Data Working Group and the ICH E9 Revision 1 Working Group recommended using “causal estimands of primary interest.” The analysis based on the PP population, however, is not generally causal because PP is determined post-treatment, hence conditioning on it may introduce selection bias. To date, no causal inference has been proposed to assess to equivalence. In this paper, we propose a causal framework and co-primary causal estimands to test equivalence by applying Frangakis and Rubin (2002)’s principal stratification in causal inference. We identify three conditions when the current PP estimator is unbiased for one of the proposed co-primary causal estimands – the“Survivor Average Causal Effect” (SACE) estimand. Simulation was used to demonstrate the bias, type 1 error, and power associated with the PP estimator when these three conditions are not met. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator (primary analysis) in testing equivalence when the underlying sensitivity parameters vary across a clinically meaningful range. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis method. Our work starts causal evaluation of equivalence assessment in clinical endpoint BE studies with non-compliance and missing data, and can be applied to clinical biosimilar and non-inferiority studies.
*The views expressed in this article represent the opinions of the authors, and do not represent the views and/or policies of the U.S. Food and Drug Administration.
To access the recording, please visit the Video-on-Demand Library.
Defining the estimand of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. The need for more precise specifications of estimands is highlighted in the draft addendum ICH E9(R1) which was published for public consultation in August 2017. Although not explicitly mentioned in ICH E9(R1), the addendum brings causal reasoning – besides randomization and ITT – into our world of pharmaceutical statistics. In this webinar, we will discuss the link between the ICH E9(R1) and causal inference. Furthermore, per protocol analyses will be discussed from a causal inference perspective and a case study where a principal strata estimand was investigated will be presented.
For more information please click here.
Baldur Magnusson
Novartis Pharma AG
Using principal stratification to address post-randomization events: A case study
In a randomized controlled trial, occurrence of post-randomization events associated with treatment and the primary endpoint may complicate the interpretation of the overall treatment effect. In this presentation, we discuss how these events may be accounted for at the estimand and the estimator level in the context of a recent case study. We define a principal stratification estimand derived from the scientific question of interest. Consideration is given to identifying assumptions, model-based derivation of an estimator, handling of covariates and missing data. We also discuss the role of sensitivity analyses.
Please click here to view the slides.
Daniel Scharfstein
Professor of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Estimands and Causal Inference
Recently, the ICH proposed an addendum to the E9 Guidance: Statistical Principles for Clinical Trials. This addendum is focused on estimands and sensitivity analysis for randomized trials with intercurrent events. In this webinar, I will discuss the potential outcomes framework for causal inference and use it to formally define estimands that address different types of intercurrent events. I will then discuss the assumptions required to identify these estimands from the observable data and discuss the important role of sensitivity analysis.
Please click here to view the slides.
Wanjie Sun
FDA/CDER/OB/DBVIII
Estimating Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Treatment Noncompliance and Missing Data
In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (i.e., completers and compliers in general). The FDA Missing Data Working Group and the ICH E9 Revision 1 Working Group recommended using “causal estimands of primary interest.” The analysis based on the PP population, however, is not generally causal because PP is determined post-treatment, hence conditioning on it may introduce selection bias. To date, no causal inference has been proposed to assess to equivalence. In this paper, we propose a causal framework and co-primary causal estimands to test equivalence by applying Frangakis and Rubin (2002)’s principal stratification in causal inference. We identify three conditions when the current PP estimator is unbiased for one of the proposed co-primary causal estimands – the“Survivor Average Causal Effect” (SACE) estimand. Simulation was used to demonstrate the bias, type 1 error, and power associated with the PP estimator when these three conditions are not met. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator (primary analysis) in testing equivalence when the underlying sensitivity parameters vary across a clinically meaningful range. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis method. Our work starts causal evaluation of equivalence assessment in clinical endpoint BE studies with non-compliance and missing data, and can be applied to clinical biosimilar and non-inferiority studies.
*The views expressed in this article represent the opinions of the authors, and do not represent the views and/or policies of the U.S. Food and Drug Administration.
To access the recording, please visit the Video-on-Demand Library.
Defining the estimand of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. The need for more precise specifications of estimands is highlighted in the draft addendum ICH E9(R1) which was published for public consultation in August 2017. Although not explicitly mentioned in ICH E9(R1), the addendum brings causal reasoning – besides randomization and ITT – into our world of pharmaceutical statistics. In this webinar, we will discuss the link between the ICH E9(R1) and causal inference. Furthermore, per protocol analyses will be discussed from a causal inference perspective and a case study where a principal strata estimand was investigated will be presented.
For more information please click here.
Baldur Magnusson
Novartis Pharma AG
Using principal stratification to address post-randomization events: A case study
In a randomized controlled trial, occurrence of post-randomization events associated with treatment and the primary endpoint may complicate the interpretation of the overall treatment effect. In this presentation, we discuss how these events may be accounted for at the estimand and the estimator level in the context of a recent case study. We define a principal stratification estimand derived from the scientific question of interest. Consideration is given to identifying assumptions, model-based derivation of an estimator, handling of covariates and missing data. We also discuss the role of sensitivity analyses.
Please click here to view the slides.
Daniel Scharfstein
Professor of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Estimands and Causal Inference
Recently, the ICH proposed an addendum to the E9 Guidance: Statistical Principles for Clinical Trials. This addendum is focused on estimands and sensitivity analysis for randomized trials with intercurrent events. In this webinar, I will discuss the potential outcomes framework for causal inference and use it to formally define estimands that address different types of intercurrent events. I will then discuss the assumptions required to identify these estimands from the observable data and discuss the important role of sensitivity analysis.
Please click here to view the slides.
Wanjie Sun
FDA/CDER/OB/DBVIII
Estimating Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Treatment Noncompliance and Missing Data
In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (i.e., completers and compliers in general). The FDA Missing Data Working Group and the ICH E9 Revision 1 Working Group recommended using “causal estimands of primary interest.” The analysis based on the PP population, however, is not generally causal because PP is determined post-treatment, hence conditioning on it may introduce selection bias. To date, no causal inference has been proposed to assess to equivalence. In this paper, we propose a causal framework and co-primary causal estimands to test equivalence by applying Frangakis and Rubin (2002)’s principal stratification in causal inference. We identify three conditions when the current PP estimator is unbiased for one of the proposed co-primary causal estimands – the“Survivor Average Causal Effect” (SACE) estimand. Simulation was used to demonstrate the bias, type 1 error, and power associated with the PP estimator when these three conditions are not met. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator (primary analysis) in testing equivalence when the underlying sensitivity parameters vary across a clinically meaningful range. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis method. Our work starts causal evaluation of equivalence assessment in clinical endpoint BE studies with non-compliance and missing data, and can be applied to clinical biosimilar and non-inferiority studies.
*The views expressed in this article represent the opinions of the authors, and do not represent the views and/or policies of the U.S. Food and Drug Administration.
To access the recording, please visit the Video-on-Demand Library.
Defining the estimand of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. The need for more precise specifications of estimands is highlighted in the draft addendum ICH E9(R1) which was published for public consultation in August 2017. Although not explicitly mentioned in ICH E9(R1), the addendum brings causal reasoning – besides randomization and ITT – into our world of pharmaceutical statistics. In this webinar, we will discuss the link between the ICH E9(R1) and causal inference. Furthermore, per protocol analyses will be discussed from a causal inference perspective and a case study where a principal strata estimand was investigated will be presented.
For more information please click here.
Baldur Magnusson
Novartis Pharma AG
Using principal stratification to address post-randomization events: A case study
In a randomized controlled trial, occurrence of post-randomization events associated with treatment and the primary endpoint may complicate the interpretation of the overall treatment effect. In this presentation, we discuss how these events may be accounted for at the estimand and the estimator level in the context of a recent case study. We define a principal stratification estimand derived from the scientific question of interest. Consideration is given to identifying assumptions, model-based derivation of an estimator, handling of covariates and missing data. We also discuss the role of sensitivity analyses.
Please click here to view the slides.
Daniel Scharfstein
Professor of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Estimands and Causal Inference
Recently, the ICH proposed an addendum to the E9 Guidance: Statistical Principles for Clinical Trials. This addendum is focused on estimands and sensitivity analysis for randomized trials with intercurrent events. In this webinar, I will discuss the potential outcomes framework for causal inference and use it to formally define estimands that address different types of intercurrent events. I will then discuss the assumptions required to identify these estimands from the observable data and discuss the important role of sensitivity analysis.
Please click here to view the slides.
Wanjie Sun
FDA/CDER/OB/DBVIII
Estimating Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Treatment Noncompliance and Missing Data
In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (i.e., completers and compliers in general). The FDA Missing Data Working Group and the ICH E9 Revision 1 Working Group recommended using “causal estimands of primary interest.” The analysis based on the PP population, however, is not generally causal because PP is determined post-treatment, hence conditioning on it may introduce selection bias. To date, no causal inference has been proposed to assess to equivalence. In this paper, we propose a causal framework and co-primary causal estimands to test equivalence by applying Frangakis and Rubin (2002)’s principal stratification in causal inference. We identify three conditions when the current PP estimator is unbiased for one of the proposed co-primary causal estimands – the“Survivor Average Causal Effect” (SACE) estimand. Simulation was used to demonstrate the bias, type 1 error, and power associated with the PP estimator when these three conditions are not met. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator (primary analysis) in testing equivalence when the underlying sensitivity parameters vary across a clinically meaningful range. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis method. Our work starts causal evaluation of equivalence assessment in clinical endpoint BE studies with non-compliance and missing data, and can be applied to clinical biosimilar and non-inferiority studies.
*The views expressed in this article represent the opinions of the authors, and do not represent the views and/or policies of the U.S. Food and Drug Administration.
To access the recording, please visit the Video-on-Demand Library.
Defining the estimand of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. The need for more precise specifications of estimands is highlighted in the draft addendum ICH E9(R1) which was published for public consultation in August 2017. Although not explicitly mentioned in ICH E9(R1), the addendum brings causal reasoning – besides randomization and ITT – into our world of pharmaceutical statistics. In this webinar, we will discuss the link between the ICH E9(R1) and causal inference. Furthermore, per protocol analyses will be discussed from a causal inference perspective and a case study where a principal strata estimand was investigated will be presented.
For more information please click here.
Baldur Magnusson
Novartis Pharma AG
Using principal stratification to address post-randomization events: A case study
In a randomized controlled trial, occurrence of post-randomization events associated with treatment and the primary endpoint may complicate the interpretation of the overall treatment effect. In this presentation, we discuss how these events may be accounted for at the estimand and the estimator level in the context of a recent case study. We define a principal stratification estimand derived from the scientific question of interest. Consideration is given to identifying assumptions, model-based derivation of an estimator, handling of covariates and missing data. We also discuss the role of sensitivity analyses.
Please click here to view the slides.
Daniel Scharfstein
Professor of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Estimands and Causal Inference
Recently, the ICH proposed an addendum to the E9 Guidance: Statistical Principles for Clinical Trials. This addendum is focused on estimands and sensitivity analysis for randomized trials with intercurrent events. In this webinar, I will discuss the potential outcomes framework for causal inference and use it to formally define estimands that address different types of intercurrent events. I will then discuss the assumptions required to identify these estimands from the observable data and discuss the important role of sensitivity analysis.
Please click here to view the slides.
Wanjie Sun
FDA/CDER/OB/DBVIII
Estimating Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Treatment Noncompliance and Missing Data
In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (i.e., completers and compliers in general). The FDA Missing Data Working Group and the ICH E9 Revision 1 Working Group recommended using “causal estimands of primary interest.” The analysis based on the PP population, however, is not generally causal because PP is determined post-treatment, hence conditioning on it may introduce selection bias. To date, no causal inference has been proposed to assess to equivalence. In this paper, we propose a causal framework and co-primary causal estimands to test equivalence by applying Frangakis and Rubin (2002)’s principal stratification in causal inference. We identify three conditions when the current PP estimator is unbiased for one of the proposed co-primary causal estimands – the“Survivor Average Causal Effect” (SACE) estimand. Simulation was used to demonstrate the bias, type 1 error, and power associated with the PP estimator when these three conditions are not met. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator (primary analysis) in testing equivalence when the underlying sensitivity parameters vary across a clinically meaningful range. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis method. Our work starts causal evaluation of equivalence assessment in clinical endpoint BE studies with non-compliance and missing data, and can be applied to clinical biosimilar and non-inferiority studies.
*The views expressed in this article represent the opinions of the authors, and do not represent the views and/or policies of the U.S. Food and Drug Administration.
To access the recording, please visit the Video-on-Demand Library.
Defining the estimand of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. The need for more precise specifications of estimands is highlighted in the draft addendum ICH E9(R1) which was published for public consultation in August 2017. Although not explicitly mentioned in ICH E9(R1), the addendum brings causal reasoning – besides randomization and ITT – into our world of pharmaceutical statistics. In this webinar, we will discuss the link between the ICH E9(R1) and causal inference. Furthermore, per protocol analyses will be discussed from a causal inference perspective and a case study where a principal strata estimand was investigated will be presented.
For more information please click here.
Baldur Magnusson
Novartis Pharma AG
Using principal stratification to address post-randomization events: A case study
In a randomized controlled trial, occurrence of post-randomization events associated with treatment and the primary endpoint may complicate the interpretation of the overall treatment effect. In this presentation, we discuss how these events may be accounted for at the estimand and the estimator level in the context of a recent case study. We define a principal stratification estimand derived from the scientific question of interest. Consideration is given to identifying assumptions, model-based derivation of an estimator, handling of covariates and missing data. We also discuss the role of sensitivity analyses.
Please click here to view the slides.
Daniel Scharfstein
Professor of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Estimands and Causal Inference
Recently, the ICH proposed an addendum to the E9 Guidance: Statistical Principles for Clinical Trials. This addendum is focused on estimands and sensitivity analysis for randomized trials with intercurrent events. In this webinar, I will discuss the potential outcomes framework for causal inference and use it to formally define estimands that address different types of intercurrent events. I will then discuss the assumptions required to identify these estimands from the observable data and discuss the important role of sensitivity analysis.
Please click here to view the slides.
Wanjie Sun
FDA/CDER/OB/DBVIII
Estimating Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Treatment Noncompliance and Missing Data
In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (i.e., completers and compliers in general). The FDA Missing Data Working Group and the ICH E9 Revision 1 Working Group recommended using “causal estimands of primary interest.” The analysis based on the PP population, however, is not generally causal because PP is determined post-treatment, hence conditioning on it may introduce selection bias. To date, no causal inference has been proposed to assess to equivalence. In this paper, we propose a causal framework and co-primary causal estimands to test equivalence by applying Frangakis and Rubin (2002)’s principal stratification in causal inference. We identify three conditions when the current PP estimator is unbiased for one of the proposed co-primary causal estimands – the“Survivor Average Causal Effect” (SACE) estimand. Simulation was used to demonstrate the bias, type 1 error, and power associated with the PP estimator when these three conditions are not met. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator (primary analysis) in testing equivalence when the underlying sensitivity parameters vary across a clinically meaningful range. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis method. Our work starts causal evaluation of equivalence assessment in clinical endpoint BE studies with non-compliance and missing data, and can be applied to clinical biosimilar and non-inferiority studies.
*The views expressed in this article represent the opinions of the authors, and do not represent the views and/or policies of the U.S. Food and Drug Administration.
To access the recording, please visit the Video-on-Demand Library.
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.
Our monthly webinar will allow attendees to gain practical knowledge and skills in Open-Source coding and tools, with a focus on applications in the pharmaceutical industry. The sessions will provide starting points in a number of areas, correct any common misconceptions and provide valuable resources for further learning.
Develop your non-technical skills by reading The Art of Explanation by Ros Atkins and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply skills from the book in-between sessions.
Date: Tuesday 9th September 2025 Join us to hear Sofia Weigle and Weng-Kee Wong present their recent work.
This course is aimed at biostatisticians with no or some pediatric drug development experience who are interested to further their understanding. We will give you an introduction to the pediatric drug development landscape. This will include identifying the key regulations and processes governing pediatric development, a discussion on the needs and challenges when conducting pediatric research and a focus on the ways to overcome these challenges from a statistical perspective.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
AI agents are large language models equipped with tools that can autonomously tackle challenging tasks. This talk will explore how generative AI agents can enable biomedical discovery.
The webinar is targeted at statisticians working in the pharmaceutical industry, and the objective is to 1) provide a basic understanding of IV methodology including how it relates to causal inference, and 2) present two inspirational pharma-relevant applications.
The Pre-Clinical Special Interest Group (SIG) Workshop 2025 will take place over two half-days on 7 - 8 October in Verona, Italy, bringing together experts from industry, academia, and regulatory institutions to discuss key challenges and innovations in pre-clinical research.
Four sessions will include ML foundation (including an introduction, data exploration for ML and dimensionality reduction and feature selection), Supervised learning (including support vector machines and model evaluation and interpretation), model optimization and unsupervised learning (including clustering) and advanced topics (including neural networks, deep learning and large language models).
The program will feature insightful sessions led by distinguished invited speakers, alongside a poster session showcasing the latest advancements in the field. Further details will be provided.
Date: Wednesday 5th November 2025 A careers talk about medical statistics and how it plays a crucial role in developing new medicines
Date: 19 November 2025 This event is aimed at students with an interest in the field of Medical Statistics, for example within pharmaceuticals, healthcare and/or medical research.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
As an Associate Director Biostatistics Early Development, you will be a key member of our biostatistics group, you will play a crucial role in the design, analysis, and interpretation of clinical trials for early development programs.
If you are passionate about biostatistics and real-world data, and are looking for an exciting opportunity to contribute to groundbreaking research, we encourage you to apply.
Are you passionate about making a difference in the world of healthcare? Novartis is seeking a dynamic and experienced professional to join our team in London at The Westworks.
As the Director of HTA Biostatistics & Medical Affairs, you will play a pivotal role in shaping the future of healthcare by providing strategic biostatistical leadership and expertise.
As a Senior Principal Biostatistician, you will be responsible and accountable for all statistical work, both scientific and operational, for one or more assigned clinical trials
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