Past PSI Events

Conferences

PSI Webinar: Causal Inference

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.

Scientific Meetings

PSI Webinar: Causal Inference

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.

Training Courses

PSI Webinar: Causal Inference

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.

Journal Club

PSI Webinar: Causal Inference

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.

Webinars

PSI Webinar: Causal Inference

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.

Careers Meetings

PSI Webinar: Causal Inference

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.

Upcoming Events

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.

PSI Mentoring 2026

Date: Ongoing 6 month cycle beginning late April/early May 2026 Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.

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.