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14 February 2024

Lorenz Uhlmann is guiding the way to improve a plot to make it a more effective visualisation. Visualisations are available on the Wonderful Wednesday blog.

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Lorenz Uhlmann is guiding the way to improve a plot to make it a more effective visualisation. Visualisations are available on the Wonderful Wednesday blog.

There is a lot of different aspects to consider for a good visual. Be inspired by examples using animation, interactivity, adaptation of scale and color or implemented visual explanation. The next challenge is presented by special guest Mathilde Saccareau. See the Wonderful Wednesday homepage for more detail.

Wonderful Wednesdays are brought to you by the Visualisation SIG. The Wonderful Wednesday team includes: Bodo Kirsch, Alexander Schacht, Mark Baillie, Zachary Skrivanek, Lorenz Uhlmann, David Carr, Steve Mallett, Rhys Warham, Lovemore Gakava, Zara Sari, Paolo Eusebi, Martin Karpefors, Benjamin Lang, Elias Laurin Meyer


10 January 2024

In the first Wonderful Wednesday of 2024 Bodo Kirsch is presenting the top 6 visualisations of 2023. Visualisations are available on the Wonderful Wednesday blog.

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In the first Wonderful Wednesday of 2024 Bodo Kirsch is presenting the top 6 visualisations of 2023. Visualisations are available on the Wonderful Wednesday blog.

Even the best graphs can be improved. Follow the panel discussing features that make a visualisation especially effective. The next challenge is looking for the published visualisations that can use an improvement. See the Wonderful Wednesday homepage for more detail.

Wonderful Wednesdays are brought to you by the Visualisation SIG. The Wonderful Wednesday team includes: Bodo Kirsch, Alexander Schacht, Mark Baillie, Zachary Skrivanek, Lorenz Uhlmann, Rachel Phillips, David Carr, Steve Mallett, Rhys Warham, Lovemore Gakava, Zara Sari, Paolo Eusebi, Martin Karpefors, Benjamin Lang, Elias Laurin Meyer

14 December 2023

Causal Inference in Clinical Trials: To understand the potential of the practical application of causal inference methods in drug development and be able to apply these to real world problems or clinical trials.

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Stephen Ruberg, Yongming Qu, Sean Yiu, and Martin Linder.

Causal Inference in Clinical Trials: To understand the potential of the practical application of causal inference methods in drug development and be able to apply these to real world problems or clinical trials.

This second webinar in this two-part series is aimed at illustrating real practical applications in drug development using case studies of how such ideas can provide valuable understanding of the effects of treatments in the presence of intercurrent events or where effects may be mediated by intermediate factors.

Estimating Treatment Effects in Patients Who Adhere to Treatment
Stephen Ruberg (Analytix thinking) / Yongming Qu (Eli Lilly)
The estimation of treatment effects has traditionally been based on the value of randomization and the causal inference it confers. However, causal inference from randomized controlled trials requires that all patients be analyzed as randomized AND, importantly, that all patients be followed for the duration of the trial and the primary outcome measured. Since many large or long-term trials involve patients who discontinue the study or discontinue their study treatment, this approach – often called intent-to-treat (ITT) – actually becomes an estimate of the effect of initiating (or being assigned) a treatment and NOT the effect of actually taking the treatment, which we call the direct treatment effect. An alternative approach is to censor the data from the time of treatment deviation and impute the resulting missing values (e.g., a hypothetical strategy). This approach uses all randomized patients but requires strong assumption on the potential outcome after the deviation away from the randomized treatment. While ICH-E9 recommended the ITT approach in general (or at least the use of all randomized patients in the analysis), ICH-E9(R1) has opened the door to other possible estimands and strategies for estimating a treatment effect. One such alternative is the direct treatment effect in patients (principal stratum) who actually would take/adhere to a treatment (Adherers Average Causal Effect – AdACE). This lecture will be divided into two parts: the first will motivate why such an estimand is of major importance, and the second will provide technical details on its estimation using causal inference methods. Examples will be given to highlight the methods, the code needed, and the interpretation of such the AdACE estimate.

Comparative safety analysis of time-varying exposures in post marketing observational studies
Sean Yiu (Roche)
Health authorities often mandate license holders of approved treatments to conduct post marketing observational studies to sufficiently assess long-term risk of important safety events, e.g. malignancies, since randomized clinical trials are typically too short and underpowered to detect treatment effects on such events. Furthermore, comparative safety analysis of newly approved versus other already approved treatments may be requested as part of the post marketing requirement. However, performing comparative safety analysis of long-term observational studies where treatment assignment is based on clinical practice is challenging and not well established in the regulatory setting, particularly when treatment switching (from control to active and vice versa) is anticipated to be frequent and often occurs prior to safety events of interest. Using a case study for OCREVUS, which is an approved treatment for adult patients with relapsing or primary progressive forms of multiple sclerosis, I will describe one specific post marketing requirement from the FDA on comparative safety analysis, the challenges of performing such analyses in the presence of multiple treatment switching, and highlight severe limitations of conventional methods based on time fixed treatments. I will then describe how established methodology for drawing causal inferences for the effects of time-varying exposures in the presence of time-dependent confounding, e.g. marginal structural Cox models, can address limitations of the conventional methods, and provide feedback from the FDA on the use of causal inference methodology in this observational setting.


Mediation analysis for a cardiovascular outcome trial
Martin Linder (Novo Nordisk)
There is a growing interest in statistical analyses that can answer questions concerning how a drug may affect an outcome via intermediate variables (mediators). The LEADER trial is an example. The trial showed a beneficial effect of the drug liraglutide on cardiovascular outcome in people with type 2 diabetes and high cardiovascular risk. Key opinion leaders as well as regulatory agencies asked whether the effect on cardiovascular outcome could be explained by previously known effects of liraglutide on blood glucose levels or body weight. The question is best answered within the framework of causal inference which provides methods for statistical analysis but also clarifies the assumptions necessary for a meaningful interpretation of the results.

In this presentation, we will consider some selected methods for causal mediation analysis that will be applied to the LEADER data. The methods include an approach developed jointly with experts from academia which specifically handles the case where the outcome is a time-to-event variable and the mediator is repeatedly measured.

 

13 December 2023

The last Wonderful Wednesday of 2023 is presented by Bodo Kirsch. It is all about visuals giving an overview of the demographic data of a study population. Visualisations are available on the Wonderful Wednesday blog.

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The last Wonderful Wednesday of 2023 is presented by Bodo Kirsch. It is all about visuals giving an overview of the demographic data of a study population. Visualisations are available on the Wonderful Wednesday blog

Density plots make it easy spot abnormal distribution findings. Animated those can be used to explore different subgroups. Scatterplots combined in a trellis plot show the relationship of the demographic characteristics. Combining this with density plots and additional statistical measures like correlations gives a really comprehensive overview. The next challenge is looking for the best visuals of 2023. See the Wonderful Wednesday homepage for more detail.

Wonderful Wednesdays are brought to you by the Visualisation SIG. The Wonderful Wednesday team includes: Bodo Kirsch, Alexander Schacht, Mark Baillie, Zachary Skrivanek, Lorenz Uhlmann, Rachel Phillips, David Carr, Steve Mallett, Rhys Warham, Lovemore Gakava, Zara Sari, Paolo Eusebi, Martin Brown, Martin Karpefors, Benjamin Lang, Elias Laurin Meyer

12 December 2023

In a world buzzing with artificial intelligence, can you distinguish between what’s AI-generated and what’s not? It’s time to put your intuition to the test in a fun and interactive quiz! This session showcases what AI can and cannot do (yet), challenging your perceptions, and luring you into the fascinating world of AI technology.

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Hosted by Claire Britain and Chetan Mistry, for this session we're joined by:  Ursula Becker, Kimberley Hacquoil, Paul Terrill

In a world buzzing with artificial intelligence, can you distinguish between what’s AI-generated and what’s not? It’s time to put your intuition to the test in a fun and interactive quiz! This session showcases what AI can and cannot do (yet), challenging your perceptions, and luring you into the fascinating world of AI technology.  As always, our fantastic panel is on hand with commentary throughout to share their perspectives. 

This session requires no prior knowledge and will be a learning opportunity for those less familiar with AI. It is a fun opportunity to learn more about AI capabilities, challenge yourself and have a blast!

07 December 2023

This webinar brought together renowned experts in their respective fields, who provided an introduction to three concepts, illustrated with examples, and discussed their commonalities and differences.

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Arthur Allignol, Antonio Remiro-Azócar,  Robert Hemmings, and Nicholas Latimer.

Over the past years, areas of clinical development and patient access / health technology assessment (HTA) have moved closer together. However, both areas differ in how they approach their respective research or policy questions. While the estimand framework is being the focus in clinical development, HTA is being viewed through the lens of the PICO framework. This might lead to mutual misunderstandings and ultimately prevent timely patient access due to evidence gaps. Additionally, the Target Trial Emulation framework has become popular in the Real-World Data setting, and shares some commonalities with the estimand and PICO frameworks. This webinar brought together renowned experts in their respective fields, who provided an introduction to these three concepts, illustrated with examples, and discussed their commonalities and differences.

06 December 2023

Causal Inference in Clinical Trials: To understand the potential of the practical application of causal inference methods in drug development and be able to apply these to real world problems or clinical trials.

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Kelly van Lancker, Ilya Lipkovich, Martin Ho, Alex Ocampo

Causal Inference in Clinical Trials: To understand the potential of the practical application of causal inference methods in drug development and be able to apply these to real world problems or clinical trials.

As the first installment of this two-part webinar series, this first webinar will provide an introduction to causal inference ideas and methods and how these relate to the estimand framework in both the setting of RCTs or real world data. Graphical methods for communicating causal networks will also be outlined. Please see below the Abstracts for each Guest Presentation. 


The role of causal inference in clinical trials: an introduction
Kelly van Lancker (Ghent University)
In light of the recently published ICH E9(R1) guideline on estimands and sensitivity analysis (2019) and the FDA draft guideline on covariate adjustment (2023), causal inference is progressively taking a more prominent role in the landscape of global drug development. In this talk we will try to bridge the gap between communities by elaborating on how this field provides a convenient, unifying framework, language and relevant tools to formally establish causal relationships. We will hereby illustrate how causal thinking, combined with important tools such as potential outcomes, can facilitate defining, identifying and estimating treatment effects. Building on this, we discuss the role of causal inference in different trial settings, including targeting intention-to-treat effects with covariate adjustment, handling intercurrent events and the incorporation of external control data.

Causal inference and estimands in clinical trials
Ilya Lipkovich (Eli Lilly)
This presentation revisits recent ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials and discusses various strategies for handling intercurrent events (ICEs) using the causal inference framework. The language of potential outcomes (PO) is widely accepted in the causal inference literature but is not yet recognized in the clinical trial community and was not used in defining causal estimands in ICH E9(R1). I will try to bridge the gap between the causal inference community and clinical trialists by advancing the use of causal estimands in clinical trial settings and illustrate how concepts from causal literature, such as POs and dynamic treatment regimens, can facilitate defining and implementing causal estimands for different types of outcomes providing a unifying language for both observational and randomized clinical trials. I emphasize the need for a mix of strategies in handling different types of ICEs, rather than taking one-strategy-fit-all approach and suggest that hypothetical strategies should be used more broadly and provide examples of different hypothetical strategies for different types of ICEs.

A Causal Inference Roadmap for Generating RWE in Regulatory Context: An Introduction and Illustration
Martin Ho (Pfizer)
As real-world data (RWD) become more readily available, the regulatory agencies, medical product developers, and other key stakeholders has increasing interests in exploring the use of real-world evidence (RWE) to support regulatory decisions alternative to traditional clinical trials. To facilitate and promote statistical research in design, analysis, and interpretation of RWE studies for regulatory decision making, the ASA Biopharmaceutical Section established the RWE Scientific Working Group to address challenges and identify opportunities in the statistical research of this area. In a Working Group publication in 2022, Ho and colleagues (DOI 10.1080/19466315.2021.1883475) have proposed a causal inference roadmap for study design and analysis that generates RWE for regulatory considerations. In this talk, Martin will briefly review the steps of the roadmap before using an example to illustrate how to apply the roadmap to generate RWE for regulatory consideration.

Single-World Intervention Graphs for Defining, Identifying, and Communicating Estimands in Clinical Trials
Alex O'Campo (Novartis)
Confusion often arises when attempting to articulate target estimand(s) of a clinical trial in plain language. We aim to rectify this confusion by using a type of causal graph called the Single-World Intervention Graph (SWIG) to provide a visual representation of the estimand that can be effectively communicated to interdisciplinary stakeholders. These graphs not only display estimands, but also illustrate the assumptions under which a causal estimand is identifiable by presenting the graphical relationships between the treatment, intercurrent events, and clinical outcomes. To demonstrate its usefulness in pharmaceutical research, we present examples of SWIGs for various intercurrent event strategies specified in the ICH E9(R1) addendum, as well as an example from a real-world clinical trial for chronic pain. LaTeX code to generate all the SWIGs shown is this paper is made available. We advocate clinical trialists adopt the use of SWIGs in their estimand discussions during the planning stages of their studies.

16 November 2023

PSI Webinar of the European Biomarkers European Special Interest Group: A promising adaptive biomarker-based design strategy for early phase and machine learning as an enabler of precision medicine!

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Guillaume Desachy, Alessandra Serra, Sandrine Guilleminot, Nicole Krämer, Karl Köchert, Nils Ternès.

PSI Webinar of the European Biomarkers European Special Interest Group: A promising adaptive biomarker-based design strategy for early phase and machine learning as an enabler of precision medicine!

Presentation 1: A promising adaptive biomarker-based design strategy for early phase clinical trials
Identifying predictive biomarkers is crucial in patient-centric clinical development. Enrichment strategies in late stages of drug development have been widely studied in the literature. However, implementing these strategies in early stages presents significant challenges due to the small sample size and numerous uncertainties that arise at this point in the development process. These uncertainties encompass biomarker (BMK) predictive value, cutoff value of the biomarker used to identify patients in the BMK-positive subgroup, the proportion of patients in the BMK-positive subgroup and the magnitude of the treatment effect in patients BMK-positive and BMK-negative. Early phase adaptive designs can improve trial efficiency by allowing for adaptions during the course of the trial. In this work, we are interested in adaptations based on interim analysis permitting a refinement of the existing study population according to their predictive biomarkers. Simulations show that the proposed design leads to better decision-making compared to a classical design that does not consider an enrichment expansion. Specifically, in the considered settings, gains up to 30% in the overall probability to hit the study success criteria at the end of the trial were achieved in comparison to a conventional design. 

Presentation 2: Machine Learning as an enabler of precision medicine
Since the re-creation of the Biomarkers ESIG in 2022, people from different horizons and job titles have expressed some interest in machine learning (ML) and artificial intelligence (AI) methods applied to drug development, from research to clinical. With this fact and the increasing interest in ML/AI from the scientific community and beyond, a group of a dozen people was set-up in the past months with the ambition to create a cross-company best practices guidance on the use of AI/ML in drug development. The group started with some proactive joint discussions to better understand each other’s interest in this area and has already contributed to the review and feedback to FDA’s recent discussion paper on usage of AI/ML in drug development. Now, the group is in a highly dynamic ideation phase to identify and choose topics which have the potential to impact drug development, e.g., virtual twin (VT) technology, GxP compliant AI/ML, use of image-based or digital biomarkers.

09 November 2023

Kristian Brock (AstraZeneca) and Pater Thall (MD Anderson) present their recent work on Bayesian designs in efficacy and safety with discussions led by Sebastian Weber.

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Kristian Brock (AstraZeneca) and Pater Thall (MD Anderson) present their recent work on Bayesian designs in efficacy and safety with discussions led by Sebastian Weber.

Presenters & Papers: 
1. Kristian Brock, Chen Chen, Shuyen Ho, Greg Fuller, Jared Woolfolk, Cindy McShea, Nils Penard: 'A Bayesian method for safety signal detection in ongoing blinded randomised controlled trials' 
https://onlinelibrary.wiley.com/doi/10.1002/pst.2278

2. Yun Qing, Peter F. Thall, Ying Yuan: 'A Bayesian piecewise exponential phase II design for monitoring a time-to-event endpoint' 
https://onlinelibrary.wiley.com/doi/10.1002/pst.2256

08 November 2023

Steve Mallet is presenting ways to visualise a complex testing strategy. In the example of nine hypotheses in three families interactive plots help to make educated design decisions. Visualisations are available on the Wonderful Wednesday blog.

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Steve Mallet is presenting ways to visualise a complex testing strategy. In the example of nine hypotheses in three families interactive plots help to make educated design decisions. Visualisations are available on the Wonderful Wednesday blog

The display of multiple dependencies can be handled with trellis plots. Three dimensional plots are supported by plotly. All options can be explored in an interactive facet plot embedded in a shiny app. The next challenge is on displaying outliers in demographic data. See the Wonderful Wednesday homepage for more detail.

Wonderful Wednesdays are brought to you by the Visualisation SIG. The Wonderful Wednesday team includes: Bodo Kirsch, Alexander Schacht, Mark Baillie, Zachary Skrivanek, Lorenz Uhlmann, Rachel Phillips, David Carr, Steve Mallett, Rhys Warham, Lovemore Gakava, Zara Sari, Paolo Eusebi, Martin Brown, Martin Karpefors, Benjamin Lang.

26 October 2023

Dr Huang shares her experience in extending/innovating methodology and concepts developed in vaccine statistics to the analyses of pre-efficacy and efficacy trials of monoclonal antibodies.

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Yunda Huang, Dean Follmann and Fabian Tibaldi 

Dr Huang shares her experience in extending/innovating methodology and concepts developed in vaccine statistics to the analyses of pre-efficacy and efficacy trials of monoclonal antibodies.

19 October 2023

The meeting will consider the importance of patient-centric input into regulatory and HTA decision-making discussing the latest developments in the field and presenting industry examples. Speakers from EMA, the pharmaceutical industry, and those working with HTAs and patients will give their perspective on the importance and practical aspects of selecting relevant for patients outcomes in the evaluation of clinical trials.

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Dr. Byron Jones, Dr. Brett Hauber, Dr. Bennett Levitan, Dr. Christine Sturchler, Dr. Hannah Penton, Dr. Marco Boeri, Teodora Trasieva.

Day 2 of the patient-focused drug development meeting presented 3 industry case studies and finished with a panel discussion:
“The importance of different symptoms to people living with Chronic Obstructive Pulmonary Disease (COPD): results of a multi-country patient preference study. Industry case (Novartis)”, presented by Dr. Byron Jones, Biostatistician Specialising in Patient Preference Studies in the Patient Engagement Science group at Novartis

“Using Patient Preferences to Inform Dose Selection”, presented by Dr. Brett Hauber, Senior Director, Patient Preferences in Worldwide Medical and Safety of Pfizer

“The relevance of fatigue to relapse rate in multiple sclerosis: Applying patient preference data to the OPTIMUM trial”, presented by Dr. Bennett Levitan, Senior Director, Benefit-risk Assessment, Department of Epidemiology at Johnson & Johnson

Panel discussion, including Dr. Byron Jones, Dr. Brett Hauber, Dr. Bennett Levitan, Dr. Christine Sturchler, Dr. Hannah Penton, Dr. Marco Boeri.

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