Matthew Lyon, Ari Siggaard Knoph, Daniel Sabanes-Bove

Talks from the speakers will cover the use of R in a programming community, submitted to regulators using R, and also programming beyond R in C++ and Julia.

Sample size and power calculations are an important task for statisticians at the planning stage of a trial. Steve Mallet presents powerful visualisations to explain the impact of change in assumptions to a "non-technical" audience. Visualisations are available on the Wonderful Wednesday blog.

A small shiny app is helping to explain the concept of type I and type II errors. Depending on the actual question Statulator plots, trellis plots or even 3D plots can be used to support the argumentation. This concept casn also be used for Bayesian methods. The next challenge is on study flow charts. 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.

Peter Goos

The family of orthogonal minimally aliased response surface designs or OMARS designs bridges the gap between small definitive screening designs and classical response surface designs, such as central composite and Box-Behnken designs. The initial OMARS designs involve three levels per factor and large numbers of quantitative factors to be studied efficiently using limited numbers of experimental tests. Many of the OMARS design possess good projection properties and offer better powers for quadratic effects than definitive screening designs with similar numbers of runs. Therefore, OMARS designs offer the possibility to perform a screening experiment and a response surface experiment in a single step, and thereby offer the opportunity to speed up innovation and process improvement. A technical feature of the initial OMARS designs is that they study every quantitative factor at its middle level the same number of times. In this talk, we relax this constraint and arrange the designs in blocks, and thereby broaden the family of OMARS designs, presenting application of OMARS designs in pharmaceutical and chemical industries. 

Min-Hua Jen, Kate Ren, Andrew Thomson, Marcia Rueckbeil and Richard Sizelove

Randomised controlled trials (RCTs) have been the gold standard for the evaluation of efficacy and safety of medical interventions. However, investigators have incentives to look for alternative ways to obtain relevant comparative effect estimates more rapidly for healthcare decision makers. The EUnetHTA 21 methodological guidelines will be officially finalised late in 2024 and will impact the statistical approaches used to obtain these effect estimates. Indirect treatment comparisons (ITCs) for the joint clinical assessment (JCA) require the estimation of comparative effectiveness via non-randomised designs.    Statisticians must be prepared for the changes this guidance will require. Indirect treatment comparisons should be considered a fundamental tool in the pharmaceutical statistician's toolbox, alongside other commonly used statistical techniques in the industry.  This webinar aims to provide information on the purpose and methods for ITC. The speakers will review and compare different approaches for ITC in both anchored and unanchored case. Both drug regulatory and reimbursement body’s views on using ITC will be discussed. The speakers will offer insights into issues such as marginal and conditional effect, unmeasured confounding and provide case studies to demonstrate the use of ITC methods in practice.  The event will be structured as two webinars, each of 3 hours. The first webinar will discuss ITC for an anchored case. The second webinar will discuss ITC for an unanchored case. You do not have to attend both webinars, but we highly recommend you join both webinars to have a comprehensive understand of the topic in ITC.  

This Wonderful Wednesday was created in collaboration with the Biomarkers SIG. Rhys Warham is presenting the proposed plots. Visualisations are available on the Wonderful Wednesday blog.

Special guests Mathilde Saccareau and Guillaume Desachy are joining the panel to discuss pros and cons of different visualisations. To be displayed is data of vaginal microbiom profiles over time comparing pre-term birth and term birth. Proposed were donut charts and raincloud charts as well as specific T-SNE and UMAP plots, The next challenge is an open challenge on displaying sample size and power calculations to a "non-technical" audience. 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

Min-Hua Jen, Antonio Remiro-Azocar, Anja Schiel, Suzy Van Sanden

Randomised controlled trials (RCTs) have been the gold standard for the evaluation of efficacy and safety of medical interventions. However, investigators have incentives to look for alternative ways to obtain relevant comparative effect estimates more rapidly for healthcare decision makers. The EUnetHTA 21 methodological guidelines will be officially finalised late in 2024 and will impact the statistical approaches used to obtain these effect estimates. Indirect treatment comparisons (ITCs) for the joint clinical assessment (JCA) require the estimation of comparative effectiveness via non-randomised designs.

Statisticians must be prepared for the changes this guidance will require. Indirect treatment comparisons should be considered a fundamental tool in the pharmaceutical statistician's toolbox, alongside other commonly used statistical techniques in the industry.

This webinar aims to provide information on the purpose and methods for ITC. The speakers will review and compare different approaches for ITC in both anchored and unanchored case. Both drug regulatory and reimbursement body’s views on using ITC will be discussed. The speakers will offer insights into issues such as marginal and conditional effect, unmeasured confounding and provide case studies to demonstrate the use of ITC methods in practice. 

Day 1, 26th February 2024: Indirect treatment comparison for anchored setting

• Antonio Remiro-Azocar (Bayer) - Marginal versus conditional estimands, non-collapsibility and standardization in the context of indirect treatment comparisons
• Anja Schiel (Norwegian Medical Products Agency (NoMA) - ITC’s applicability and acceptability in the European HTA landscape
• Suzy Van Sanden (Johnson & Johnson) - Matching-adjusted indirect comparison (MAIC): A case studies in prostate cancer
• Panel discussion

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

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

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.

 

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

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!

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.