If you’ve ever wondered what adaptive designs really are, when they make sense, and how ICH E20 will influence our work as statisticians, this episode will give you a clear, practical overview.
You’ll learn:

✔ Why adaptive designs often save valuable time—and what organizational barriers keep teams from using them.

✔ What types of adaptations are possible and truly useful in confirmatory settings.

✔ How combining evidence across study stages works in principle.

01:28 – Catching up with Kaspar
Kaspar returns to the podcast to dive into the topic of adaptive clinical trials.

02:34 – Why adapt?
We discuss the main motivation behind adapting a trial and when it’s worth the effort.

03:00 – Group-sequential designs
A quick look back at where adaptive concepts began and why they remain relevant.

06:03 – Practical adaptations
We touch on examples of adaptations that can make studies more flexible and efficient.

10:00 – Planning challenges
Kaspar shares how real-world constraints shape decisions around adaptive design.

15:06 – Why not more often?
We reflect on the cultural and operational reasons these designs are still less common than expected.

25:30 – ICH E20
An overview of what the new guideline covers and why statisticians should pay attention.

27:13 – Looking ahead
I share upcoming opportunities to continue this discussion at industry meetings in Basel.

29:13 – Closing thoughts
A reminder about the value of good planning and purposeful adaptation in clinical trials.

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✔ Hear my personal reflections on 456 episodes and the evolution of this podcast.

✔ Learn a simple, values-based view of leadership that applies no matter your level.

✔ Discover how to influence people—not departments—and build trust.

✔ See why contextual teaching beats generic “Stats 101” courses.

✔ Walk away with three immediate actions: decide to lead, listen deeply, and invest in your presentation skills.

00:00 – Why Alun is interviewing me for Episode 456

01:57 – What counts as an “episode” and why this milestone matters

03:03 – From estimands to blurred lines across stats/data science

06:10 – My view of leadership: helping others accomplish something

08:08 – Values, purpose, and the “win–win” principle

10:09 – Goal-driven meetings and tying them to vision and values

12:44 – Why you can’t influence a department—you influence people

15:47 – Trust = character × competence × care (as others perceive it)

17:16 – Being known: why personal and departmental branding matters

19:00 – How targeted training builds credibility and influence

23:00 – Presentation skills as a multiplier for all other communication

28:34 – Listening: the most underrated leadership skill

33:00 – My three practical actions to apply this week

35:30 – Closing thoughts and invitation to connect

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🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

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f you’ve ever wondered whether single-arm studies are “good enough” for regulators or HTA bodies, this episode will challenge your assumptions. Anja Schiel, one of Europe’s leading voices at the regulator–HTA interface, explains why comparisons matter, where single-arm designs break down, and what smarter alternatives exist.

You’ll walk away with:

✔ A clearer understanding of the limits of single-arm trials

✔ Practical strategies for choosing comparators that strengthen your evidence package

✔ Insight into adaptive and hybrid designs that balance efficiency with rigor

✔ A regulator/HTA insider’s perspective on what decision-makers really need from statisticians

00:01:36 – Meet Anja Schiel and learn how she bridges regulation and HTA

00:02:28 – Why connecting regulators and HTA bodies early matters for drug development

00:06:07 – Where single-arm studies often fall short in real-world decision-making

00:08:01 – What accelerated approvals have taught us about assumptions in trial design

00:10:17 – Why comparison—not just randomization—is at the heart of sound evidence

00:12:12 – The pitfalls of relying on literature-based or naïve comparisons

00:16:54 – How regulators and HTA approach evidence differently

00:17:00 – What “concurrent control” really means and why it’s crucial

00:20:33 – Strategic thinking when selecting comparators for long-term value

00:26:00 – The role of adaptive and hybrid designs in modern trials

00:33:59 – Ethical considerations when trial designs fall short

00:36:26 – Why rare diseases demand smarter collaboration and evidence planning

00:40:25 – Communicating study objectives and estimands clearly for all stakeholders

00:44:55 – Final takeaways: how statisticians can lead the push for better designs

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 Medical Data Leaders Community – Join my network of statisticians and data leaders to enhance your influencing skills.

🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

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Working with physicians isn’t always easy. Different mindsets, expectations, and communication styles can get in the way. In this episode, you’ll hear how to:

✔ Build trust and respect with physicians in pharma

✔ Communicate effectively across disciplines

✔ Know when to support, when to push back, and how to be seen as a partner

[01:28] Introducing the topic of working with physicians in pharma

[02:27] Seeing physicians as colleagues, not customers

[04:53] Learning to speak each other’s language

[06:26] Cultural challenges for physicians moving from hospitals into pharma

[10:59] Approaching discussions with a partnership mindset

[12:59] Why involving statisticians early leads to smoother studies

[15:18] Strategies for handling disagreements constructively

[19:08] The p-value debate and knowing when to push back

[24:55] Explaining outputs so physicians (and beyond) can understand

[25:26] The idea of having a physician mentor

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

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🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

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✔ EU HTA is becoming reality: Joint Clinical Assessments begin soon with oncology/ATMPs and will expand to all medicines over the next years.

✔ Statisticians are central: Re-analyses, indirect comparisons, RWE, and quality-of-life analyses will be required—often beyond what regulatory trials were designed for.

✔ Timelines are tight: From EMA Day 120 scoping to dossier deadlines and final JCAs just 30 days post-marketing authorization.

✔ Transparency and resources matter: Joint assessments will be public, and both companies and agencies face capacity and clarity challenges.

✔ You can prepare now: Incorporate HTA needs into trial design, analysis planning, and cross-functional collaborations.

00:00 – 02:30 | I introduce the episode and explain why EU HTA is such a critical topic

02:30 – 05:30 | Lara and Anders introduce themselves and their HTA work at MSD and Novo Nordisk

05:30 – 10:45 | Regulatory vs. HTA: safe & effective vs. how good, for whom, and at what cost

10:45 – 18:30 | Europe’s patchwork: national differences in comparators, standards of care, and access

18:30 – 23:45 | The EU regulation: joint clinical assessments, economic modeling, and what’s changing

23:45 – 32:30 | What it means for us as statisticians: re-analyses, ITCs/NMAs, RWE, QoL, and capacity issues

32:30 – 36:00 | Why “transparency” can’t just be 50,000-page PDFs—clear, reproducible evidence matters

36:00 – 45:00 | The PSI HTA SIG’s role, current activities, and how you can get involved

45:00 – end | Our final takeaways and a call for statisticians to engage now

🔗 Join the PSI HTA Special Interest Group and watch for their newsletter and training.

🔗 Review EUnetHTA 21 methodological drafts—they are shaping the future of JCAs.

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 Medical Data Leaders Community – Join my network of statisticians and data leaders to enhance your influencing skills.

🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

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HTA – Health Technology Assessment

EU HTA / JCA – Joint Clinical Assessment forming the evidence base for national HTA decisions

EMA / CHMP – European Medicines Agency / Committee for Medicinal Products for Human Use

RWE – Real-World Evidence; NMA/ITC – Network/Indirect Treatment Comparison

QoL/HRQoL – (Health-Related) Quality of Life measures

PSI HTA SIG – PSI Special Interest Group on HTA

EFPIA – European Federation of Pharmaceutical Industries and Associations

✔ Why analyzing adverse events differently from efficacy endpoints creates problems.

✔ How differing follow-up times and censoring bias AE results.

✔ The role of the Aalen–Johansen estimator and why it should be standard practice.

✔ What the SAVVY collaboration achieved by uniting pharma, academia, and regulators.

✔ Real-world examples of how safety analyses can dramatically change the interpretation of treatment risk.

✔ Lessons on collaboration, methodology, and change management in the pharma industry.

Adverse events are a critical part of any trial, yet they’re often analyzed using simplistic methods that can mislead decision-makers. This episode will help you:

Gain insights you can apply immediately to your own projects to improve the accuracy and credibility of your analyses.

Understand the hidden biases in traditional AE analysis.

Learn how to align safety and efficacy assessments for a fairer benefit–risk evaluation.

Discover the power of collaboration between pharma, academia, and regulators through the SAVVY project.

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 Medical Data Leaders Community – Join my network of statisticians and data leaders to enhance your influencing skills.

🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

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✔ How Thomas and Sam were first introduced to SAS and R — and how their early experiences shaped their preferences.

✔ Key differences in learning curves and the resources available for beginners.

✔ How each tool fares in day-to-day work and long-term maintainability.

✔ Strengths and weaknesses of SAS and R communities — and the events, forums, and support structures that keep them thriving.

✔ The impact of cost, licensing, and open-source vs proprietary models on adoption.

✔ How both tools handle data visualization and producing publication-quality graphics.

✔ Regulatory acceptance: How far R has come in being used for submissions to agencies like the FDA — and what’s still needed for broader acceptance.

✔ Why your choice of tool might also depend on generational trends and the skillsets of new talent entering the field.

This isn’t just a technical comparison — it’s a candid, practical discussion based on real-world pharmaceutical research experience. You’ll hear about cultural differences between the SAS and R worlds, the business factors that influence adoption, and the ways companies are moving toward hybrid environments where both can thrive.

If you’re making decisions about tools for your team or career, this episode will help you navigate the trade-offs with eyes wide open.

🔗 Thomas' Adventure Blog

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🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

If you’re working on evidence generation plans or preparing for joint clinical advice, this episode is packed with insights you don’t want to miss.

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✔ How to recognize when your “chimp” is in control — and what to do about it

✔ The difference between emotional, rational, and programmed brain responses

✔ How to manage anxiety and fear in high-stakes situations like meetings and presentations

✔ Why we often overwork out of tribal guilt — and how to break that cycle

✔ How false beliefs can be reprogrammed — just like changing default settings in a computer

✔ Why rest, reflection, and celebration are essential for performance

Do you ever freeze in meetings, hold back from sharing your opinion, or overwork out of guilt or fear of letting others down? You’re not alone — and there’s a reason why.

This episode will help you:

Whether you’re presenting to leadership, leading a project, or just navigating stress — The Chimp Paradox can equip you with the tools to respond more intentionally and perform at your best.

✔ How two leading statisticians transitioned into data science

✔ The key differences (and overlaps) between data science, statistics, big data, and machine learning

✔ Why data science is more than hype—and why statisticians are needed more than ever

✔ The role of visualization and statistical learning in interpreting high-dimensional biomedical data

✔ Real-world applications of data science in biomarker discovery, precision medicine, and pharmacovigilance

✔ What makes data science in pharma different from tech giants like Google or Amazon

✔ Tips for statisticians who want to get started in data science

If you're a statistician wondering whether data science is your next career step—or simply curious about how the two fields intersect—this episode offers an honest, expert-led exploration. Yannis and Rajat pull back the curtain on what data science really involves, how it's transforming pharma and healthcare, and what skills and mindset statisticians can bring to this evolving space.

🔗 Explore Cytel’s data science insights and case studies

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🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

If you’re working on evidence generation plans or preparing for joint clinical advice, this episode is packed with insights you don’t want to miss.

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✔ Why “index date” is more complicated than it sounds

✔ Common mistakes around exposure definitions

✔ The importance of understanding how RWE data is generated

✔ What programmers should know about timing, variables, and algorithms

✔ Why project management in RWE must be iterative and stakeholder-driven

✔ Key terminology pitfalls that can trip up even experienced professionals

✔ How data issues like duplicates, implausible values, and partial dates impact analyses

✔ Best practices for communication, timelines, and managing expectations in RWE projects

If you're working in real-world evidence or thinking of transitioning from clinical trials, this episode is packed with practical advice to help you avoid common mistakes and set your projects up for success. Rachel shares from her own hands-on experience—starting from data management and programming to leading statistical analyses in RWE. Alexander and Rachel also highlight real-world data quirks that no textbook prepares you for, making this episode an essential resource for statisticians, data scientists, and healthcare researchers alike.

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 Medical Data Leaders Community – Join my network of statisticians and data leaders to enhance your influencing skills.

🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

If you’re working on evidence generation plans or preparing for joint clinical advice, this episode is packed with insights you don’t want to miss.

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