The Effective Statistician - in association with PSI

A conversation with Kaspar Rufibach Why You Should Listen: ✔ By the end of this episode, you’ll: ✔ Pick up concrete steps to improve your presentation skills over time: feedback, recordings, formal training, and deliberate practice. ✔ See why communication is leadership – and why “you can’t lead if you can’t communicate” really applies to statisticians. ✔ Learn how to start a presentation: from a blank sheet to a clear set of 2–4 key messages your audience will actually remember. ✔ Rethink slide design so your slides support you—instead of becoming an information dump that competes with your voice. ✔ Understand the crucial difference between academic talks (“what I did”) and business presentations (“what we should do now and why”). Get practical ideas to prepare earlier, present shorter, and focus on what your audience truly needs to hear. Episode highlights with timestamps [00:02:00] Why presentations matter more than you thinkKaspar shares why statisticians can’t “hide behind the numbers” if they want real impact. [00:03:00] How Kaspar actually starts a talkFrom a blank sheet of paper to a small set of key messages. [00:07:45] The problem with crowded slidesWhy most slide decks try to do the wrong job—and what Kaspar does instead. [00:14:00] Presentations, reputation, and wasted timeHow the way you present shapes your personal brand with colleagues and leaders. [00:18:00] When leaders don’t care about your methodsThe shift from “here’s what I did” to “here’s what we should do.” [00:20:45] Starting with your conclusionHow leading with recommendations can save you when your time is suddenly cut. [00:23:30] Simple habits to become a better presenterSmall changes Kaspar says you can start using in your next talk. [00:29:00] Dealing with fear and staying authenticWhy there are no “natural born presenters” and what really helps you improve. [00:33:00] Kaspar’s three core principlesThe key ideas he hopes every statistician remembers after this episode. Links: 🔗 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. Join the Conversation:Did you find this episode helpful? Share it with your colleagues and let me know your thoughts! Connect with me on LinkedIn and be part of the discussion. Subscribe & Stay Updated:Never miss an episode! Subscribe to The Effective Statistician on your favorite podcast platform and continue growing your influence as a statistician.

A conversation with Deepa Jahagirdar Why Listen ✔ You want a clearer understanding of when and why ECAs make sense. ✔ You’re dealing with real-world data and need a practical framework for selecting the right source. ✔ You’ve heard the term target trial emulation, but want to understand how it’s applied in real projects. ✔ You want to strengthen the causal credibility of your studies without relying solely on randomized trials. ✔ You want simple, actionable principles for handling confounding and unmeasured bias. Episode Highlights: [00:00] – Setting the stageI introduce the topic of external control arms and why they’re more widely relevant than many statisticians think. [01:35] – Introducing DeepaDeepa shares her path from social epidemiology into designing and supporting ECA studies at Cytel. [03:00] – Why ECAs are fascinatingWe talk about how methods used to study policies without RCTs translate into clinical research. [04:00] – Where ECAs show upI walk through common scenarios—from rare diseases to extension studies—where external controls add value. [07:30] – Choosing the right real-world dataDeepa explains how she approaches data selection depending on disease, outcomes, and feasibility. [10:20] – Target trial emulationWe discuss how designing the “ideal RCT” guides everything that follows when constructing an ECA. [16:30] – Handling confoundingDeepa explains the role of expert knowledge, DAGs, and standard adjustment approaches. [21:20] – Thinking about unmeasured confoundingWe talk about assessing robustness and understanding how much bias it would take to overturn your results. [24:20] – Final takeawaysDeepa highlights the importance of focusing on the big causal question and overall robustness—not perfection. Links: 🔗 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. Join the Conversation:Did you find this episode helpful? Share it with your colleagues and let me know your thoughts! Connect with me on LinkedIn and be part of the discussion. Subscribe & Stay Updated:Never miss an episode! Subscribe to The Effective Statistician on your favorite podcast platform and continue growing your influence as a statistician. **Episode Highlights: ** [00:00] – Setting the stage I introduce the topic of external control arms and why they’re more widely relevant than many statisticians think. [01:35] – Introducing Deepa Deepa shares her path from social epidemiology into designing and supporting ECA studies at Cytel. [03:00] – Why ECAs are fascinating We talk about how methods used to study policies without RCTs translate into clinical research. [04:00] – Where ECAs show up I walk through common scenarios—from rare diseases to extension studies—where external controls add value. [07:30] – Choosing the right real-world data Deepa explains how she approaches data selection depending on disease, outcomes, and feasibility. [10:20] – Target trial emulation We discuss how designing the “ideal RCT” guides everything that follows when constructing an ECA. [16:30] – Handling confounding Deepa explains the role of expert knowledge, DAGs, and standard adjustment approaches. [21:20] – Thinking about unmeasured confounding We talk about assessing robustness and understanding how much bias it would take to overturn your results. [24:20] – Final takeaways

Frank Konietschke, a Professor of statistics with expertise in non-parametric methods, dismantles the myth that non-parametric means just Wilcoxon tests. He explores a broad toolkit for analyzing skewed data, outliers, and small samples. Learn how ranks can quantify the relative treatment effect without relying on means, and discover effective ways to present results using confidence intervals and visuals. Frank also shares valuable software tools for implementing rank-based models, ensuring you don't miss the innovative strategies available for robust statistical analysis.

In this engaging discussion, Kaspar Rufibach, an experienced biostatistician and authority on adaptive clinical trials, shares his insights. He explains the challenges of communicating results from adaptive studies and emphasizes the importance of using clear, defensible language. Listeners learn about the difference between conditional and unconditional bias, the implications for point estimates, and the significance of median-unbiased estimation. Kaspar also addresses the complexities surrounding secondary endpoints and the necessity of pre-specifying adjustments to ensure trust and reproducibility.

In this discussion, Kaspar Rufibach, an experienced biostatistician and adaptive clinical trial expert, delves into the practicalities of adaptive designs and the ICH E20 guideline. He explains why these designs save time and the types of meaningful adaptations available for drug development. Kaspar also addresses the operational barriers preventing wider use of adaptive trials and emphasizes the importance of understanding their implications on endpoints and inference. Lastly, he outlines the key points of the new ICH E20 guideline that statisticians should note.

A conversation with Alexander Schacht and Alun Bedding Why You Should Listen: ✔ 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. Episode Highlights: 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 Resources and Links: Stephen R. Covey — The 7 Habits of Highly Effective People Michael Hyatt — leadership and values-driven success John Blakey — trust model (ability, integrity, benevolence) Oscar Trimboli — How to Listen Toastmasters — practice for public speaking How to Be an Artist (book on learning and creativity) 🔗 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. Join the Conversation:Did you find this episode helpful? Share it with your colleagues and let me know your thoughts! Connect with me on LinkedIn and be part of the discussion. Subscribe & Stay Updated:Never miss an episode! Subscribe to The Effective Statistician on your favorite podcast platform and continue growing your influence as a statistician.

Anja Schiel, a vice chair at UNETA 21 and expert at the Norwegian Medicines Agency, delves into the complexities of single-arm studies in drug approval. She discusses the critical need for robust comparisons in evidence evaluation and the limitations of single-arm designs. Anja also highlights the importance of concurrent controls and shares practical strategies for choosing effective comparators. With insights into adaptive trial designs, she sheds light on statistical communication between regulators and HTA bodies, emphasizing the need for clear evidence strategies.

Discussion with Benjamin Piske and Alexander Schacht Why You Should Listen: 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 Episode Highlights: [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 Links: 🔗 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. Join the Conversation:Did you find this episode helpful? Share it with your colleagues and let me know your thoughts! Connect with me on LinkedIn and be part of the discussion. Subscribe & Stay Updated:Never miss an episode! Subscribe to The Effective Statistician on your favorite podcast platform and continue growing your influence as a statistician.

Interview with Lara Wolfson and Anders Gorst-Rasmussen Why You Should Listen: ✔ 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. Episode Highlights: 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 Links: 🔗 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. Join the Conversation:Did you find this episode helpful? Share it with your colleagues and let me know your thoughts! Connect with me on LinkedIn and be part of the discussion. Subscribe & Stay Updated:Never miss an episode! Subscribe to The Effective Statistician on your favorite podcast platform and continue growing your influence as a statistician. Glossary: 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

Kaspar Rufibach, an expert in survival analysis and member of the SAVVY collaboration, and Jan Beyersmann, a professor of biostatistics at Ulm University, discuss the complexities of analyzing adverse events in clinical trials. They highlight how varying follow-up times can bias results and advocate for using the Aalen–Johansen estimator as a standard practice. The conversation emphasizes the successful collaboration between pharma and academia, revealing how real-world data can change the perception of treatment risks and enhance benefit-risk evaluations.