Top 7: How to work with a physician within Pharma to become a valuable partner

Why this episode made our all-time Top 9: If you’ve ever thought “non-parametric = Wilcoxon/Mann-Whitney and that’s it,” this conversation will happily destroy that myth. Frank shows how rank-based methods unlock rigorous analyses for skewed data, outliers, ordinal endpoints, small samples, composites/estimands—and how to communicate effects without relying on means.

Adaptive designs let us learn earlier, stop smarter, and protect patients—but they also make communication tricky. In this episode, Kaspar Rufibach and I dig into what “still correct” looks like when you try to explain results from group-sequential and other adaptive trials to regulators, clinicians, and scientific audiences. We unpack conditional vs. unconditional bias, median-unbiased estimation, stage-wise ordering for p-values, confidence intervals in multi-stage settings, and what to do with secondary endpoints and multiplicity. We also touch on ICHE20 (Adaptive Clinical Trials) and why pre-specification isn’t just a box-tick—it’s what builds trust.

In this episode, I’m joined once again by my friend and frequent guest, Kaspar Rufibach, to talk about a topic that’s been around for decades but is gaining fresh attention thanks to the new ICH E20 draft guideline—adaptive designs in confirmatory clinical trials. Kaspar and I discuss why and when we should consider adapting a clinical trial, what kinds of adaptations are statistically valid and meaningful in a regulatory context, and why these designs—despite their efficiency—are still not used as often as they could be. We also dive into the statistical foundations behind adaptive designs, such as p-value combination methods and meta-analytic thinking, and explore how adaptive approaches can help us make faster and smarter decisions in drug development.

This episode is a little different because Alun turns the microphone toward me. After 456 episodes, it feels both strange and exciting to be the “guest” on my own show. Together, we reflect on the journey so far and then dive into a topic close to both our hearts: the human skills that make statisticians and quantitative scientists truly effective. We talk about leadership as helping others accomplish something, how to influence people across functions (not just departments), why being known inside your organization matters, and how presentation skills can make or break your impact. We wrap up with three actions you can start applying right away.

I’m excited to reshare one of our most-played conversations—the one where Norwegian regulator/HTA leader Anja Schiel and I get very practical about when single-arm trials fail decision-makers and what comparative, smarter alternatives look like for regulators, HTA bodies, payers, clinicians, and—most importantly—patients.

As statisticians in pharma, one of the most important professional relationships we can build is with our physician colleagues. When this partnership works well, studies run smoother, decisions are better, and our impact for patients grows. In this all-time Top 7 replay, Benjamin Piske and I talk about what makes this collaboration effective, the challenges you may face, and how to establish yourself as a true partner rather than “just the statistician.”

This is one of our most downloaded episodes ever, and I’m excited to bring it back in this replay. In this conversation, I spoke with Lara Wolfson (MSD) and Anders Gorst-Rasmussen (Novo Nordisk) about EU HTA (European Union Health Technology Assessment): what it is, why it’s coming, and why statisticians like us must pay attention. If you’ve ever wondered whether your approach to safety analysis is leading to misleading conclusions, this episode is a must-listen.

We’re bringing back one of our most downloaded episodes ever – a deep dive into how adverse events should be analyzed properly. This conversation with Jan Beyersmann and Kaspar Rufibach is packed with methodological insights and practical implications for statisticians working in clinical trials. Adverse event (AE) analysis has long been approached differently from efficacy analysis, often using overly simplistic methods that can bias results. In this episode, we discuss why that’s a problem – and how the SAVVY collaboration (Survival analysis for AdVerse events with Varying follow-up times) is pushing the field forward. Together with academia and multiple pharma companies, this collaboration tackled the issue of AE analysis using real randomized trial data, not just simulations. The findings show how common methods can underestimate or overestimate event probabilities and how established statistical methods can be applied more consistently to ensure fair benefit–risk assessments. If you’ve ever wondered whether your approach to safety analysis is leading to misleading conclusions, this episode is a must-listen.

In this special replay of one of our all-time most popular episodes, we dive deep into one of the most debated topics in the pharmaceutical industry: R vs SAS. Together with Thomas Neitmann and my co-host Sam Gardner, we compare these two powerful statistical programming tools from multiple angles — ease of learning, day-to-day usability, community support, visualization capabilities, regulatory acceptance, and more. Whether you are a seasoned SAS programmer, an R enthusiast, or someone deciding which tool to focus on, this conversation will give you valuable insights into where each shines, where they struggle, and how the industry is evolving.

In this special replay episode — the top 3 most downloaded of all time — I’m again joined by Stuart McGuire as we explore The Chimp Paradox by Professor Steve Peters. This book provides a simple yet powerful model for understanding how our brain works — and how it often works against us if we’re not aware of it. Whether in meetings, under pressure, or dealing with self-doubt, understanding your inner “chimp” can help you manage emotions, lead with clarity, and avoid the traps that keep so many statisticians and scientists stuck. This episode remains a favorite because it strikes at the core of how we think, react, and lead — especially in high-stakes scientific and business environments.