Replay: R vs SAS - which is the better tool in pharmaceutical research

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.

This episode ranks as the #2 most downloaded of all time—and for good reason. As data science continues to disrupt and redefine the healthcare and pharmaceutical industries, statisticians everywhere are asking: Where do I fit in? In this insightful conversation, two leaders from Cytel—Yannis Jemiai, Head of Consulting and Software, and Rajat Mukherjee, Head of Data Science—share their personal journeys from traditional statistics into data science, how the field is evolving, and why statisticians are uniquely positioned to lead the future of analytics in life sciences. Whether you're curious, skeptical, or already exploring data science, this episode will inspire and equip you with practical insights.

This episode originally struck a chord with statisticians around the world—and for good reason. Whether you’re just starting with real-world evidence (RWE) or mentoring someone who is, this conversation is packed with practical lessons that will help you navigate the complexities of observational data with more confidence. In this special replay, guest Rachel Tham and I reflect on the real-world analysis mistakes, misconceptions, and growing pains they wish someone had warned them about earlier in their careers. From ambiguous index dates to messy exposure definitions and unexpected data quirks—this episode will save you hours of rework and help you better manage timelines and expectations.

In this thought-provoking keynote recording, Manjari Narayan takes us on a journey through one of the most pressing and promising intersections in modern science: the convergence of artificial intelligence, statistics, and biotechnology. Drawing on her extensive experience in both academia and biotech startups, Manjari explores the critical role statisticians can play in AI-driven drug discovery, biomarker validation, and experimental design. We are living in a "Cambrian explosion" of biotechnology, where high-throughput experiments, protein engineering, humanized models, and AI-powered screening open massive opportunities—but also introduce challenges in scientific validity, reproducibility, and decision-making. Through personal vignettes and cutting-edge examples, Manjari lays out how statistical thinking can (and should) drive better outcomes in early-stage drug development, biomarker discovery, and translational model evaluation. This episode is a must-listen for statisticians, data scientists, and healthcare innovators navigating the rapidly evolving biotech and AI startup landscape.

In this special keynote episode, I’m excited to share the recording of Professor Tim Friede’s thought-provoking presentation from The Effective Statistician Conference 2024. Tim, a leading expert in biostatistics and clinical trial design, dives deep into the combination of randomized controlled trials (RCTs) and real-world data (RWD)—especially in the context of rare diseases. Drawing from his work at the University Medical Center Göttingen and numerous European research initiatives, Tim presents a compelling case for integrating RWD to support small or underpowered RCTs using advanced statistical models. He shares real-world examples (including CJD and Alport syndrome), simulation insights, and practical recommendations that can change how we approach evidence generation in low-prevalence populations. What You’ll Learn: