A novel approach for finding predictive biomarkers

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:

In this keynote episode, Professor Sebastian Schneeweiss from Harvard Medical School shares groundbreaking insights from his extensive research into emulating randomized controlled trials (RCTs) using real-world data (RWD). Recorded live at The Effective Statistician Conference 2024, this talk explores whether non-randomized studies based on electronic health records and claims data can reach conclusions as reliable as those from traditional RCTs. Prof. Schneeweiss, also Chief of the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital, walks us through the RCT DUPLICATE project, a major FDA-funded initiative that evaluated whether regulatory decisions could be replicated through high-quality real-world evidence (RWE). From the successes to the limitations—and everything in between—this episode is packed with lessons for statisticians, regulators, and pharmaceutical leaders interested in the future of data-driven healthcare decisions.

In this episode of The Effective Statistician, Alun Bedding takes the mic to explore one of the most underrated yet transformative tools in any professional's career—networking. With the PSI Conference happening, this episode offers timely insights into how statisticians—introverts and extroverts alike—can use networking to build meaningful professional relationships, accelerate their careers, and find support within the broader community. Alun shares personal anecdotes, including how a casual conversation with Alexander at a past PSI event led to his current guest host role, and outlines a step-by-step mindset and strategy for networking with authenticity and purpose.

In this episode, Alun Bedding welcomes Emma Crawford for a powerful and personal conversation about building inclusive workplace cultures—ones where every individual can thrive. Emma shares her late diagnosis of autism and how it reshaped her experience and expectations of the workplace. Together, they explore the limitations of the traditional “reasonable adjustments” framework and introduce the concept of success enablers—workplace strategies and tools that benefit not just neurodivergent individuals but everyone on the team. The conversation dives deep into the role of leadership, AI tools, flexible work arrangements, and cultural shifts that prioritize accessibility and well-being for all employees. Emma also previews her interactive workshop at the upcoming PSI conference and gives insight into how the session will encourage open discussion, hands-on activities, and actionable takeaways to help leaders and teams foster a more inclusive environment. Whether you’re a people leader, statistician, or advocate for change—this episode will challenge your assumptions and inspire more inclusive practices.

How can oncologists and healthcare professionals keep up with the ever-growing body of research to make the best decisions for patients? In this episode, I speak with Anna Forsythe, a pharmacologist, health economist, and founder of OncoScope, a groundbreaking platform delivering daily updated systematic literature reviews (SLRs) in oncology. Drawing on decades of experience in pharma and health economics, Anna shares how automation and AI are transforming the traditionally tedious SLR process—making up-to-date evidence accessible to clinicians in just a few clicks. Anna’s vision is clear: democratize access to high-quality, current evidence for clinicians—and ultimately improve patient care.

In this episode, I’m joined by Julia Geronimi from Servier and Dr. Pavel Mozgunov from the University of Cambridge to explore a topic that’s absolutely central to advancing precision medicine—predictive biomarkers. We dive into the challenges of identifying predictive vs. prognostic biomarkers, especially in early-phase clinical trials with limited sample sizes. What makes their approach so exciting is that it offers a model-flexible, visually intuitive way to detect predictiveness—even before we talk about dichotomizing biomarkers or setting cutoffs. If you work on clinical trial design, translational science, or biomarker development, this conversation will give you fresh tools—and a lot to think about.

In this special episode, I’m sharing the recording of a webinar I co-hosted with Cytel on March 20, 2025. I was joined by an expert panel of leaders in statistics and clinical development: Yannis Jemiai, Flaminia Chiesa, and Benjamin Piske. Together, we explored how the role of statisticians is rapidly evolving in response to industry changes, data innovations, and AI-driven transformation. This rich discussion dives into what it means to lead as a Clinical Data Scientist today—and why statisticians are uniquely positioned to influence strategy, innovation, and decision-making across the healthcare and pharmaceutical sectors.