When should we move beyond Excel for health technology assessment?

I sit down with Dr Robert Smith to explore why increasingly complex health economic models call for a different approach—and how R can help.

We discuss the challenges of reviewing large spreadsheets, R’s strengths for simulation modelling, and how the R for HTA community shares tools and knowledge. Robert also explains how generative AI speeds up coding while making domain expertise, clear problem definition, and human judgement even more valuable. We close with practical ways to learn, contribute, and get involved in open-source work.

How can we make sure our analyses lead to action?

I’m joined by Dr. Annika Bergbauer, economist, IT management consultant, and host of Datenaffaire, to explore how we can put people at the centre of data-driven decisions. We discuss stakeholder engagement, clear communication, and the human skills that help us turn technical work into practical impact.

How can statisticians improve dose decisions and drive innovation in their everyday work? I speak with Marie-Karelle Rivière and Anaïs Andrillon from Saryga about Project Optimus, their approach to dose optimization, and what it takes to turn challenging questions into meaningful research.

What helps us build a fulfilling career in statistics beyond technical expertise?

In this episode, I speak with Godwin Yung, who leads the Statistical Methodology group at Roche–Genentech, about lessons from his first decade in the pharmaceutical industry. We explore three themes from his career reflections: finding purpose, listening to understand, and learning from mentors.

Godwin shares the personal stories behind these lessons, from persevering through a difficult publication journey to discovering leadership strengths he once took for granted. Together, we discuss how we can contribute more effectively at work while making room for the people who matter most.

How do we generate evidence that supports regulatory approval, patient access, and better care?

In this episode, I talk with Gorana Capkun from Merck Healthcare, where she leads the patient-focused and real-world evidence team. We explore how integrated evidence planning brings together clinical trials, real-world evidence, and patient perspectives across the product life cycle. Gorana explains why planning should begin early, how collaboration helps teams identify evidence gaps, and why statisticians play a critical role in turning broad requests into precise research questions.

How can we make statistics meaningful to someone who does not work with it every day? I talk with Steve Mallett about his work with the PSI Schools team and the lessons it offers for training colleagues in pharma. We explore how hands-on activities, interactive data visualization, and a clear understanding of the audience can help people connect statistical concepts to patients and speak about results with greater confidence.

Will artificial intelligence make statisticians obsolete—or make our expertise even more valuable?

In this episode, I speak with independent statistical consultant Chris Harbron about how AI could reshape the role of statisticians in the pharmaceutical industry. Our conversation builds on Chris’s paper, Will the Pharmaceutical Industry Need Statisticians in an AI World?

We explore what happens when AI generates protocols, statistical analysis plans, programs, and reports. We also discuss why producing a plausible document differs from making sound decisions about clinical development.

Chris and I examine the areas where human statisticians continue to add essential value—from navigating trade-offs and challenging assumptions to building trust and accepting accountability. We also share practical ways statisticians can begin working with AI while protecting quality, scientific rigor, and authenticity.

How can we build a patient-reported outcome strategy that captures the patient voice and strengthens clinical development?

In this episode of The Effective Statistician Podcast, I speak with Julia Poritz, Senior Research Consultant on Cytel’s Evidence, Value, and Access team, about developing and implementing an effective patient-reported outcome (PRO) strategy for clinical trials.

We discuss how PROs help us understand the effects of a disease and its treatment from the patient’s perspective—including symptoms, physical functioning, health perceptions, and overall quality of life. Julia explains how the Wilson and Cleary model helps us connect these different outcomes and create a clear measurement strategy.

We also explore the four essential parts of a PRO strategy: selecting appropriate PRO measures, implementing them within the study, analyzing the data, and reporting the results. Throughout our conversation, we emphasize the importance of early planning, validated measures, patient relevance, regulatory expectations, and comparability with previous clinical trials.

In this episode, I talk with Ning Leng, Ph.D., Director II, Data and AI Acceleration Group, Data & Statistical Sciences at AbbVie, about the growing role of the R Consortium in the pharmaceutical industry.

Ning brings extensive experience in statistics, computational genomics, open-source technology, and the adoption of R across the pharmaceutical industry. Before joining AbbVie, she spent 10 years at Roche Genentech, where she helped drive the adoption of R, cloud technologies, Git, and Shiny.

We discuss how the R Consortium creates a platform for statisticians, programmers, pharmaceutical companies, and regulators to collaborate on practical challenges—and how that collaboration is changing the way we approach regulatory submissions.

Cancer treatments have changed dramatically over the past decade, but have our dose-finding strategies kept pace?

In this episode, I speak with Dr. Ayon Mukherjee, who leads statistical innovation in early oncology development at Eli Lilly. Together, we explore Project Optimus, the FDA initiative that is changing how we think about dose optimization in oncology.

Instead of simply finding the highest dose patients can tolerate, Project Optimus encourages us to identify the dose that provides the best balance between efficacy, safety, pharmacokinetics, pharmacodynamics, and long-term tolerability.

Ayon explains why the traditional maximum tolerated dose approach worked well for chemotherapy but often falls short for targeted therapies and immunotherapies. We also discuss how statisticians can help lead this transformation by designing better dose optimization studies and collaborating more effectively with clinicians, pharmacologists, and regulators.