The Effective Statistician - in association with PSI

A Conversation with Robert Smith

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.

Why you should listen

If you work in statistics, health economics, or HTA, this conversation will help you assess where R could support your work. Together, we explore how to:

• Recognise when a model’s complexity calls for a move beyond spreadsheets.
• Connect statistical analyses and economic modelling within R.
• Learn from an open-source community across academia, consultancies, and industry.
• Use generative AI while keeping human judgement and domain expertise central.
• Support junior colleagues as AI takes over more routine coding.

Episode highlights with timestamps

• 02:25 — How Robert found his way into R and health economics
• 03:54 — Why move beyond Excel?
• 06:15 — The challenge of reviewing and maintaining spreadsheet models
• 08:40 — Why R rather than Python?
• 11:29 — What the R for HTA consortium brings together
• 13:25 — Where the modelling work goes deeper
• 15:47 — How to learn and get involved
• 17:39 — Why industry supports open-source methods
• 19:29 — How consultancies adapt their services
• 20:51 — How generative AI changes model development
• 23:17 — Why human judgement still drives the work
• 25:08 — Start with a small contribution

Links and Resources:

🔗Robert Smith on LinkedIn

🔗 Robert’s publications

🔗 R for HTA consortium

🔗 Dark Peak Analytics

🔗 R for Health Economics courses

🔗 Structured Expert Elicitation Platform

🔗 Learn more aboutPSI and become a member

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 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.

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:
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A Conversation with With Dr. Annika Bergbauer

Why you should listen

We can produce excellent analyses and build powerful tools, yet still struggle to change how decisions are made. In this conversation, we explore how we can close that gap.

Together, we discuss how we can:

• Involve stakeholders early so we build solutions that meet their needs.
• Understand the fears, incentives, and pressures behind resistance to change.
• Use personas to make our analyses, dashboards, and presentations more relevant.
• Tell clear stories that help our findings lead to action.
• Strengthen our listening, business understanding, and leadership skills as AI changes our work.

Episode highlights with timestamps

• 01:30 — How we turn data into better decisions
• 02:22 — What we can learn from Annika’s journey
• 03:48 — Why we cannot always access the data we need
• 05:09 — How we move from describing outcomes to understanding causes
• 06:59 — What we face in randomized trials and observational research
• 08:20 — Why our successful pilots do not always become useful tools
• 11:35 — How we bring the right stakeholders on board
• 12:57 — How we make our message clear
• 13:50 — How we understand incentives and everyday pressures
• 15:17 — How we create and use stakeholder personas
• 17:41 — How we develop our skills as AI changes our work
• 19:34 — Why we need to listen before we present
• 20:33 — How we build trust through human conversations
• 21:59 — What we bring to the table as humans

Links and Resources:

🔗 Connect with my guest, Dr. Annika Bergbauer

🔗 Listen to Annika’s podcast, Datenaffaire

🔗 Explore the Datenaffaire episode archive

🔗 Learn more aboutPSI and become a member

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 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.

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.

https://podcasts.apple.com/us/podcast/the-effective-statistician-in-association-with-psi/id1360198752

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A Conversation with Marie-Karelle Riviere and Anaïs Andrillon

Highlights and timestamps

• 06:09 — Rethinking dose selection with Project Optimus
• 13:11 — Exploring the U-DESPE approach to dose optimization
• 23:07 — How a small team drives statistical innovation
• 29:46 — Moving from everyday challenges to meaningful research
• 32:02 — Creating an environment where innovation can thrive

Links and resources

Saryga
https://saryga.com/

Marie-Karelle Rivière and Anaïs Andrillon — Profiles and Publications
https://saryga.com/about/

Anaïs Andrillon on LinkedIn
https://www.linkedin.com/in/ana%C3%AFs-andrillon-phd-677a89b5/

Marie-Karelle Rivière https://www.linkedin.com/in/marie-karelle-riviere-38342011/

PSI Presentation Slides — U-DESPE
https://www.psiweb.org/docs/default-source/conference/2026-conference-slides/tuesday-16-june/5-epsig_andrillon_anais__micallef_sandrine.pdf?sfvrsn=6b4ca9db_1

PSI Early Phase Special Interest Group
https://www.psiweb.org/sigs-special-interest-groups/early-phase

PSI Special Interest Group Contacts
https://psiweb.org/sigs-special-interest-groups/sigs/sig-lead-contacts

Previous Episode — Project Optimus and What You Need to Know About It
https://theeffectivestatistician.com/project-optimus-and-what-you-need-to-know-about-it/

FDA Project Optimus
https://www.fda.gov/about-fda/oncology-center-excellence/project-optimus

A Conversation with Godwin Yung

Why You Should Listen I explore practical ideas to help you:

• Choose work that matters. Focus your time on problems worth solving rather than trying to pursue every interesting idea.
• Strengthen your influence through listening. Understand your colleagues’ needs before offering solutions.
• Build a clearer professional reputation. Recognise what others know you for and shape how you contribute.
• Discover strengths you may overlook. Learn how mentors can identify qualities that feel ordinary to you.
• Grow without losing sight of life outside work. Reflect on how family, relationships, and purpose shape a sustainable career.

Highlights with Timestamps

•

02:00 — Reflecting on a decade in pharma I ask Godwin about his journey from Takeda to Roche–Genentech and the reflections that inspired his LinkedIn post.

•

06:20 — Finding purpose in problems worth solving We explore how a meaningful research question helped Godwin focus his efforts and persevere through a challenging publication process.

•

13:30 — Balancing professional ambition with family life Godwin shares the personal challenges behind his work, and I reflect on why our careers cannot capture everything that matters in our lives.

•

18:44 — Listening to understand before offering solutions We discuss how bringing different perspectives together helped Godwin build connections around probability of success in drug development.

•

25:24 — Building your brand and asking better questions I explain how others’ perceptions shape your professional reputation. We also explore why greater seniority makes humility and listening even more valuable.

•

31:35 — Finding mentors who recognise your potential Godwin shares how mentors helped him recognise his strengths and find the confidence to step into leadership. I discuss the value of a varied network, including peer and reverse mentoring.

Links and Resources

•

Connect with Godwin Yung on LinkedIn — Follow his career reflections and statistical work.

•

10 Years in Pharma: 10 Lessons Learned by Godwin Yung

A Conversation with Gorana Capkun

Why You Should Listen

• Understand integrated evidence planning and how it connects evidence generation across functions, stakeholders, and geographies.
• Look beyond regulatory approval to consider what payers, physicians, and patients need from the evidence.
• Strengthen your contribution as a statistician by asking questions that clarify endpoints, study designs, and intended conclusions.
• Learn why patient perspectives matter when assessing treatment benefits and the practical challenges of clinical care.
• Recognise common planning mistakes that can delay access and reduce the impact of otherwise strong research.

Episode Highlights With Timestamp

• 02:23 — From pure mathematics to patient-focused evidence Gorana shares how her career across statistics, epidemiology, and pharmaceutical functions shaped her approach to evidence generation.
• 04:29 — What makes an evidence plan integrated? We discuss a shared strategic roadmap that connects different methods and stakeholder needs throughout the product life cycle.
• 07:17 — Why evidence planning should start in research Gorana explains how early collaboration helps teams understand unmet needs, disease burden, and how to measure success.
• 15:56 — Balancing expert advice with real-world data We explore how advisory boards can guide research—and why their observations may not tell the whole story.
• 18:49 — Aligning evidence needs before launch We discuss why early input from market access, medical affairs, and other functions matters when choosing endpoints and study designs.
• 25:59 — What changes after a treatment reaches patients? We examine how clinical practice reveals new questions about tolerability, treatment burden, and outcomes beyond trial populations.
• 29:44 — Common mistakes and how to avoid them Gorana shares why starting too late, working in silos, and unclear communication can undermine integrated evidence planning.

Links and Resources

• Merck Healthcare
• The Effective Statistician
• The Effective Statistician Academy — Explore the Academy and its learning resources. The Effective Statistician
• Join PSI — Learn about membership in the statistical community supporting healthcare research. psiweb.org
• Further reading: Integrated Evidence Planning for Enhancing Patient Care — Harnessing the Power of Real-World Evidence — A related publication co-authored by Gorana Capkun. PMC

A Conversation with Steve Mallett

Why You Should Listen

• Discover how PSI helps students see where maths can take them, including careers in pharmaceutical statistics.
• Learn how interactive visuals can connect a histogram to the experiences of individual patients.
• Find practical ways to design statistical training around the questions your audience actually has.
• Hear how you can support schools outreach, even if you have never led a classroom workshop.

Major Episode Highlights

• 02:00 — Steve shares why he joined the PSI Schools team and what the team hopes to achieve. [- 06:13 — We discuss translating outreach activities and bringing them to schools beyond the UK.
• 07:39 — Steve explains how new volunteers can work alongside experienced presenters.
• 09:35 — We walk through a school visit featuring R Shiny workshops, a clinical trial simulation, and career conversations.
• 12:56 — Steve shows how an interactive histogram can help students connect variability with patient outcomes.
• 17:15 — We discuss how to build training around your audience’s challenges and help concepts stick.
• 23:01 — Steve explains why medical colleagues want the confidence to discuss clinical trial results accurately.
• 25:22 — I bring together our key lessons on knowing your audience, using clear visuals, and encouraging exploration.

Links and Resources

•

list text herePSI Schools website: https://www.psiweb.org/careers/schools-zone

•

list text here Email address for people interested in volunteering: careers@psiweb.org

•

list text here is an example of one of our Shiny workshop activities: mallettstats.shinyapps.io/Clinical-Trial-Workshop/

Steve Mallett's LinkedIn

A Conversation with Chris Harbron

Why you should listen

Listen to this episode to:

• Understand how AI may change statistical work in the pharmaceutical industry
• Recognize the limitations of using AI in clinical trial design and decision-making
• Explore why human judgment, collaboration, and accountability still matter
• Learn which capabilities statisticians should strengthen for an AI-enabled future
• Discover practical ways to experiment with AI in your daily work
• Approach the future of statistics with greater confidence and curiosity

Episode highlights with timestamps

• 01:30 – Will AI make statisticians redundant?
I introduce the central question behind my conversation with Chris.
• 03:27 – Can AI create an entire clinical study package?
We examine what AI can generate—and why plausible outputs still require careful scrutiny.
• 05:16 – A protocol is more than a document
Chris explains how document development supports alignment, precision, and better scientific discussions.
• 07:08 – Navigating real-world trade-offsWe discuss the challenge of balancing timelines, budgets, sample sizes, biomarkers, and broader development goals.
• 10:18 – Combining human and artificial intelligence
We consider how AI can complement experience rather than simply compete with it.
• 12:41 – Why keeping a human in the loop matters
Chris highlights an important challenge surrounding review quality and confidence in AI-generated work.
• 13:37 – Building trust in an AI-enabled workplace
We explore what statisticians provide beyond technical competence.
• 15:53 – Who remains accountable when AI gets it wrong?
Our conversation turns to responsibility, risk, and the limits of delegating decisions to technology.
• 17:17 – How statisticians can prepare for the future
Chris offers practical guidance for professionals working with clinical trials, real-world evidence, and other data projects.
• 22:07 – A promising future for statisticians
We close by considering how AI could create new opportunities for the profession.

Links and Resources:

🔗 Read Chris Harbron’s paper:Will the Pharmaceutical Industry Need Statisticians in an AI World?

🔗 Learn more aboutChris Harbron and PoshStatsConnect with Chris Harbron on LinkedIn

🔗 Learn more aboutPSI and become a member

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 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.

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.

https://podcasts.apple.com/us/podcast/the-effective-statistician-in-association-with-psi/id1360198752

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A conversation with Julia Poritz

Why you should listen

You’ll learn:

• Learn how to build a cohesive patient-reported outcome strategy from measure selection through publication.
• Choose PRO measures that reflect what truly matters to patients.
• Capture treatment effects that traditional clinical endpoints may overlook.
• Understand how regulatory, payer, and clinical trial requirements influence PRO planning.
• Avoid common mistakes involving patient burden, translation, analysis, and reporting.
• Generate PRO evidence that is scientifically rigorous, comparable, and meaningful to patients.

Episode highlights with timestamps

• 01:29 – The Role of Patient-Reported Outcomes
• Julia shares how PROs help bring the patient voice into clinical research.
• 04:19 – Connecting Treatment to Quality of Life
• We discuss a useful framework for understanding how treatment can affect symptoms, functioning, and patients’ overall well-being.
• 08:59 – Building a Cohesive PRO Strategy
• Julia introduces the essential areas researchers should consider when planning their approach.
• 15:00 – Common Challenges in PRO Planning
• We explore practical issues involving implementation, translation, and international studies.
• 16:23 – When Should PRO Planning Begin?
• Julia explains why timing matters and what development teams should consider when introducing PRO measures.
• 19:14 – Keeping Patients at the Center
• We close with the most important principle behind every effective PRO strategy.

Links and Resources:

🔗 PROTEUS Toolbox: Patient-Reported Outcomes Tools, Engaging Users and Stakeholders

🔗 National Cancer Institute: Introducing the PROTEUS Toolbox

🔗 CONSORT Reporting Guidelines

🔗 STROBE Reporting Guidelines

🔗 PRISMA Reporting Guidelines

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 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.

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.

https://podcasts.apple.com/us/podcast/the-effective-statistician-in-association-with-psi/id1360198752

https://open.spotify.com/show/7gD9D4feE2uGozsa7O32pq?si=cda91c16e8034062

A Conversation with Ning Leng

Why you should listen

If you work in statistics, data science, statistical programming, or clinical development, this episode will give you a practical look at how the industry is moving toward more modern and collaborative approaches.

You’ll learn:

• How the R Consortium is helping pharmaceutical companies work together and collaborate with the FDA.
• What the R Consortium’s regulatory submission pilots can teach you about using R in real-world submissions.
• How open-source collaboration can influence regulatory guidance and industry practices.
• How tools like R, Shiny, containers, and WebAssembly could make regulatory submissions more interactive and reproducible.
• How the community is beginning to explore generative AI for clinical trials and statistical programming.
• Why benchmark datasets, challenging test cases, and quality control will be critical for using AI responsibly.
• How you can explore the R Consortium’s public resources and get involved in its work.
• Whether you’re already using R or simply want to understand where statistical programming and regulatory submissions are heading, this conversation with Ning Leng will give you valuable insights into the future of our field.

Episode highlights with timestamps

• 01:30 — Meet Ning Leng and her journey into R
Ning introduces her background in statistics and computational genomics and explains how she became involved in R adoption, cloud migration, Git, and Shiny at Roche Genentech.
• 03:57 — Why R wasn't being used for regulatory submissions
Ning explains the misconception that the FDA did not accept R and identifies the real challenge: the industry lacked practical examples showing how to submit R-based materials.
• 05:46 — What is the R Consortium?
We discuss the R Consortium's role as a nonprofit organization that promotes good practices and the use of R across industries.
• 06:16 — How the R Consortium collaborates with the FDA
Ning explains the submission working group, validation hub, and how the Consortium provides a platform for collaboration between pharmaceutical companies and the FDA.
• 08:13 — R Consortium FDA submission pilots
Ning walks through the different pilots, from submitting TLGs to incorporating Shiny, ADaM code, containers, WebAssembly, and alternative data formats.
• 10:29 — How companies are using the pilot submissions
We explore how pharmaceutical companies use the public R Consortium pilots as practical templates when preparing their own R-based regulatory submissions.
• 11:24 — How collaboration influenced FDA guidance
Ning explains how lessons from the pilots helped clarify FDA guidance around file formats, including .r and .zip files.
• 12:17 — Moving beyond PDF-based electronic submissions
We discuss the opportunity to use interactive graphics, HTML, Shiny, and other modern technologies to make electronic regulatory submissions more useful and traceable.
• 14:14 — Generative AI enters the picture
Ning explains how the R Consortium is beginning to explore generative AI for clinical trial reporting, trial design, and statistical programming.
• 15:12 — Building AI skills and benchmark test cases
We discuss open-source AI skills, benchmark datasets, and the importance of testing AI against difficult and unusual corner cases.
• 16:28 — How you can get involved with the R Consortium
Ning shares practical ways to explore the community, including its public working-group materials, meeting minutes, recordings, and Slack channel.

Links and Resources:

🔗 R Consortium: Learn more about the R Consortium and its work to promote the use of R and good practices across industries: https://r-consortium.org/

🔗 R Consortium R Submissions Working Group: Explore the working group's projects, meeting materials, submission pilots, and opportunities to get involved: https://rconsortium.github.io/submissions-wg/

🔗 R Submissions Working Group — Pilot Projects: Learn about the different FDA submission pilots and how the community is exploring R for regulatory submissions: https://rconsortium.github.io/submissions-wg/pilot_background.html

🔗 R Consortium 2026 Plans and 2025 Success: Read about the latest work from the R Submissions Working Group, including the development of Pilots 6 and 7: https://r-consortium.org/posts/submissions-wg-2026/

🔗 Pilot 4 — WebAssembly and Containers: Learn how the R Consortium explored submitting a Shiny application using WebAssembly and containers for FDA review: https://r-consortium.org/posts/using-r-to-submit-research-to-the-fda-pilot-4-successfully-submitted/

🔗 R Consortium Working Groups: Browse the R Consortium's different working groups and projects: https://r-consortium.org/all-projects/isc-working-groups.html

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 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.

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.

https://podcasts.apple.com/us/podcast/the-effective-statistician-in-association-with-psi/id1360198752

https://open.spotify.com/show/7gD9D4feE2uGozsa7O32pq?si=cda91c16e8034062

A Conversation with Ayon Mukherjee

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.

Why Listen to this Episode:

• Understand what Project Optimus is and why it is transforming oncology drug development.
• Learn why the traditional maximum tolerated dose (MTD) approach is no longer sufficient for many targeted therapies and immunotherapies.
• Discover how statisticians can use PK/PD, exposure-response, efficacy, safety, and tolerability data to support better dose optimization.
• Explore the progress the industry has made since Project Optimus was launched and the challenges that remain.
• Gain practical insights on collaborating effectively with clinicians, pharmacologists, regulators, and academic partners.
• Find out what statisticians can do today to help advance innovative dose optimization strategies and improve patient outcomes.

Episode Highlights

• 00:00 – Introduction to the episode
• 01:31 – Ayon Mukherjee introduces himself and his work in early-phase oncology and dose optimization.
• 02:58 – What is Project Optimus, and why did the FDA introduce it?
• 03:28 – Why traditional chemotherapy dose-finding approaches no longer fit modern targeted therapies and immunotherapies.
• 05:21 – The key principles of Project Optimus: balancing efficacy, safety, PK/PD, and long-term tolerability.
• 08:14 – The types of data needed to support dose optimization beyond dose-limiting toxicities.
• 09:40 – How far has the industry come since Project Optimus launched in 2021?
• 11:06 – Why communication and cross-functional collaboration are essential for successful implementation.
• 13:29 – Regulatory acceptance and the gap between published methodologies and industry adoption.
• 15:27 – The value of industry-academia collaboration and cross-company working groups.
• 16:53 – Why education and training are critical for increasing awareness and adoption.
• 20:06 – Open-source tools, R Shiny applications, and practical resources for implementing innovative trial designs.
• 23:23 – Final thoughts on how statisticians can improve dose optimization and ultimately serve patients better.

Links and Resources

• Connect with Dr. Ayon Mukherjee on LinkedIn
• FDA Project Optimus – An initiative from the FDA Oncology Center of Excellence to reform dose optimization in oncology drug development.
• https://trialdesign.org/ – Open-source tools and R Shiny applications for innovative clinical trial designs.
• Innovative Design Scientific Working Group (IDSWG) – A collaborative group advancing innovative clinical trial designs in early-phase oncology.

A Discussion with Alun Bedding and Emma May

In this insightful interview, Emma May and Alun Bedding explore the nuances of coaching and mentoring, sharing personal stories, frameworks, and practical tips to enhance professional growth. Discover how these powerful tools can transform statisticians' careers and foster leadership development.

Key topics:

• Differences and overlaps between coaching and mentoring
• Frameworks for mentoring conversations (Challenges, Choices, Consequences)
• The importance of independence in coaching and mentoring
• Building trust and confidence in professional relationships
• Role of reflective practice and visualization in growth

Episode Highlights

• 02:00 – Emma shares how coaching and mentoring helped her overcome limiting beliefs.
• 04:00 – Coaching vs. mentoring: the key differences and why both matter.
• 12:20 – Why technical expertise alone isn't enough for career growth.
• 17:00 – The value of having an independent coach or mentor.
• 22:00 – Building leadership through small, consistent actions and accountability.
• 26:40 – How improv and role-play strengthen communication and leadership.
• 32:25 – Choosing between coaching and mentoring—and why one session can make a lasting impact.

Links

• Understanding Coaching & Mentoring PDF
• Emma May: LinkedIn https://linkedin.com/in/emma-may

A Conversation with Benjamin Ackerman

External control arms are becoming increasingly important in drug development, but creating valid comparisons requires more than matching patient populations.

In this episode, I speak with Ben Ackerman, Director of Real-World Biostatistics at GSK, about one of the most overlooked challenges in external control arm studies: endpoint bias. We discuss why differences in how outcomes are measured can influence study results, what researchers should consider when designing studies, and how the field is evolving to address these challenges.

If you work with real-world evidence, causal inference, or innovative clinical trial designs, this episode offers valuable insights into improving the credibility and transparency of external control arm analyses.

**Why You Should Listen **

• Learn why endpoint alignment matters as much as population matching.
• Understand how measurement differences can create bias in external control arm studies.
• Discover practical methods to quantify and mitigate endpoint bias.
• Hear how regulators are increasingly evaluating endpoint comparability.
• Gain insights into better study design and pre-specification strategies for real-world evidence research.

**Episode Highlights **

• 00:01:31 – Introducing Ben Ackerman and external control arms
• 00:04:41 – Why endpoint bias deserves more attention
• 00:08:38 – Understanding the challenges of comparing different data sources
• 00:12:30 – Practical considerations for study design
• 00:16:32 – The role of transparency and pre-specification
• 00:20:30 – Regulatory perspectives and future expectations
• 00:26:07 – Where the field is heading next

**About Ben Ackerman ** Ben Ackerman is Director of Real-World Biostatistics at GSK and a PhD biostatistician specializing in causal inference, real-world evidence methods, and the integration of randomized trial data with observational data sources. His research focuses on improving evidence generation through innovative statistical methods that bridge clinical trials and real-world healthcare data.

A Discussion with Alun Bedding and David Wright, Jürgen Hummel, and Jenny Devenport

This episode features three leading statistical methodology experts discussing the role, impact, and future of methodology groups in the pharmaceutical industry. They explore organizational structures, skill sets, AI integration, and strategies to accelerate adoption of innovative methods.

**Key topics: **

• Role and impact of methodology groups
• Organizational considerations for methodology teams
• Skills and traits of great statisticians
• Integration of AI and machine learning in pharma
• Strategies to accelerate adoption of new methods

**Episode highlights: **

• 00:00 Introduction to Statistical Methodology Groups
• 02:18 Exploring the Paper's Insights
• 06:49 The Role of Methodology Statisticians
• 10:13 Consultation and Collaboration in Drug Development
• 12:54 Addressing the Innovation Problem in Drug Development
• 16:06 Qualities of a Great Methodology Statistician
• 20:31 The Future of Methodology Groups and AI
• 25:46 The Importance of Human Insight in Clinical Trials
• 28:27 The Prevalence of Methodology Groups in the Industry
• 30:29 Goals of Methodology Departments

**Links and resources: **

• Statistical Methodology Groups in the Pharmaceutical Industry Paper Https://statistics.biopharmaceutics.com/article/10.1177/15501477221112345
• EFSPI Statistical Leaders Group https://www.efspi.org/statistical-methodology-leaders/
• EFSPI Ecosystem https://www.efspi.org/ecosystem/

**Guest links **

• Jenny Devenport: https://www.linkedin.com/in/jenny-devenport/
• David Wright: https://www.linkedin.com/in/david-wright/
• Jurgen Hummel: https://www.linkedin.com/in/jurgen-hummel/

A Discussion with Andrew (Andy) York

**Episode Summary ** What does “quality” really mean in statistical programming?

In this episode of The Effective Statistician, I speak with Andrew (Andy) York about the evolving world of programming validation, traceability, and quality assurance in clinical trials. Andy has decades of experience in statistical programming, leadership roles across pharma and CROs, and now works with AI-driven solutions focused on improving validation and traceability.

We discuss why traditional approaches to validation are becoming increasingly difficult to sustain, how expectations from regulators continue to grow, and why traceability is far more than just linking programs and datasets.

Andy also shares how modern AI-powered tools can automatically map programming workflows, connect datasets and outputs, and create end-to-end traceability from raw data to final tables, figures, and listings.

If you work with statistical programming, clinical data workflows, submissions, or validation processes, this episode will challenge some long-held assumptions and introduce you to where the future may be heading.

**Why You Should Listen **

• Learn what “quality” in programming really means beyond simply writing working code
• Understand the challenges of maintaining traceability across complex clinical trial workflows
• Discover why manual validation processes are becoming harder to scale
• Hear how AI is starting to transform validation and traceability in programming
• Explore the balance between regulatory expectations, efficiency, and confidence in outputs
• Gain insights from someone who has seen the evolution of statistical programming from the very beginning

**Episode Highlights **

•

00:01:30 — Andy York’s journey into statistical programming Andy shares how he started programming during the early days of SAS in pharma and how the role of programmers evolved over the decades.

•

00:04:41 — What does programming quality actually mean? We discuss confidence in outputs, customer expectations, regulatory requirements, and creating programs that your future self can still understand years later.

•

00:06:46 — The regulator’s perspective on validation and traceability Andy explains why full traceability from raw data to final outputs is essential for regulatory confidence.

•

00:08:15 — The limitations of traditional traceability approaches We reflect on the common experience of manually navigating folders, programs, and datasets to reconstruct programming logic.

•

00:09:45 — How automated traceability changes the game Andy explains how modern tools can automatically create end-to-end traceability matrices across programs, datasets, and outputs.

•

00:10:45 — Forward traceability vs. backward traceability A fascinating discussion about not only tracing outputs back to source data, but also understanding where every data point flows forward through the analysis process.

**Links and References: **

• Verisian - https://verisian.com/
• Bayer case study https://verisian.com/customer-stories/how-bayer-uses-verisian-ai-to-automate-submission-document-generation

A Conversation with Alun Bedding and Richard Zink

In this episode, I hand over the mic to Alun Bedding, who speaks with Richard Zink about a topic that might surprise many statisticians: applied improvisation.

At first glance, improv may seem unrelated to statistics or leadership. But as Richard shares his journey—from a biostatistician and software developer to an improv practitioner—it becomes clear how powerful these skills are for communication, collaboration, and leadership.

We explore how improv techniques help you think on your feet, listen deeply, support others, and communicate more effectively—all critical skills for statisticians working in complex, cross-functional environments.

**Why You Should Listen

** If you’ve ever struggled with:

• Explaining complex ideas clearly
• Speaking confidently in meetings
• Engaging non-statistical stakeholders
• Feeling held back by imposter syndrome

…then this episode is for you.

Applied improvisation offers a practical, hands-on way to strengthen interpersonal skills—without boring theory. Instead of reading about communication, you experience it.

**You’ll learn:

**

• Why communication—not technical skill—is often the real bottleneck
• How improv builds confidence and reduces fear of mistakes
• Why “yes, and…” can transform conversations and teamwork
• How body language alone can make or break your message

**Episode Highlights

**

• 00:00 – Introduction to Applied Improvisation Why improv is more than comedy—and why it matters for statisticians
• 03:00 – Richard’s Journey into Improv From watching Whose Line Is It Anyway? to teaching scientists
• 06:30 – Thinking on Your Feet & Team Collaboration Why improv is not about you—it’s about building ideas together
• 08:20 – “Yes, And” and “I’ve Got Your Back” Core improv principles that strengthen communication and trust
• 10:20 – You Don’t Need to Be Funny Why authenticity matters more than humor
• 12:00 – Why Statisticians Need Interpersonal Skills Great ideas go nowhere if you can’t communicate them
• 14:30 – Learning Through Play Why improv exercises outperform traditional leadership training
• 18:30 – Online vs. In-Person Improv How improvisation works even in virtual settings
• 20:40 – What to Expect from the PSI Workshop Interactive exercises, reflection, and real-world application
• 22:30 – The “Gibberish” Exercise A powerful lesson: communication goes far beyond words
• 24:20 – Simplicity in Communication How to make ideas memorable for non-statisticians
• 26:00 – Improv and Presentation Skills Reducing fear and embracing mistakes
• 27:30 – Overcoming Imposter Syndrome How improv builds confidence over time
• 30:10 – Final Advice: Rethinking Mistakes Mistakes are not failures—they’re opportunities for growth

**Resources & Links

**

• Connect with Richard Zink on LinkedIn: https://www.linkedin.com/in/richard-c-zink/
• Learn more about PSI and their events: PSI https://postpartum.net/professionals/psi-conference/
• Explore training opportunities in applied improvisation https://ww3.aievolution.com/JSMAnnual2025/Events/viewEv?ev=4318

A Conversation with Anna Mosikian

Episode Overview

In this episode, I speak with Anna Mosikian, a physician by training and Global Clinical Program Lead working at the intersection of clinical development and strategic marketing.

Anna brings a powerful perspective on how clinical data translates into real-world value—bridging evidence generation, regulatory expectations, and commercial impact. We dive into what clinical leaders truly expect from statisticians and how statisticians can move from technical contributors to strategic partners.

Why You Should Listen

If you want to become a more effective statistician—not just technically strong but highly valued in your organization—this episode is for you.

You’ll learn how to:

• Build real partnerships with clinical leaders
• Increase your influence in decision-making
• Communicate your work in a way that drives action
• Think beyond analysis and into impact

Episode Highlights

• 00:01:30 – Anna’s unique journey from physician to pharma leadership
• 00:03:25 – A surprising expectation clinical leads have of statisticians
• 00:04:21 – The one mindset shift that changes how you approach trials
• 00:06:16 – How to create a culture where ideas actually get heard
• 00:08:40 – A common blind spot in clinical trials you might overlook
• 00:12:59 – Why waiting for the “perfect idea” can hold you back
• 00:17:15 – A simple test to check if you truly understand your work
• 00:20:39 – What really builds trust across teams
• 00:22:32 – The question you should always ask—but most don’t
• 00:25:49 – Why exploration matters more than you think
• 00:26:19 – A major industry shift impacting how trials are designed
• 00:30:35 – Final advice on becoming a standout statistician

A Conversation with Alun Bedding

Why You Should Listen

• You want to build stronger, trust-based relationships at work
• You’re leading teams and want measurable ways to improve culture
• You want to understand how trust impacts retention and performance
• You’re tired of “soft” leadership topics without concrete actions
• You want practical frameworks you can apply immediately

Highlights (with Timestamps)

01:31 – Why trust is the foundation of everything You can’t influence, negotiate, or collaborate effectively without trust.

02:01 – If you can’t measure it, you can’t improve it Why trust needs to be treated like any other measurable concept.

03:29 – Trust across all relationships From leadership to cross-functional teams and clients.

04:29 – The cost of low trust Feeling unsafe, guarded, and unable to speak openly.

05:27 – Organizations say trust matters—but don’t measure it A critical gap in leadership practice.

06:25 – The clinical trial analogy Why unclear measurement leads to ineffective improvement.

07:25 – Start by removing trust-destroying behaviors Often the fastest way to improve trust.

08:19 – Trust as a lead measure How it predicts outcomes like engagement and retention.

10:16 – Evidence: why trust drives performance Lower stress, higher productivity, and better retention.

11:14 – The Leadership Trust Index A structured way to measure trust within organizations.

13:38 – How to implement trust surveys Best practices for response rates and anonymity.

16:04 – Action builds trust—not surveys alone Why leadership follow-through matters most.

18:21 – Trust is built through consistent delivery Doing what you say—every time.

20:41 – The 3 Cs of trust Character, care, and competence.

22:32 – Simple ways to build trust today Listen actively, show appreciation, and understand others.

24:00 – Bringing trust measurement into your organization From workshops to coaching and continuous improvement.

26:25 – Final takeaway Trust is measurable—and that makes it actionable.

Interview with Rachael Lawrance

**

Why You Should Listen

** If you work in clinical trials or healthcare analytics, this episode will help you:

• Understand why patient perspectives are now central—not optional
• Learn the difference between PROs and COAs, and when each matters
• See how questionnaires are scientifically developed and validated
• Improve how you select, analyze, and interpret endpoints
• Avoid common pitfalls when using patient-reported data

Episode Highlights

• 00:00 – Introduction and background to the episode
• 01:31 – Rachael’s journey into patient-centered outcomes
• 03:24 – Why PROs have become central to drug development
• 05:18 – What COAs are and how they extend beyond PROs
• 05:46 – The four types of COAs explained
• 08:58 – Generic vs disease-specific questionnaires
• 13:16 – How PRO instruments are developed and validated
• 15:06 – What “meaningful change” really means
• 17:02 – Using Phase II data to inform Phase III endpoints
• 18:24 – FDA expectations and guidance on PROs
• 21:47 – Using the estimand framework for patient-centered endpoints
• 23:15 – What “psychometrically sound” actually means
• 31:16 – How I would choose the right PRO or COA for a trial
• 34:01 – Avoiding missing data and reducing patient burden
• 37:18 – Key takeaways on interpreting patient-reported data

About the Guest

Rachael Lawrance Senior Director & Functional Lead Statistics, Patient-Centered Outcomes (PCO), Adelphi Values

Rachael is a highly experienced statistician specializing in patient-centered outcomes. She works across the full lifecycle of PRO development—from qualitative research and questionnaire design to statistical validation and interpretation.

With a background in pharma and consultancy, she focuses on bridging the gap between rigorous statistical methodology and meaningful patient insights.

🔗 Links & References

• Adelphi Values
• The Effective Statistician
• PSI Conference 2026
• FDA Patient-Focused Drug Development (PFDD) Guidance Series
• PRO Consortium (PRO-C) resources

A Conversation with Alun Bedding and Emma May

Why You Should Listen

• You want to grow into a leadership role as a statistician
• You’re looking to improve communication and influence
• You want to better understand emotional intelligence in practice
• You aim to build trust and psychological safety within your team
• You want actionable leadership strategies you can apply immediately

What You Will Learn in This Episode

• Why emotional intelligence is critical for leadership effectiveness
• How to recognize and manage your own emotional responses
• The importance of psychological safety and how to create it
• Why coaching is more effective than giving answers
• How trust underpins influence and team performance
• The role of growth mindset in continuous development
• Practical communication techniques including the power of silence

Episode Highlights [00:02:00] A non-linear path into statistics Emma shares how she “fell into” statistics and discovered her passion for people development.

[00:04:00] Beyond technical expertise Why communication, emotional intelligence, and leadership skills are essential complements to technical knowledge.

[00:06:00] Emotional intelligence and career success How EQ contributes significantly to leadership effectiveness and workplace performance.

[00:07:00] Understanding emotions in practice Recognizing and managing emotions both your own and others’.

[00:10:00] The role of instinct vs rational thinking How immediate emotional reactions influence behavior and decision-making.

[00:12:00] Creating psychological safety in teams Using structured agreements to foster openness and trust.

[00:18:00] Speaking up and being brave Why courage is essential for maintaining trust and integrity.

[00:22:00] From advising to coaching Shifting from giving solutions to asking the right questions.

[00:25:00] Leadership as partnership Working collaboratively rather than solving problems alone.

[00:28:00] Trust as the foundation of leadership Why trust is essential for influence and effectiveness.

[00:30:00] Developing a growth mindset Learning from feedback and reframing failure as progress.

[00:34:00] The value of humility in leadership Why saying “I don’t know” can strengthen credibility.

[00:36:00] One key leadership habit curiosity Asking questions and actively listening to understand others.

[00:37:00] The power of silence How creating space in conversations leads to deeper insights.

About the Guest

Emma May Emma May is a statistician and leadership coach with a strong focus on professional development. Her work centers on helping individuals build the human skills such as emotional intelligence, communication, and trust that complement technical expertise and enable long-term career success.

A conversation with Manuel Cossio, Director Global AI at Cytel

Why You Should Listen:

If you’re curious about Generative AI but unsure how it truly fits into clinical development, medical writing, or statistical programming, this episode will give you clarity.

We talk openly about:

✔ What’s actually working right now in pharma

✔ Where GenAI can save significant time and reduce burnout

✔ How to use AI safely in regulated environments

✔ What to watch out for when it comes to hallucinations, governance, and data protection

Whether you’re a statistician, programmer, data scientist, or biometrics leader, this episode will help you see where AI can realistically support your work—and where human expertise remains essential.

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.

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