The Effective Statistician - in association with PSI

By Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industryScience Natural Sciences

The Effective Statistician - in association with PSI episodes

November 02Move Beyond Excel with R for Health Technology Assessment
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:
" rel="noopener noreferrer">🔗 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 about PSI 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.
27 min
October 26Turn Data into Decisions by Putting People First
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:
" rel="noopener noreferrer">🔗 Connect with my guest, Dr. Annika Bergbauer
🔗 Listen to Annika’s podcast, Datenaffaire
🔗 Explore the Datenaffaire episode archive
🔗 Learn more about PSI 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.
24 min
October 19Better Dose Decisions and a Culture of Statistical Innovation
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
39 min
October 12Five Lessons for a Meaningful Career in Statistics
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
42 min
October 05Integrated Evidence Planning That Connects Drug Development to Patient Needs
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
34 min
September 28How to Make Statistics Click with Schools Outreach and Interactive Data Visualization
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
PSI Schools website: https://www.psiweb.org/careers/schools-zone
Email address for people interested in volunteering: [email protected]
Example of one of our Shiny workshop activities: mallettstats.shinyapps.io/Clinical-Trial-Workshop/
Steve Mallett's LinkedIn
28 min
September 07Will AI Replace Statisticians? The Future of Statistics in Pharma
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 about Chris Harbron and PoshStatsConnect with Chris Harbron on LinkedIn
🔗 Learn more about PSI 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.
25 min
September 01How to Build an Effective Patient-Reported Outcome Strategy for Clinical Trials
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.
21 min
August 11How the R Consortium Is Transforming Regulatory Submissions and AI in Clinical Trials
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.
19 min
July 21Project Optimus and what you need to know about it
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.
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.
26 min

About The Effective Statistician - in association with PSI

From the publisher's feed

The podcast from statisticians for statisticians to have a bigger impact at work. This podcast is set up in association with PSI - Promoting Statistical Insight. This podcast helps you to grow your…