Podcast
The Effective Statistician - in association with PSI
Hosted by Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry · EN
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 leadership skills, learn about ongoing discussions in the scientific community, build you knowledge about the health sector and be more efficient at work. This podcast helps statisticians at all levels with and without management experience. It is targeted towards the health, but lots of topics will be important for the wider data scientists community.
11episodes
Episodes
Newest firstAll episodes
Unlocking Growth: The Power of Coaching and Mentoring for Statisticians
1w ago00:35:40Tap to summarizeA 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
Transcribe →Understanding and Mitigating Endpoint Bias in External Control Arms
Jun 900:28:41Tap to summarizeA 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.
Transcribe →The Future of Statistical Methodology in Drug Development
Jun 200:31:52Tap to summarizeA 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/
Transcribe →Rethinking Programming Validation and Traceability in Clinical Trials
May 2600:23:24Tap to summarizeA 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
Transcribe →How Applied Improvisation Develops and Reinforces Interpersonal Skills
May 400:33:16Tap to summarizeA 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
Transcribe →What does a clinical lead expect from a statistician?
Apr 2700:32:12Tap to summarizeA 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
Transcribe →Measuring Trust – A Key to Effective Leadership
Apr 2100:27:51Tap to summarizeA 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.
Transcribe →Choosing and Interpreting PROs and COAs – A Guide for Clinical Trial Statisticians
Apr 1700:41:26Tap to summarizeInterview 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
Transcribe →How Statisticians Can Build Trust and Lead with Emotional Intelligence
Mar 2300:39:15Tap to summarizeA 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.
Transcribe →The Evolving Role of Generative AI in Pharma
Jan 2000:33:08Tap to summarizeA 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. 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.
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