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.
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.
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.
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 Benjamin 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.
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 Benjamin 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.
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 Benjamin 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.
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.
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.
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.
Generative AI is moving fast—and in pharma, it’s no longer just a buzzword. In this episode of The Effective Statistician Podcast, I speak with Manuel Cossio about how Generative AI is already being applied in real-world pharma settings, where it’s delivering value today, and what still needs careful consideration in regulated environments.
Manuel brings a unique hybrid background, combining molecular biology, genetics, pharma experience, and deep AI engineering expertise. He works at the cutting edge of AI in clinical development, including agentic systems, human-in-the-loop approaches, and large-scale document automation.
This conversation goes well beyond theory. We focus on practical use cases, real limitations, and how statisticians, programmers, and data scientists can responsibly use GenAI to become more effective.
Generative AI is moving fast—and in pharma, it’s no longer just a buzzword. In this episode of The Effective Statistician Podcast, I speak with Manuel Cossio about how Generative AI is already being applied in real-world pharma settings, where it’s delivering value today, and what still needs careful consideration in regulated environments.
Manuel brings a unique hybrid background, combining molecular biology, genetics, pharma experience, and deep AI engineering expertise. He works at the cutting edge of AI in clinical development, including agentic systems, human-in-the-loop approaches, and large-scale document automation.
This conversation goes well beyond theory. We focus on practical use cases, real limitations, and how statisticians, programmers, and data scientists can responsibly use GenAI to become more effective.
Generative AI is moving fast—and in pharma, it’s no longer just a buzzword. In this episode of The Effective Statistician Podcast, I speak with Manuel Cossio about how Generative AI is already being applied in real-world pharma settings, where it’s delivering value today, and what still needs careful consideration in regulated environments.
Manuel brings a unique hybrid background, combining molecular biology, genetics, pharma experience, and deep AI engineering expertise. He works at the cutting edge of AI in clinical development, including agentic systems, human-in-the-loop approaches, and large-scale document automation.
This conversation goes well beyond theory. We focus on practical use cases, real limitations, and how statisticians, programmers, and data scientists can responsibly use GenAI to become more effective.
As we start 2026, I want to take a moment to express my sincere gratitude to everyone who has been part of The Effective Statistician podcast. This episode features my co-host, Alun Bedding, reflecting on the past year and sharing thanks on behalf of both of us.
From our listeners who tune in, share episodes, and engage with the content, to our guests who generously share their expertise and real-world experiences — your participation makes this podcast both insightful and practical for statisticians worldwide.
I also want to recognize Reine and her production team. So much of the work that brings each episode to life happens behind the scenes, and we are deeply grateful for their professionalism and dedication.
We’re excited about what 2026 has in store. I look forward to continuing this journey with all of you and bringing even more valuable conversations to our community.
As we start 2026, I want to take a moment to express my sincere gratitude to everyone who has been part of The Effective Statistician podcast. This episode features my co-host, Alun Bedding, reflecting on the past year and sharing thanks on behalf of both of us.
From our listeners who tune in, share episodes, and engage with the content, to our guests who generously share their expertise and real-world experiences — your participation makes this podcast both insightful and practical for statisticians worldwide.
I also want to recognize Reine and her production team. So much of the work that brings each episode to life happens behind the scenes, and we are deeply grateful for their professionalism and dedication.
We’re excited about what 2026 has in store. I look forward to continuing this journey with all of you and bringing even more valuable conversations to our community.
As we start 2026, I want to take a moment to express my sincere gratitude to everyone who has been part of The Effective Statistician podcast. This episode features my co-host, Alun Bedding, reflecting on the past year and sharing thanks on behalf of both of us.
From our listeners who tune in, share episodes, and engage with the content, to our guests who generously share their expertise and real-world experiences — your participation makes this podcast both insightful and practical for statisticians worldwide.
I also want to recognize Reine and her production team. So much of the work that brings each episode to life happens behind the scenes, and we are deeply grateful for their professionalism and dedication.
We’re excited about what 2026 has in store. I look forward to continuing this journey with all of you and bringing even more valuable conversations to our community.
In this episode, I talk with my long-time friend and frequent guest, Kaspar Rufibach, about a skill that quietly determines how much impact we really have: presenting and communicating our work.
We walk through how Kaspar prepares his talks (including why he starts months in advance), how he structures messages so stakeholders actually remember and act on them, and why overcrowded slides are often just a sign that we haven’t done the hard thinking yet.
We also get honest about something many statisticians feel but rarely discuss: the fear of public speaking, the frustration of bad meetings, and the “personal brand” you build every time you present—whether you intend to or not.
If you’ve ever walked out of a meeting thinking “I don’t think they really understood what I meant,” this episode is for you.
In this episode, I talk with my long-time friend and frequent guest, Kaspar Rufibach, about a skill that quietly determines how much impact we really have: presenting and communicating our work.
We walk through how Kaspar prepares his talks (including why he starts months in advance), how he structures messages so stakeholders actually remember and act on them, and why overcrowded slides are often just a sign that we haven’t done the hard thinking yet.
We also get honest about something many statisticians feel but rarely discuss: the fear of public speaking, the frustration of bad meetings, and the “personal brand” you build every time you present—whether you intend to or not.
If you’ve ever walked out of a meeting thinking “I don’t think they really understood what I meant,” this episode is for you.
In this episode, I talk with my long-time friend and frequent guest, Kaspar Rufibach, about a skill that quietly determines how much impact we really have: presenting and communicating our work.
We walk through how Kaspar prepares his talks (including why he starts months in advance), how he structures messages so stakeholders actually remember and act on them, and why overcrowded slides are often just a sign that we haven’t done the hard thinking yet.
We also get honest about something many statisticians feel but rarely discuss: the fear of public speaking, the frustration of bad meetings, and the “personal brand” you build every time you present—whether you intend to or not.
If you’ve ever walked out of a meeting thinking “I don’t think they really understood what I meant,” this episode is for you.
In this episode, I’m joined by Deepa Jahagirdar, Associate Research Principal at Cytel, to explore what it really takes to build a good external control arm (ECA). Deepa brings a fascinating background from social epidemiology, where causal questions often need to be answered without running randomized trials. That experience translates directly into today’s growing need for ECAs, especially when we rely on real-world data to support single-arm trials, extension phases, or situations where randomization simply isn’t possible.
Together, we discuss how to choose the right data source, how target trial emulation works in practice, what to do about confounding, and how to judge whether an ECA is truly robust. If you’re working with real-world evidence, complex study designs, or causal inference, this episode will give you clarity and confidence in approaching ECAs the right way.
In this episode, I’m joined by Deepa Jahagirdar, Associate Research Principal at Cytel, to explore what it really takes to build a good external control arm (ECA). Deepa brings a fascinating background from social epidemiology, where causal questions often need to be answered without running randomized trials. That experience translates directly into today’s growing need for ECAs, especially when we rely on real-world data to support single-arm trials, extension phases, or situations where randomization simply isn’t possible.
Together, we discuss how to choose the right data source, how target trial emulation works in practice, what to do about confounding, and how to judge whether an ECA is truly robust. If you’re working with real-world evidence, complex study designs, or causal inference, this episode will give you clarity and confidence in approaching ECAs the right way.
In this episode, I’m joined by Deepa Jahagirdar, Associate Research Principal at Cytel, to explore what it really takes to build a good external control arm (ECA). Deepa brings a fascinating background from social epidemiology, where causal questions often need to be answered without running randomized trials. That experience translates directly into today’s growing need for ECAs, especially when we rely on real-world data to support single-arm trials, extension phases, or situations where randomization simply isn’t possible.
Together, we discuss how to choose the right data source, how target trial emulation works in practice, what to do about confounding, and how to judge whether an ECA is truly robust. If you’re working with real-world evidence, complex study designs, or causal inference, this episode will give you clarity and confidence in approaching ECAs the right way.
Why this episode made our all-time Top 9: If you’ve ever thought “non-parametric = Wilcoxon/Mann-Whitney and that’s it,” this conversation will happily destroy that myth. Frank shows how rank-based methods unlock rigorous analyses for skewed data, outliers, ordinal endpoints, small samples, composites/estimands—and how to communicate effects without relying on means.
Why this episode made our all-time Top 9: If you’ve ever thought “non-parametric = Wilcoxon/Mann-Whitney and that’s it,” this conversation will happily destroy that myth. Frank shows how rank-based methods unlock rigorous analyses for skewed data, outliers, ordinal endpoints, small samples, composites/estimands—and how to communicate effects without relying on means.
Why this episode made our all-time Top 9: If you’ve ever thought “non-parametric = Wilcoxon/Mann-Whitney and that’s it,” this conversation will happily destroy that myth. Frank shows how rank-based methods unlock rigorous analyses for skewed data, outliers, ordinal endpoints, small samples, composites/estimands—and how to communicate effects without relying on means.
Adaptive designs let us learn earlier, stop smarter, and protect patients—but they also make communication tricky. In this episode, Kaspar Rufibach and I dig into what “still correct” looks like when you try to explain results from group-sequential and other adaptive trials to regulators, clinicians, and scientific audiences. We unpack conditional vs. unconditional bias, median-unbiased estimation, stage-wise ordering for p-values, confidence intervals in multi-stage settings, and what to do with secondary endpoints and multiplicity. We also touch on ICHE20 (Adaptive Clinical Trials) and why pre-specification isn’t just a box-tick—it’s what builds trust.
Adaptive designs let us learn earlier, stop smarter, and protect patients—but they also make communication tricky. In this episode, Kaspar Rufibach and I dig into what “still correct” looks like when you try to explain results from group-sequential and other adaptive trials to regulators, clinicians, and scientific audiences. We unpack conditional vs. unconditional bias, median-unbiased estimation, stage-wise ordering for p-values, confidence intervals in multi-stage settings, and what to do with secondary endpoints and multiplicity. We also touch on ICHE20 (Adaptive Clinical Trials) and why pre-specification isn’t just a box-tick—it’s what builds trust.
Adaptive designs let us learn earlier, stop smarter, and protect patients—but they also make communication tricky. In this episode, Kaspar Rufibach and I dig into what “still correct” looks like when you try to explain results from group-sequential and other adaptive trials to regulators, clinicians, and scientific audiences. We unpack conditional vs. unconditional bias, median-unbiased estimation, stage-wise ordering for p-values, confidence intervals in multi-stage settings, and what to do with secondary endpoints and multiplicity. We also touch on ICHE20 (Adaptive Clinical Trials) and why pre-specification isn’t just a box-tick—it’s what builds trust.
In this episode, I’m joined once again by my friend and frequent guest, Kaspar Rufibach, to talk about a topic that’s been around for decades but is gaining fresh attention thanks to the new ICH E20 draft guideline—adaptive designs in confirmatory clinical trials.
Kaspar and I discuss why and when we should consider adapting a clinical trial, what kinds of adaptations are statistically valid and meaningful in a regulatory context, and why these designs—despite their efficiency—are still not used as often as they could be.
We also dive into the statistical foundations behind adaptive designs, such as p-value combination methods and meta-analytic thinking, and explore how adaptive approaches can help us make faster and smarter decisions in drug development.
In this episode, I’m joined once again by my friend and frequent guest, Kaspar Rufibach, to talk about a topic that’s been around for decades but is gaining fresh attention thanks to the new ICH E20 draft guideline—adaptive designs in confirmatory clinical trials.
Kaspar and I discuss why and when we should consider adapting a clinical trial, what kinds of adaptations are statistically valid and meaningful in a regulatory context, and why these designs—despite their efficiency—are still not used as often as they could be.
We also dive into the statistical foundations behind adaptive designs, such as p-value combination methods and meta-analytic thinking, and explore how adaptive approaches can help us make faster and smarter decisions in drug development.
In this episode, I’m joined once again by my friend and frequent guest, Kaspar Rufibach, to talk about a topic that’s been around for decades but is gaining fresh attention thanks to the new ICH E20 draft guideline—adaptive designs in confirmatory clinical trials.
Kaspar and I discuss why and when we should consider adapting a clinical trial, what kinds of adaptations are statistically valid and meaningful in a regulatory context, and why these designs—despite their efficiency—are still not used as often as they could be.
We also dive into the statistical foundations behind adaptive designs, such as p-value combination methods and meta-analytic thinking, and explore how adaptive approaches can help us make faster and smarter decisions in drug development.
