Search Results for: CALC

This final episode aims to prepare you for the interview process. You’ll learn what to expect at an interview or assessment center, and hear experiences from both sides of the table. An interesting listen that you won’t want to miss!

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.

We’re bringing back one of our most downloaded episodes ever – a deep dive into how adverse events should be analyzed properly. This conversation with Jan Beyersmann and Kaspar Rufibach is packed with methodological insights and practical implications for statisticians working in clinical trials.

Adverse event (AE) analysis has long been approached differently from efficacy analysis, often using overly simplistic methods that can bias results. In this episode, we discuss why that’s a problem – and how the SAVVY collaboration (Survival analysis for AdVerse events with Varying follow-up times) is pushing the field forward.

Together with academia and multiple pharma companies, this collaboration tackled the issue of AE analysis using real randomized trial data, not just simulations. The findings show how common methods can underestimate or overestimate event probabilities and how established statistical methods can be applied more consistently to ensure fair benefit–risk assessments.

If you’ve ever wondered whether your approach to safety analysis is leading to misleading conclusions, this episode is a must-listen.

This episode originally struck a chord with statisticians around the world—and for good reason. Whether you’re just starting with real-world evidence (RWE) or mentoring someone who is, this conversation is packed with practical lessons that will help you navigate the complexities of observational data with more confidence.

In this special replay, guest Rachel Tham and I reflect on the real-world analysis mistakes, misconceptions, and growing pains they wish someone had warned them about earlier in their careers.

From ambiguous index dates to messy exposure definitions and unexpected data quirks—this episode will save you hours of rework and help you better manage timelines and expectations.