Interpreting the relative treatment effect

Interview with Frank Konietschke

Why You Should Listen:

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.

You’ll walk away with:

✔ Non-parametric ≠ one test: A broad toolkit for two-group, multi-group, longitudinal, factorial, and covariate-adjusted designs.

✔ When ranks shine: Ordinal scales, heavy skew, small n (e.g., preclinical/animal studies), outliers, composite endpoints under the estimand framework.

✔ Interpretable effects without means: The probability-based “relative treatment effect”—“What’s the chance a random patient on A does better than a random patient on B?”

✔ Link to parametrics (when you must): How the rank-based effect relates to standardized mean differences under normality.

✔ Presenting results: Confidence intervals for rank-based effects and clean visualizations.

✔ Software exists: SAS macros and R packages for rank-based models (plus pointers to Frank’s book).

✔ Missing data & estimands: Practical thinking about composite strategies, treatment policy, and ongoing research for rank methods with missingness.

Episode Highlights:

00:00 – 03:31 | Welcome & setup
TES resources, PSI community, and why innovative methods often struggle with adoption.

03:32 – 06:00 | Meet Frank
From Göttingen to Munich, Texas, and back to Berlin; preclinical research focus.

06:01 – 09:11 | What are non-parametric analyses?
No strict distributional model; works for metric, ordinal, and binary data.

09:12 – 12:13 | Why ranks?
Small samples, unknown distributions; robustness when outliers occur.

12:14 – 14:35 | Where ranks are the better choice
Ordinal ratings (A/B/C/… without meaningful distances), outliers, skew, composites.

14:36 – 21:18 | Defining the treatment effect without means
Relative treatment effect as a probability (e.g., 60% = in 60% of random pairings, new treatment is better).
Connection to parametric world under normality assumptions.

21:19 – 23:13 | How to present it
Confidence intervals for rank-based effects and clear plots.

23:14 – 30:18 | Beyond two groups
Multi-arm trials, repeated measures, factorial designs, covariate adjustments; pseudo-ranks and why unweighted references improve interpretability and power properties.

30:19 – 35:33 | Missing data, real-world setups & estimands
Practical strategies (composites, treatment policy) and active research on rank methods with missingness.

35:34 – 39:41 | Collaboration & wrap-up
Research networks, software, and how statisticians can lead method adoption.

References:

• Book: Brunner, E., Bathke, A.C., Konietschke, F. (2019).  Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs -Using R and SAS. Springer
• Brunner, E., Konietschke, F., Pauly, M., & Puri, M. L. (2017). Rank‐based procedures in factorial designs: hypotheses about non‐parametric treatment effects. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(5), 1463-1485.
• Konietschke, F., Bathke, A. C., Hothorn, L. A., & Brunner, E. (2010). Testing and estimation of purely nonparametric effects in repeated measures designs. Computational Statistics & Data Analysis, 54(8), 1895-1905.
• Konietschke, F., Hothorn, L. A., & Brunner, E. (2012). Rank-based multiple test procedures and simultaneous confidence intervals. Electronic Journal of Statistics, 6, 738-759.
• Konietschke, F., Harrar, S. W., Lange, K., & Brunner, E. (2012). Ranking procedures for matched pairs with missing data—asymptotic theory and a small sample approximation. Computational Statistics & Data Analysis, 56(5), 1090-1102.

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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