Interview with Frank Konietschke
Frank has something in common with Benjamin and myself – we all studied statistics in Göttingen and learned and researched in non-parametric statistics. Afterwards, we went into different paths. Whereas Benjamin started at a CRO and I joined large pharma organizations, Frank continued on the academic track.
He recently became Professor at the famous Charitee in Berlin, where he’s still dedicating a lot of research to the field of non-parametric statistics. However, he’s not an ivory tower researcher but also applies these approaches in the medical research he’s taking part in.
Learn about a whole universe of different approaches, which will help you overcome many limitations of the methods, which you’re using daily.
In today’s episode, we’ll cover the following questions:
- What are non-parametric analyses?
- How can you distinguish between parametric, semi-parametic and non-parametric analyses
- How do ranks work best and when should we use ranks?
- How can we describe treatment effects when using ranks?
- What is the relationship between the relative effect and common treatment descriptions e.g. in the continuous case and binary case?
- What are the advantages and problems with these rank based approaches?
- How does it work, if I have multiple time points, multiple arms, covariates, etc?
- What is relevant literature to read?
- Are there any tips on implementing these approaches, i.e. programming help?
The references below will help you learn more about these approaches and give you the tools to implement them.
Have fun listening to this episode and share it with your colleagues.
About Frank Konietschke
Frank has done extensive research on methodological developments in nonparametric statistics including ranking procedures and resampling methods for various designs and models. His results are published in numerous publications in various journals including two papers in “the highest-quality journal” Journal of the Royal Statistical Society Series B. Recently, he published a book on nonparametric statistics the Springer Series in Statistics. He lectured and taught nonparametric statistics on almost every continent and has been invited speaker at about 80 different universities, companies and research institutions. Currently, he is professor of Statistics at the Charite Berlin, where he leads a research group working on the development and application of statistical methods of translation and early clinical trials.
- 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.
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