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Geroldinger M, Verbeeck J, Thiel KE, Molenberghs G, Bathke AC, Laimer M, Zimmermann G. A neutral comparison of statistical methods for analyzing longitudinally measured ordinal outcomes in rare diseases. Biom J 2024; 66:e2200236. [PMID: 36890631 DOI: 10.1002/bimj.202200236] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 12/27/2022] [Accepted: 01/30/2023] [Indexed: 03/10/2023]
Abstract
Ordinal data in a repeated measures design of a crossover study for rare diseases usually do not allow for the use of standard parametric methods, and hence, nonparametric methods should be considered instead. However, only limited simulation studies in settings with small sample sizes exist. Therefore, starting from an Epidermolysis Bullosa simplex trial with the above-mentioned design, a rank-based approach using the R package nparLD and different generalized pairwise comparisons (GPC) methods were compared impartially in a simulation study. The results revealed that there was not one single best method for this particular design, because a trade-off exists between achieving high power, accounting for period effects, and for missing data. Specifically, nparLD as well as the unmatched GPC approaches do not address crossover aspects, and the univariate GPC variants partly ignore the longitudinal information. The matched GPC approaches, on the other hand, take the crossover effect into account in the sense of incorporating the within-subject association. Overall, the prioritized unmatched GPC method achieved the highest power in the simulation scenarios, although this may be due to the specified prioritization. The rank-based approach yielded good power even at a sample size ofN = 6 $N=6$ , whereas the matched GPC method could not control the type I error.
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Affiliation(s)
- Martin Geroldinger
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Salzburg, Austria
- Department of Research and Innovation, Paracelsus Medical University, Salzburg, Austria
| | - Johan Verbeeck
- Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium
| | - Konstantin E Thiel
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Salzburg, Austria
- Department of Research and Innovation, Paracelsus Medical University, Salzburg, Austria
| | - Geert Molenberghs
- Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), KULeuven, Leuven, Belgium
| | - Arne C Bathke
- Intelligent Data Analytics (IDA) Lab Salzburg, Department of Artificial Intelligence and Human Interfaces, Faculty of Digital and Analytical Sciences, Paris Lodron University of Salzburg, Salzburg, Austria
| | - Martin Laimer
- Department of Dermatology and Allergology, Paracelsus Medical University, Salzburg, Austria
| | - Georg Zimmermann
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Salzburg, Austria
- Department of Research and Innovation, Paracelsus Medical University, Salzburg, Austria
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