gms | German Medical Science

66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF)

26. - 30.09.2021, online

Ranking Procedures for the Repeated Measures Design with Clustered and Missing Data

Meeting Abstract

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  • Kerstin Rubarth - Charité - Universitätsmedizin Berlin - Institut für Biometrie und klinische Epidemiologie, Berlin, Germany
  • Frank Konietschke - Charité - Universitätsmedizin Berlin- Institut für Biometrie und klinische Epidemiologie, Berlin, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). sine loco [digital], 26.-30.09.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 115

doi: 10.3205/21gmds083, urn:nbn:de:0183-21gmds0839

Veröffentlicht: 24. September 2021

© 2021 Rubarth et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Introduction: A commonly used design in health, medical and biomedical research is the repeated measures design. Often, a parametric model is used for the analysis of such data. However, if sample size is rather small or if data is skewed or is on an ordinal scale, a nonparametric approach would fit the data better than a classic parametric approach, e.g. linear mixed models. Another issue, that naturally arises when dealing with clinical or pre-clinical data, is the occurrence of missing data. Most methods can only use a complete data set, if no imputation technique is applied. Furthermore, especially in preclinical trials, dependent replicates (cluster data) sometimes occur, e.g. animals sharing the same cage should not be analyzed as if they were independent. Therefore, the proposed framework will be applicable to repeated measures data with missing data and dependent replicates.

Methods: The newly developed ranking procedure is a flexible method for general non-normal, ordinal, ordered categorical and even binary data and uses in case of missing data all available information instead of only the information obtained from complete cases. The hypotheses are defined in terms of the nonparametric relative effect [1], [2] and can be tested by using quadratic test procedures as well as the multiple contrast test procedure (MCTP) [3]. Additionally, the framework allows for the incorporation of clustered data within the repeated measurements, e.g. animal experiments, where several animals share the same cage and are therefore clustered within a cage.

Results: Simulation studies indicate a good performance in terms of the type-I error rate and the power under different alternatives with a missing rate up to 30%, also under non-normal data and missingness at random (MAR) scenarios. A real data example illustrates the application of the proposed methodology.

Discussion and Conclusion: Missing values is a frequently occurring problem in repeated measures designs. We extended the methods of Konietschke et al. [4] to allow for missing data as well as dependent replicates and could show that even in scenarios with small sample sizes, high missing rates up to 30% the newly proposed methodology controls the type-I error rate. Further simulation studies indicated that in ’extreme’ scenarios, e.g very small sample sizes, extremely high missing probabilities, heteroscedasticity and high correlations, the MCTP tends to be liberal.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


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