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How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?

Author

Listed:
  • Johan Verbeeck
  • Martin Geroldinger
  • Konstantin Thiel
  • Andrew Craig Hooker
  • Sebastian Ueckert
  • Mats Karlsson
  • Arne Cornelius Bathke
  • Johann Wolfgang Bauer
  • Geert Molenberghs
  • Georg Zimmermann

Abstract

To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross‐over design. However, it is unclear how these within‐treatment period and within‐subject clustered data are best analyzed in small‐sample trials. In a real‐data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non‐parametric marginal models, generalized pairwise comparison models, GEE‐type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross‐over design depends on the type of outcome and the number of time points the treatment has an effect on. The non‐parametric marginal model testing the treatment–time‐interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due to incompleteness.

Suggested Citation

  • Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann, 2023. "How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?," Biometrics, The International Biometric Society, vol. 79(4), pages 3998-4011, December.
  • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3998-4011
    DOI: 10.1111/biom.13920
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    1. Noguchi, Kimihiro & Gel, Yulia R. & Brunner, Edgar & Konietschke, Frank, 2012. "nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i12).
    2. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    3. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    4. William N. Anderson & Johan Verbeeck, 2023. "Exact Permutation and Bootstrap Distribution of Generalized Pairwise Comparisons Statistics," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    5. S. le Cessie & J. C. van Houwelingen, 1994. "Logistic Regression for Correlated Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 95-108, March.
    6. Beunckens, Caroline & Sotto, Cristina & Molenberghs, Geert, 2008. "A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1533-1548, January.
    7. F. Bretz & J. C. Pinheiro & M. Branson, 2005. "Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies," Biometrics, The International Biometric Society, vol. 61(3), pages 738-748, September.
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