Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/43161
Title: How to analyze continuous and discrete repeated measures in small-sample cross-over trials?
Authors: VERBEECK, Johan 
Geroldinger, M
Thiel, K
Hooker, AC
Ueckert, S
Karlsson, M
Bathke, AC
Bauer, JW
MOLENBERGHS, Geert 
Zimmermann, G
Issue Date: 2023
Publisher: WILEY
Source: BIOMETRICS, 79 (4) , p. 3998 -4011
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.
Keywords: Barnard test;cross-over;epidermolysis bullosa simplex;GEE;generalized pairwise comparison;model averaging;non-parametric marginal model;rare diseases;repeated measures
Document URI: http://hdl.handle.net/1942/43161
ISSN: 0006-341X
e-ISSN: 1541-0420
DOI: 10.1111/biom.13920
ISI #: 001049312800001
Rights: 2023 The International Biometric Society.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

Show full item record

WEB OF SCIENCETM
Citations

2
checked on Oct 12, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.