How to analyze continuous and discrete repeated measures in small-sample cross-over trials?
Biometrics
; 79(4): 3998-4011, 2023 12.
Article
em En
| MEDLINE
| ID: mdl-37587671
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.
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Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Doenças Raras
Tipo de estudo:
Clinical_trials
/
Guideline
Limite:
Humans
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Bélgica