A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures
In this paper, we propose a family of correlation structures for crossover designs with repeated measures for both, Gaussian and non-Gaussian responses using generalized estimating equations (GEE). The structure considers two matrices: one that models between-period correlation and another one that...
| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
Springer Nature
2024
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| Materias: | |
| Acceso en línea: | https://link.springer.com/article/10.1007/s00362-022-01391-z http://hdl.handle.net/20.500.12324/39989 https://doi.org/10.1007/s00362-022-01391-z |
| Sumario: | In this paper, we propose a family of correlation structures for crossover designs with repeated measures for both, Gaussian and non-Gaussian responses using generalized estimating equations (GEE). The structure considers two matrices: one that models between-period correlation and another one that models within-period correlation. The overall correlation matrix, which is used to build the GEE, corresponds to the Kronecker between these matrices. A procedure to estimate the parameters of the correlation matrix is proposed, its statistical properties are studied and a comparison with standard models using a single correlation matrix is carried out. A simulation study showed a superior performance of the proposed structure in terms of the quasi-likelihood criterion, efficiency, and the capacity to explain complex correlation phenomena patterns in longitudinal data from crossover designs. |
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