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 th...
| Main Authors: | , , |
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| Format: | article |
| Language: | Inglés |
| Published: |
Springer Nature
2024
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| Subjects: | |
| Online Access: | https://link.springer.com/article/10.1007/s00362-022-01391-z http://hdl.handle.net/20.500.12324/39372 https://doi.org/10.1007/s00362-022-01391-z |
| Summary: | 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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