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- W4386248932 abstract "A multivariate mixed-effects model seems to be the most appropriate for gene expression data collected in a crossover trial. It is, however, difficult to obtain reliable results using standard statistical inference when some responses are missing. Particularly for crossover studies, missingness is a serious concern as the trial requires a small number of participants. A Monte Carlo EM (MCEM)-based technique was adopted to deal with this situation. In addition to estimation, MCEM likelihood ratio tests are developed to test fixed effects in crossover models with missing data. Intensive simulation studies were conducted prior to analyzing gene expression data." @default.
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- W4386248932 date "2023-06-01" @default.
- W4386248932 modified "2023-09-27" @default.
- W4386248932 title "Likelihood-based missing data analysis in crossover trials" @default.
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- W4386248932 doi "https://doi.org/10.1214/23-bjps570" @default.
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