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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2303.05443 |
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| _version_ | 1866910109529014272 |
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| author | Pareek, Savita Das, Kalyan Mukhopadhyay, Siuli |
| author_facet | Pareek, Savita Das, Kalyan Mukhopadhyay, Siuli |
| contents | This work proposes a statistical model for crossover trials with multiple skewed responses measured in each period. A 3 $\times$ 3 crossover trial data where different drug doses were administered to subjects with a history of seasonal asthma rhinitis to grass pollen is used for motivation. In each period, gene expression values for ten genes were measured from each subject. It considers a linear mixed effect model with skew normally distributed random effect or random error term to model the asymmetric responses in the crossover trials. The paper examines cases (i) when a random effect follows a skew-normal distribution, as well as (ii) when a random error follows a skew-normal distribution. The EM algorithm is used in both cases to compute maximum likelihood estimates of parameters. Simulations and crossover data from the gene expression study illustrate the proposed approach.
Keywords: Crossover design, Mixed effect models, Skew-normal distribution, EM algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_05443 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Likelihood-based Inference for Skewed Responses in a Crossover Trial Setup Pareek, Savita Das, Kalyan Mukhopadhyay, Siuli Methodology This work proposes a statistical model for crossover trials with multiple skewed responses measured in each period. A 3 $\times$ 3 crossover trial data where different drug doses were administered to subjects with a history of seasonal asthma rhinitis to grass pollen is used for motivation. In each period, gene expression values for ten genes were measured from each subject. It considers a linear mixed effect model with skew normally distributed random effect or random error term to model the asymmetric responses in the crossover trials. The paper examines cases (i) when a random effect follows a skew-normal distribution, as well as (ii) when a random error follows a skew-normal distribution. The EM algorithm is used in both cases to compute maximum likelihood estimates of parameters. Simulations and crossover data from the gene expression study illustrate the proposed approach. Keywords: Crossover design, Mixed effect models, Skew-normal distribution, EM algorithm. |
| title | Likelihood-based Inference for Skewed Responses in a Crossover Trial Setup |
| topic | Methodology |
| url | https://arxiv.org/abs/2303.05443 |