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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.20606 |
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| _version_ | 1866916973740294144 |
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| author | Huang, Yifei Tong, Liping Yang, Jie |
| author_facet | Huang, Yifei Tong, Liping Yang, Jie |
| contents | In the context of paid research studies and clinical trials, budget considerations often require patient sampling from available populations which comes with inherent constraints. We introduce the R package CDsampling, which is the first to our knowledge to integrate optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy when the model information is limited. To demonstrate its efficacy, we provide simulated examples and a real-data example with datasets embedded in the package and compare them with classical sampling methods. Furthermore, the package revisits the theoretical results of the Fisher information matrix for generalized linear models (including regular linear regression model) and multinomial logistic models, offering functions for its computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20606 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CDsampling: An R Package for Constrained D-Optimal Sampling in Paid Research Studies Huang, Yifei Tong, Liping Yang, Jie Computation Applications In the context of paid research studies and clinical trials, budget considerations often require patient sampling from available populations which comes with inherent constraints. We introduce the R package CDsampling, which is the first to our knowledge to integrate optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy when the model information is limited. To demonstrate its efficacy, we provide simulated examples and a real-data example with datasets embedded in the package and compare them with classical sampling methods. Furthermore, the package revisits the theoretical results of the Fisher information matrix for generalized linear models (including regular linear regression model) and multinomial logistic models, offering functions for its computation. |
| title | CDsampling: An R Package for Constrained D-Optimal Sampling in Paid Research Studies |
| topic | Computation Applications |
| url | https://arxiv.org/abs/2410.20606 |