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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.00183 |
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| _version_ | 1866908681031909376 |
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| author | Petrakos, Niki Z. Moodie, Erica E. M. Savy, Nicolas |
| author_facet | Petrakos, Niki Z. Moodie, Erica E. M. Savy, Nicolas |
| contents | The current literature regarding generation of complex, realistic synthetic tabular data, particularly for randomized controlled trials (RCTs), often ignores missing data. However, missing data are common in RCT data and often are not Missing Completely At Random. We bridge the gap of determining how best to generate realistic synthetic data while also accounting for the missingness mechanism. We demonstrate how to generate synthetic missing values while ensuring that synthetic data mimic the targeted real data distribution. We propose and empirically compare several data generation frameworks utilizing various strategies for handling missing data (complete case, inverse probability weighting, and multiple imputation) by quantifying generation performance through a range of metrics. Focusing on the Missing At Random setting, we find that incorporating additional models to account for the missingness always outperformed a complete case approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Incorporating Missingness in a Framework for Generating Realistic Synthetic Randomized Controlled Trial Data Petrakos, Niki Z. Moodie, Erica E. M. Savy, Nicolas Other Statistics The current literature regarding generation of complex, realistic synthetic tabular data, particularly for randomized controlled trials (RCTs), often ignores missing data. However, missing data are common in RCT data and often are not Missing Completely At Random. We bridge the gap of determining how best to generate realistic synthetic data while also accounting for the missingness mechanism. We demonstrate how to generate synthetic missing values while ensuring that synthetic data mimic the targeted real data distribution. We propose and empirically compare several data generation frameworks utilizing various strategies for handling missing data (complete case, inverse probability weighting, and multiple imputation) by quantifying generation performance through a range of metrics. Focusing on the Missing At Random setting, we find that incorporating additional models to account for the missingness always outperformed a complete case approach. |
| title | Incorporating Missingness in a Framework for Generating Realistic Synthetic Randomized Controlled Trial Data |
| topic | Other Statistics |
| url | https://arxiv.org/abs/2512.00183 |