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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.06296 |
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| _version_ | 1866908772043063296 |
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| author | Jin, Man Fang, Yixin |
| author_facet | Jin, Man Fang, Yixin |
| contents | A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and control treatments. Next, an efficient estimation method is introduced: a targeted minimum loss estimator (TMLE) utilizing pseudo-observations. Moreover, a version of the copy reference (CR) approach is developed to perform a sensitivity analysis for right-censoring. The proposed TL framework is demonstrated using a real data application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06296 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Targeted Learning Framework for Estimating Restricted Mean Survival Time Difference using Pseudo-observations Jin, Man Fang, Yixin Methodology Machine Learning A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and control treatments. Next, an efficient estimation method is introduced: a targeted minimum loss estimator (TMLE) utilizing pseudo-observations. Moreover, a version of the copy reference (CR) approach is developed to perform a sensitivity analysis for right-censoring. The proposed TL framework is demonstrated using a real data application. |
| title | A Targeted Learning Framework for Estimating Restricted Mean Survival Time Difference using Pseudo-observations |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2601.06296 |