Efficient Targeted Maximum Likelihood Estimators for Two-Phase Design Problems
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914357947924480 |
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| author | Qiu, Sky Gruber, Susan Shaw, Pamela A. Williamson, Brian D. van der Laan, Mark J. |
| author_facet | Qiu, Sky Gruber, Susan Shaw, Pamela A. Williamson, Brian D. van der Laan, Mark J. |
| contents | In a typical two-phase design, a random sample is drawn from the target population in phase 1, during which only a subset of variables is collected. In phase 2, a subsample of the phase-1 cohort is selected, and additional variables are measured. This setting induces a coarsened data structure on the data from the second phase. We assume coarsening at random, that is, the phase-2 sampling mechanism depends only on variables fully observed. We review existing estimators, including the generalized raking estimator and the inverse probability of censoring weighted targeted maximum likelihood estimation (IPCW-TMLE) along with its extensions that also target the phase-2 sampling mechanism to improve efficiency. We further introduce a new class of estimators constructed within the TMLE framework that are asymptotically equivalent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_24131 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Targeted Maximum Likelihood Estimators for Two-Phase Design Problems Qiu, Sky Gruber, Susan Shaw, Pamela A. Williamson, Brian D. van der Laan, Mark J. Methodology Machine Learning In a typical two-phase design, a random sample is drawn from the target population in phase 1, during which only a subset of variables is collected. In phase 2, a subsample of the phase-1 cohort is selected, and additional variables are measured. This setting induces a coarsened data structure on the data from the second phase. We assume coarsening at random, that is, the phase-2 sampling mechanism depends only on variables fully observed. We review existing estimators, including the generalized raking estimator and the inverse probability of censoring weighted targeted maximum likelihood estimation (IPCW-TMLE) along with its extensions that also target the phase-2 sampling mechanism to improve efficiency. We further introduce a new class of estimators constructed within the TMLE framework that are asymptotically equivalent. |
| title | Efficient Targeted Maximum Likelihood Estimators for Two-Phase Design Problems |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2602.24131 |