Handling outcome-dependent missingness with binary responses: A Heckman-like model
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912710437896192 |
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| author | Doretti, Marco Stanghellini, Elena Taraborrelli, Alessandro |
| author_facet | Doretti, Marco Stanghellini, Elena Taraborrelli, Alessandro |
| contents | In regression models with missing outcomes, selection bias can arise when the missingness mechanism depends on the outcome itself. This proposal focuses on an extension of the Heckman model to a setting where the outcome is binary and both the selection process and the outcome are modeled through logistic regression. A correction term analogous to the inverse Mills' ratio is derived based on relative risks. Under given assumptions, such a strategy provides an effective tool for bias correction in the presence of informative missingness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Handling outcome-dependent missingness with binary responses: A Heckman-like model Doretti, Marco Stanghellini, Elena Taraborrelli, Alessandro Methodology In regression models with missing outcomes, selection bias can arise when the missingness mechanism depends on the outcome itself. This proposal focuses on an extension of the Heckman model to a setting where the outcome is binary and both the selection process and the outcome are modeled through logistic regression. A correction term analogous to the inverse Mills' ratio is derived based on relative risks. Under given assumptions, such a strategy provides an effective tool for bias correction in the presence of informative missingness. |
| title | Handling outcome-dependent missingness with binary responses: A Heckman-like model |
| topic | Methodology |
| url | https://arxiv.org/abs/2511.11776 |