A Penalized Functional Linear Cox Regression Model for Spatially-defined Environmental Exposure with an Estimated Buffer Distance
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910285170737152 |
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| author | Lee, Jooyoung He, Zhibing Roscoe, Charlotte James, Peter Xu, Li Spiegelman, Donna Zucker, David Wang, Molin |
| author_facet | Lee, Jooyoung He, Zhibing Roscoe, Charlotte James, Peter Xu, Li Spiegelman, Donna Zucker, David Wang, Molin |
| contents | In environmental health research, it is of interest to understand the effect of the neighborhood environment on health. Researchers have shown a protective association between green space around a person's residential address and depression outcomes. In measuring exposure to green space, distance buffers are often used. However, buffer distances differ across studies. Typically, the buffer distance is determined by researchers a priori. It is unclear how to identify an appropriate buffer distance for exposure assessment. To address geographic uncertainty problem for exposure assessment, we present a domain selection algorithm based on the penalized functional linear Cox regression model. The theoretical properties of our proposed method are studied and simulation studies are conducted to evaluate finite sample performances of our method. The proposed method is illustrated in a study of associations of green space exposure with depression and/or antidepressant use in the Nurses' Health Study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00461 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Penalized Functional Linear Cox Regression Model for Spatially-defined Environmental Exposure with an Estimated Buffer Distance Lee, Jooyoung He, Zhibing Roscoe, Charlotte James, Peter Xu, Li Spiegelman, Donna Zucker, David Wang, Molin Methodology In environmental health research, it is of interest to understand the effect of the neighborhood environment on health. Researchers have shown a protective association between green space around a person's residential address and depression outcomes. In measuring exposure to green space, distance buffers are often used. However, buffer distances differ across studies. Typically, the buffer distance is determined by researchers a priori. It is unclear how to identify an appropriate buffer distance for exposure assessment. To address geographic uncertainty problem for exposure assessment, we present a domain selection algorithm based on the penalized functional linear Cox regression model. The theoretical properties of our proposed method are studied and simulation studies are conducted to evaluate finite sample performances of our method. The proposed method is illustrated in a study of associations of green space exposure with depression and/or antidepressant use in the Nurses' Health Study. |
| title | A Penalized Functional Linear Cox Regression Model for Spatially-defined Environmental Exposure with an Estimated Buffer Distance |
| topic | Methodology |
| url | https://arxiv.org/abs/2401.00461 |