Cox Regression on the Plane
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914147657056256 |
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| author | Travis-Lumer, Yael Mandel, Micha Fabian, Ido Didi Betensky, Rebecca A. Gorfine, Malka |
| author_facet | Travis-Lumer, Yael Mandel, Micha Fabian, Ido Didi Betensky, Rebecca A. Gorfine, Malka |
| contents | The Cox proportional hazards model is the most widely used regression model in univariate survival analysis. Extensions of the Cox model to bivariate survival data, however, remain scarce. We propose two novel extensions based on a Lehmann-type representation of the survival function. The first, the simple Lehmann model, is a direct extension that retains a straightforward structure. The second, the generalized Lehmann model, allows greater flexibility by incorporating three distinct regression parameters and includes the simple Lehmann model as a special case. For both models, we derive the corresponding regression formulations for the three bivariate hazard functions and discuss their interpretation and model validity. To estimate the regression parameters, we adopt a bivariate pseudo-observations approach. For the generalized Lehmann model, we extend this approach to accommodate a trivariate structure: trivariate pseudo-observations and a trivariate link function. We then propose a two-step estimation procedure, where the marginal regression parameters are estimated in the first step, and the remaining parameters are estimated in the second step. Finally, we establish the consistency and asymptotic normality of the resulting estimators. We illustrate the approach using data from the Global Retinoblastoma Outcome Study. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_12473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cox Regression on the Plane Travis-Lumer, Yael Mandel, Micha Fabian, Ido Didi Betensky, Rebecca A. Gorfine, Malka Methodology The Cox proportional hazards model is the most widely used regression model in univariate survival analysis. Extensions of the Cox model to bivariate survival data, however, remain scarce. We propose two novel extensions based on a Lehmann-type representation of the survival function. The first, the simple Lehmann model, is a direct extension that retains a straightforward structure. The second, the generalized Lehmann model, allows greater flexibility by incorporating three distinct regression parameters and includes the simple Lehmann model as a special case. For both models, we derive the corresponding regression formulations for the three bivariate hazard functions and discuss their interpretation and model validity. To estimate the regression parameters, we adopt a bivariate pseudo-observations approach. For the generalized Lehmann model, we extend this approach to accommodate a trivariate structure: trivariate pseudo-observations and a trivariate link function. We then propose a two-step estimation procedure, where the marginal regression parameters are estimated in the first step, and the remaining parameters are estimated in the second step. Finally, we establish the consistency and asymptotic normality of the resulting estimators. We illustrate the approach using data from the Global Retinoblastoma Outcome Study. |
| title | Cox Regression on the Plane |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.12473 |