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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2006.16859 |
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| _version_ | 1866929591542611968 |
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| author | Chatton, A. Borgne, F. Le Leyrat, C. Foucher, Y. |
| author_facet | Chatton, A. Borgne, F. Le Leyrat, C. Foucher, Y. |
| contents | In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting (IPW) estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse probability weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2006_16859 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | G-computation and doubly robust standardisation for continuous-time data: a comparison with inverse probability weighting Chatton, A. Borgne, F. Le Leyrat, C. Foucher, Y. Methodology 62P10 In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting (IPW) estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse probability weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination. |
| title | G-computation and doubly robust standardisation for continuous-time data: a comparison with inverse probability weighting |
| topic | Methodology 62P10 |
| url | https://arxiv.org/abs/2006.16859 |