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• W3197804921 abstract "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." @default.
• W3197804921 created "2021-09-13" @default.
• W3197804921 creator A5019460939 @default.
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• W3197804921 date "2020-06-30" @default.
• W3197804921 modified "2023-09-26" @default.
• W3197804921 title "G-computation and doubly robust standardisation for continuous-time data: a comparison with inverse probability weighting" @default.
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• W3197804921 doi "https://doi.org/10.48550/arxiv.2006.16859" @default.
• W3197804921 hasPublicationYear "2020" @default.
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