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Preprints, Working Papers, ... Year : 2024

Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time

Ariane Cwiling
Olivier Bouaziz

Abstract

In the context of right-censored data, we study the problem of predicting the restricted time to event based on a set of covariates. Under a quadratic loss, this problem is equivalent to estimating the conditional Restricted Mean Survival Time (RMST). To that aim, we propose a flexible and easy-to-use ensemble algorithm that combines pseudo-observations and super learner. The classical theoretical results of the super learner are extended to right-censored data, using a new definition of pseudo-observations, the so-called split pseudo-observations. Simulation studies indicate that the split pseudo-observations and the standard pseudo-observations are similar even for small sample sizes. The method is applied to maintenance and colon cancer datasets, showing the interest of the method in practice, as compared to other prediction methods. We complement the predictions obtained from our method with our RMST-adapted risk measure, prediction intervals and variable importance measures developed in a previous work.
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Dates and versions

hal-04559520 , version 1 (25-04-2024)

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  • HAL Id : hal-04559520 , version 1

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Ariane Cwiling, Vittorio Perduca, Olivier Bouaziz. Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time. 2024. ⟨hal-04559520⟩
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