Pseudo-observations and super learner for the estimation of the restricted mean survival time

Ariane Cwiling (), Vittorio Perduca and Olivier Bouaziz
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Ariane Cwiling: Université Paris Cité, CNRS, MAP5
Vittorio Perduca: Université Paris Cité, CNRS, MAP5
Olivier Bouaziz: Université Paris Cité, CNRS, MAP5

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2025, vol. 31, issue 4, No 1, 713-746

Abstract: 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.

Keywords: Right-censoring; RMST; Prediction; Stacking; Pseudo-observations; Super learner (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10985-025-09668-9

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