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

TL;DR

This paper introduces a novel ensemble method combining pseudo-observations and super learner to accurately estimate the restricted mean survival time in right-censored data, with demonstrated practical effectiveness.

Contribution

It extends super learner theory to right-censored data using split pseudo-observations, providing a flexible, practical approach for RMST estimation.

Findings

Split pseudo-observations perform similarly to standard ones in small samples.

The method outperforms existing prediction techniques in real datasets.

Provides additional tools like risk measures and variable importance for survival analysis.

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.