Pseudo-observations and super learner for the estimation of the restricted mean survival time.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
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.
I · Intervention 중재 / 시술
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C · Comparison 대조 / 비교
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O · Outcome 결과 / 결론
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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In the context of right-censored data, we study the problem of predicting the restricted time to event based on a set of covariates.
APA
Cwiling A, Perduca V, Bouaziz O (2025). Pseudo-observations and super learner for the estimation of the restricted mean survival time.. Lifetime data analysis, 31(4), 713-746. https://doi.org/10.1007/s10985-025-09668-9
MLA
Cwiling A, et al.. "Pseudo-observations and super learner for the estimation of the restricted mean survival time.." Lifetime data analysis, vol. 31, no. 4, 2025, pp. 713-746.
PMID
40976812 ↗
Abstract 한글 요약
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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