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

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Lifetime data analysis 2025 Vol.31(4) p. 713-746
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PICO 자동 추출 (휴리스틱, conf 2/4)

유사 논문
P · 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
추출되지 않음
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.

Cwiling A, Perduca V, Bouaziz O

ℹ️ 이 논문은 무료 전문이 아직 없습니다. 코퍼스 전체의 44.0%는 무료 가능 (통계 →) · 🏥 기관 EZproxy로 시도

📝 환자 설명용 한 줄

In the context of right-censored data, we study the problem of predicting the restricted time to event based on a set of covariates.

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↓ .bib ↓ .ris
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

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.

🏷️ 키워드 / MeSH 📖 같은 키워드 OA만

🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반