Cover Image

The joint survival super learner: A super learner for right-censored data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Munch, Anders, Gerds, Thomas A.
Format:	Preprint
Published:	2024
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The joint survival super learner: A super learner for right‐censored data
by: Anders Munch, et al.
Published: (2026)

Nonparametric inference for sublevel-set probabilities of conditional average treatment effect functions
by: Munch, Anders, et al.
Published: (2026)

The super learner for time-to-event outcomes: A tutorial
by: Keogh, Ruth H., et al.
Published: (2025)

Practical considerations for specifying a super learner
by: Phillips, Rachael V., et al.
Published: (2022)

What if we had built a prediction model with a survival super learner instead of a Cox model 10 years ago?
by: Chatton, Arthur, et al.
Published: (2024)

Cheap Subsampling bootstrap confidence intervals for fast and robust inference
by: Ohlendorff, Johan Sebastian, et al.
Published: (2025)

Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
by: Sun, Yuming, et al.
Published: (2026)

A new composite Mann-Whitney test for two-sample survival comparisons with right-censored data
by: Hussain, Abid, et al.
Published: (2025)

Towards R-learner with Continuous Treatments
by: Zhang, Yichi, et al.
Published: (2022)

Assessing dominance in survival functions: A test for right-censored data
by: Belzunce, Félix, et al.
Published: (2025)

Debiased machine learning for counterfactual survival functionals based on left-truncated right-censored data
by: Morenz, Eric R., et al.
Published: (2024)

Quantifying uncertainty of individualized treatment effects in right-censored survival data: A comparison of Bayesian additive regression trees and causal survival forest
by: Kabata, Daijiro, et al.
Published: (2025)

Efficient nonparametric estimators of discrimination measures with censored survival data
by: Breum, Marie Skov, et al.
Published: (2024)

Extension of W-method and A-learner for multiple binary outcomes
by: Yuki, Shintaro, et al.
Published: (2024)

Regression-based proximal causal inference for right-censored time-to-event data
by: Li, Kendrick, et al.
Published: (2024)

Designing clinical trials for the comparison of single and multiple quantiles with right-censored data
by: Farah, Beatriz, et al.
Published: (2025)

Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
by: Liang, Zhongyuan, et al.
Published: (2025)

A regularized multi-state model for covariate selection with interval-censored survival data
by: Bercu, Ariane, et al.
Published: (2025)

Practical considerations for variable screening in the super learner
by: Williamson, Brian D., et al.
Published: (2023)

Statistical methods for cost-effectiveness analysis of left-truncated censored survival data with treatment delays
by: Khudyakov, Polyna, et al.
Published: (2025)

On treating right-censoring events like treatments
by: Wen, Lan, et al.
Published: (2025)

Copula-based semiparametric nonnormal transformed linear model for survival data with dependent censoring
by: Yu, Huazhen, et al.
Published: (2024)

Treatment heterogeneity with right-censored outcomes using grf
by: Sverdrup, Erik, et al.
Published: (2023)

Neural interval-censored survival regression with feature selection
by: Meixide, Carlos García, et al.
Published: (2022)

Finite sample performance of optimal treatment rule estimators with right-censored outcomes
by: Jetsupphasuk, Michael, et al.
Published: (2024)

Laplacian-P-splines for shared Gamma frailty models applied to clustered right-censored time-to-event data
by: Lewczuk, Piotr, et al.
Published: (2026)

Improving inverse probability of censoring weighting for win statistics with composite survival outcomes
by: Fang, Xi, et al.
Published: (2026)

Estimating heritability of survival traits using censored multiple variance component model
by: Kim, Do Hyun, et al.
Published: (2025)

View selection in multi-view stacking: Choosing the meta-learner
by: van Loon, Wouter, et al.
Published: (2020)

Exploring the validity of the complete case analysis for regression models with a right-censored covariate
by: Ashner, Marissa C., et al.
Published: (2023)

Measuring frailty in the elderly: an indicator based on a super-classifier
by: Rebottini, Sara, et al.
Published: (2025)

M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model
by: Li, Xingyu, et al.
Published: (2025)

A discrete-time survival model to handle interval-censored covariates, with applications to HIV cohort studies
by: Kenny, Avi, et al.
Published: (2024)

A general joint latent class model of longitudinal and survival data with the covariance modelling
by: Miao, Ruoyu, et al.
Published: (2025)

Functional Cox model for interval-censored data
by: Xu, Yangjianchen, et al.
Published: (2026)

Causal inference for censored data with continuous marks
by: Qu, Lianqiang, et al.
Published: (2026)

Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
by: Pryce, Matthew, et al.
Published: (2026)

On a penalised likelihood approach for joint modelling of longitudinal covariates and partly interval-censored data -- an application to the Anti-PD1 brain collaboration trial
by: Webb, Annabel, et al.
Published: (2024)

Bounds for the regression parameters in dependently censored survival models
by: Willems, Ilias, et al.
Published: (2025)

A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data
by: Abbott, Madeline R., et al.
Published: (2024)