Copertina

C-learning in estimation of optimal individualized treatment regimes for recurrent disease

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhan, Zi-Shu, Zhang, Jin-Lun, Shi, Chen, Xu, Xiao-Han, Ou, Chun-Quan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Methodology
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Successive classification learning for estimating quantile optimal treatment regimes
di: Xia, Junwen, et al.
Pubblicazione: (2025)

Doubly robust estimation of optimal treatment regimes for survival data using an instrumental variable
di: Xia, Junwen, et al.
Pubblicazione: (2022)

Estimating optimal interpretable individualized treatment regimes from a classification perspective using adaptive LASSO
di: Zhang, Yunshu, et al.
Pubblicazione: (2026)

Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies
di: Son, Sungtaek, et al.
Pubblicazione: (2026)

Model selection with uncertainty in estimating optimal dynamic treatment regimes
di: Wang, Chunyu, et al.
Pubblicazione: (2025)

Robust estimation of optimal dynamic treatment regimes with nonignorable missing covariates
di: Sun, Jian, et al.
Pubblicazione: (2025)

An optimal dynamic treatment regime estimator for indefinite-horizon survival outcomes
di: She, Jane, et al.
Pubblicazione: (2025)

Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates
di: Sun, Jian, et al.
Pubblicazione: (2023)

Leveraging machine learning to estimate individualized treatment effects in cluster-randomized trials
di: Li, Changjun, et al.
Pubblicazione: (2026)

Augmented match weighted estimators for average treatment effects
di: Xu, Tanchumin, et al.
Pubblicazione: (2023)

Optimal individualized treatment regimes for survival data with competing risks
di: Zhou, Christina W., et al.
Pubblicazione: (2024)

Balancing utility and cost in dynamic treatment regimes
di: Chen, Kai, et al.
Pubblicazione: (2025)

Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care
di: Coulombe, Janie, et al.
Pubblicazione: (2025)

Sensitivity analysis for constructing optimal regimes in the presence of treatment non-compliance
di: Pham, Cuong T., et al.
Pubblicazione: (2022)

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments
di: Bian, Yuan, et al.
Pubblicazione: (2026)

Recent advances in doubly-robust weighted ordinary least squares techniques for dynamic treatment regime estimation
di: Nadi, Adel Ahmadi, et al.
Pubblicazione: (2025)

Randomization-based Z-estimation for evaluating average and individual treatment effects
di: Qu, Tianyi, et al.
Pubblicazione: (2024)

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

Double machine learning to estimate the effects of multiple treatments and their interactions
di: Xiang, Qingyan, et al.
Pubblicazione: (2025)

Efficient estimation of longitudinal treatment effects using difference-in-differences and machine learning
di: Illenberger, Nicholas, et al.
Pubblicazione: (2024)

Efficient estimation for flexible spatial zero-inflated models with environmental applications
di: Shen, Chung-Wei, et al.
Pubblicazione: (2025)

Enhanced inference for distributions and quantiles of individual treatment effects in various experiments
di: Chen, Zhe, et al.
Pubblicazione: (2024)

The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials
di: Chen, Zhe, et al.
Pubblicazione: (2023)

Nonparametric estimation of an optimal treatment rule with fused randomized trials and missing effect modifiers
di: Williams, Nicholas, et al.
Pubblicazione: (2025)

Improved bounds and inference on optimal regimes
di: Laurendeau, Julien D., et al.
Pubblicazione: (2024)

An adaptive design for optimizing treatment assignment in randomized clinical trials
di: Zhang, Wei, et al.
Pubblicazione: (2025)

A studentized permutation test for the treatment effect in individual participant data meta-analysis
di: Tran, Phuc Thien, et al.
Pubblicazione: (2025)

Non-parametric assessment of the calibration of individualized treatment effects
di: Sadatsafavi, Mohsen, et al.
Pubblicazione: (2025)

Optimal treatment regimes for the net benefit of a treatment
di: Petit, François, et al.
Pubblicazione: (2025)

Uncertainty quantification and multi-stage variable selection for personalized treatment regimes
di: Bi, Jiefeng, et al.
Pubblicazione: (2024)

Bad estimation, good prediction: the Lasso in dense regimes
di: Bratsberg, Andrea, et al.
Pubblicazione: (2025)

Rate doubly robust estimation for weighted average treatment effects
di: Wang, Yiming, et al.
Pubblicazione: (2025)

Generalizing conditional average treatment effects from nested randomized trials to all trial-eligible individuals
di: Wen, Lan, et al.
Pubblicazione: (2026)

Approximating optimal SMC proposal distributions in individual-based epidemic models
di: Rimella, Lorenzo, et al.
Pubblicazione: (2022)

Trustworthy assessment of heterogeneous treatment effect estimator
di: Gao, Zijun
Pubblicazione: (2024)

A causal fused lasso for interpretable heterogeneous treatment effects estimation
di: Padilla, Oscar Hernan Madrid, et al.
Pubblicazione: (2021)

Nonparametric efficient causal estimation of the intervention-specific expected number of recurrent events with continuous-time targeted maximum likelihood and highly adaptive lasso estimation
di: Rytgaard, Helene C. W., et al.
Pubblicazione: (2024)

Interactive identification of individuals with positive treatment effect while controlling false discoveries
di: Duan, Boyan, et al.
Pubblicazione: (2021)

Sparse two-stage Bayesian meta-analysis for individualized treatments
di: Shen, Junwei, et al.
Pubblicazione: (2024)

Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome
di: Zuo, Shuozhi, et al.
Pubblicazione: (2026)