ArticleBiometrika2018
Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes.
Article in Biometrika, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.The annals of applied statistics · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Learning optimal dynamic treatment regimes from longitudinal data.American journal of epidemiology · 2024Article
- An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Optimally Choosing Medication Type for Patients With Opioid Use Disorder.American journal of epidemiology · 2023Article
- When the Ends do not Justify the Means: Learning Who is Predicted to Have Harmful Indirect Effects.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Semiparametric single-index models for optimal treatment regimens with censored outcomes.Lifetime data analysis · 2022Article
- Joint modeling and multiple comparisons with the best of data from a SMART with survival outcomes.Biostatistics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We consider estimation of an optimal individualized treatment rule when a high-dimensional vector of baseline variables is available. Our optimality criterion is with respect to delaying the expected time to occurrence of an event of interest. We use semiparametric efficiency theory to construct estimators with properties such as double robustness. We propose two estimators of the optimal rule, which arise from considering two loss functions aimed at directly estimating the conditional treatment effect and recasting the problem in terms of weighted classification using the 0-1 loss function. Our estimated rules are ensembles that minimize the crossvalidated risk of a linear combination in a user-supplied library of candidate estimators. We prove oracle inequalities bounding the finite-sample excess risk of the estimator. The bounds depend on the excess risk of the oracle selector and a doubly robust term related to estimation of the nuisance parameters. We discuss the convergence rates of our estimator to the oracle selector, and illustrate our methods by analysis of a phase III randomized study testing the efficacy of a new therapy for the treatment of breast cancer.
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