ArticleComputational statistics & data analysis2021
Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection.
Article in Computational statistics & data analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Trial
- Variable selection for estimating optimal treatment regimes with multiple treatments.Scientific reports · 2026Article
- Simultaneous Feature Selection for Optimal Dynamic Treatment Regimens.Statistics in medicine · 2025Article
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Authors and funding
5 authors.
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Abstract
The goal of the optimal treatment regime is maximizing treatment benefits via personalized treatment assignments based on the observed patient and treatment characteristics. Parametric regression-based outcome learning approaches require exploring complex interplay between the outcome and treatment assignments adjusting for the patient and treatment covariates, yet correctly specifying such relationships is challenging. Thus, a robust method against misspecified models is desirable in practice. Parsimonious models are also desired to pursue a concise interpretation and to avoid including spurious predictors of the outcome or treatment benefits. These issues have not been comprehensively addressed in the presence of competing risks. Recognizing that competing risks and group variables are frequently present, we propose a doubly robust estimation with adaptive
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Registered trials
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