ArticleStatistical methods in medical research2023
A reference-free R-learner for treatment recommendation.
Article in Statistical methods in medical research, 2023. 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.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Estimating Optimal Site-Specific Individualized Treatment Rules With Limited Data Sharing.Statistics in medicine · 2026Article
- Multi-study R-learner for estimating heterogeneous treatment effects across studies using statistical machine learning.Biostatistics (Oxford, England) · 2025Article
- A Simple Nonparametric Least-Squares-Based Causal Inference for Heterogeneous Treatment Effects.Journal of nonparametric statistics · 2025Article
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Authors and funding
3 authors.
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Abstract
Assigning optimal treatments to individual patients based on their characteristics is the ultimate goal of precision medicine. Deriving evidence-based recommendations from observational data while considering the causal treatment effects and patient heterogeneity is a challenging task, especially in situations of multiple treatment options. Herein, we propose a reference-free R-learner based on a simplex algorithm for treatment recommendation. We showed through extensive simulation that the proposed method produced accurate recommendations that corresponded to optimal treatment outcomes, regardless of the reference group. We used the method to analyze data from the Systolic Blood Pressure Intervention Trial (SPRINT) and achieved recommendations consistent with the current clinical guidelines.
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