Personalized treatment selection using observational data
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Publication:6107640
DOI10.1080/02664763.2021.2019689OpenAlexW4205619491MaRDI QIDQ6107640
Unnamed Author, Maiying Kong, K. B. Kulasekera
Publication date: 3 July 2023
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062224
Cites Work
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- The Adaptive Lasso and Its Oracle Properties
- Performance guarantees for individualized treatment rules
- Analysis of randomized comparative clinical trial data for personalized treatment selections
- The central role of the propensity score in observational studies for causal effects
- The role of the propensity score in estimating dose-response functions
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning
- Optimal Dynamic Treatment Regimes
- A Robust Method for Estimating Optimal Treatment Regimes
- Multicategory individualized treatment regime using outcome weighted learning
- Entropy Learning for Dynamic Treatment Regimes
- Multivariate Density Estimation
- Adaptive contrast weighted learning for multi‐stage multi‐treatment decision‐making
- Propensity scores based methods for estimating average treatment effect and average treatment effect among treated: A comparative study
- New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
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