DTRreg

swMATH17548CRANDTRregMaRDI QIDQ29406

DTR Estimation and Inference via G-Estimation, Dynamic WOLS, Q-Learning, and Dynamic Weighted Survival Modeling (DWSurv)

Michael Wallace

Last update: 19 November 2023

Copyright license: GNU General Public License, version 2.0

Software version identifier: 1.7, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 2.0

Source code repository: https://github.com/cran/DTRreg

Description

Dynamic treatment regime estimation and inference via G-estimation, dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference via bootstrap and recursive sandwich estimation. Estimation and inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). Extension to continuous treatment variables. Wallace et al. (2017) <doi:10.18637/jss.v080.i02>; Simoneau et al. (2020) <doi:10.1080/00949655.2020.1793341>.

Cites work

• Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg
• Optimal dynamic treatment regimes with survival endpoints: introducing DWSurv in the R package DTRreg

Described by source

• Optimal dynamic treatment regimes with survival endpoints: introducing DWSurv in the R package DTRreg

Related Items (4)

Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential multiple assignment randomized trials ⋮ Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes ⋮ Estimating Optimal Dynamic Treatment Regimes With Survival Outcomes ⋮ Doubly Robust Estimation of Optimal Dosing Strategies

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