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CoxAIPW
View on CRAN: Click
here
Download and install CoxAIPW package within the R console
Install from CRAN:
install.packages("CoxAIPW")
Install from Github:
library("remotes")
install_github("cran/CoxAIPW")
Install by package version:
library("remotes")
install_version("CoxAIPW", "0.0.3")
Attach the package and use:
library("CoxAIPW")
Maintained by
Jiyu Luo
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.CoxAIPW . https://github.com/charlesluo1002/CoxAIPW/issues . https://github.com/charlesluo1002/CoxAIPW . CoxAIPW results . CoxAIPW.pdf . CoxAIPW_0.0.3.tar.gz . CoxAIPW_0.0.3.zip . CoxAIPW_0.0.3.zip . CoxAIPW_0.0.3.zip . CoxAIPW_0.0.3.tgz . CoxAIPW_0.0.3.tgz . CoxAIPW_0.0.3.tgz . CoxAIPW_0.0.3.tgz . CoxAIPW_0.0.3.tgz . CoxAIPW_0.0.3.tgz . CoxAIPW archive . https://CRAN.R-project.org/package=CoxAIPW .
First Published: 2023-02-08
Latest Update: 2023-09-20
Description:
Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) ; Rava (2021) .
How to cite:
Jiyu Luo (2023). CoxAIPW: Doubly Robust Inference for Cox Marginal Structural Model with Informative Censoring. R package version 0.0.3, https://cran.r-project.org/web/packages/CoxAIPW. Accessed 03 Apr. 2025.
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