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CoxAIPW  

Doubly Robust Inference for Cox Marginal Structural Model with Informative Censoring
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]
All associated links for this package
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Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation].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 23 Dec. 2024.
Previous versions and publish date:
0.0.1 (2023-02-08 10:40), 0.0.2 (2023-05-31 10:00)
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Complete documentation for CoxAIPW
Functions, R codes and Examples using the CoxAIPW R package
Full CoxAIPW package functions and examples
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