CoxAIPW: Doubly Robust Inference for Cox Marginal Structural Model with Informative Censoring

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. Reference: Luo & Xu (2022) <doi:10.48550/arXiv.2206.02296>; Rava (2021) <>.

Version: 0.0.2
Imports: survival, randomForestSRC, polspline, tidyr, ranger, pracma, gbm
Published: 2023-05-31
Author: Jiyu Luo [cre, aut], Dennis Rava [aut], Ronghui Xu [aut]
Maintainer: Jiyu Luo <charlesluo1002 at>
License: GPL-3
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: CoxAIPW results


Reference manual: CoxAIPW.pdf


Package source: CoxAIPW_0.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): CoxAIPW_0.0.2.tgz, r-oldrel (arm64): CoxAIPW_0.0.2.tgz, r-release (x86_64): CoxAIPW_0.0.2.tgz, r-oldrel (x86_64): CoxAIPW_0.0.2.tgz
Old sources: CoxAIPW archive


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