CRAN/E | CoxAIPW

CoxAIPW

Doubly Robust Inference for Cox Marginal Structural Model with Informative Censoring

Installation

About

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Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation].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) doi:10.48550/arXiv.2206.02296; Rava (2021) .

github.com/charlesluo1002/CoxAIPW
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Key Metrics

Version 0.0.3
Published 2023-09-20 213 days ago
Needs compilation? no
License GPL-3
CRAN checks CoxAIPW results
Language en-US

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Maintainer

Maintainer

Jiyu Luo

charlesluo1002@gmail.com

Authors

Jiyu Luo

cre / aut

Dennis Rava

aut

Ronghui Xu

aut

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

CoxAIPW archive

Imports

survival
randomForestSRC
polspline
tidyr
ranger
pracma
gbm