Type: Package
Package: CoxAIPW
Title: Doubly Robust Inference for Cox Marginal Structural Model with
        Informative Censoring
Version: 0.0.3
Authors@R: c(
    person("Jiyu", "Luo", email = "charlesluo1002@gmail.com", role = c("cre","aut")),
    person("Dennis", "Rava", email = "drava@ucsd.edu", role = "aut"),
	person("Ronghui", "Xu", email = "rxu@health.ucsd.edu", role = "aut"))
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Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation].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) <doi:10.48550/arXiv.2206.02296>; Rava (2021) <https://escholarship.org/uc/item/8h1846gs>.
License: GPL-3
URL: https://github.com/charlesluo1002/CoxAIPW
BugReports: https://github.com/charlesluo1002/CoxAIPW/issues
Imports: survival, randomForestSRC, polspline, tidyr, ranger, pracma,
        gbm
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2023-09-12 23:20:53 UTC; xinyi0401
Author: Jiyu Luo [cre, aut],
  Dennis Rava [aut],
  Ronghui Xu [aut]
Maintainer: Jiyu Luo <charlesluo1002@gmail.com>
Repository: CRAN
Date/Publication: 2023-09-20 07:30:06 UTC
Built: R 4.6.0; ; 2026-04-25 12:52:19 UTC; unix
