The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation].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>.
| Version: | 0.0.3 |
| Imports: | survival, randomForestSRC, polspline, tidyr, ranger, pracma, gbm |
| Published: | 2023-09-20 |
| DOI: | 10.32614/CRAN.package.CoxAIPW |
| Author: | Jiyu Luo [cre, aut], Dennis Rava [aut], Ronghui Xu [aut] |
| Maintainer: | Jiyu Luo <charlesluo1002 at gmail.com> |
| BugReports: | https://github.com/charlesluo1002/CoxAIPW/issues |
| License: | GPL-3 |
| URL: | https://github.com/charlesluo1002/CoxAIPW |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README NEWS |
| CRAN checks: | CoxAIPW results |
| Reference manual: | CoxAIPW.pdf |
| Package source: | CoxAIPW_0.0.3.tar.gz |
| Windows binaries: | r-devel: CoxAIPW_0.0.3.zip, r-release: CoxAIPW_0.0.3.zip, r-oldrel: CoxAIPW_0.0.3.zip |
| macOS binaries: | r-release (arm64): CoxAIPW_0.0.3.tgz, r-oldrel (arm64): CoxAIPW_0.0.3.tgz, r-release (x86_64): CoxAIPW_0.0.3.tgz, r-oldrel (x86_64): CoxAIPW_0.0.3.tgz |
| Old sources: | CoxAIPW archive |
Please use the canonical form https://CRAN.R-project.org/package=CoxAIPW to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.