ipwCoxCSV: Inverse Probability Weighted Cox Model with Corrected Sandwich Variance

An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.

Version:	1.0
Imports:	survival, stats
Published:	2019-10-09
DOI:	10.32614/CRAN.package.ipwCoxCSV
Author:	Di Shu, Rui Wang
Maintainer:	Di Shu <shudi1991 at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	ipwCoxCSV results

Documentation:

Reference manual:

ipwCoxCSV.pdf

Downloads:

Package source:	ipwCoxCSV_1.0.tar.gz
Windows binaries:	r-devel: ipwCoxCSV_1.0.zip, r-release: ipwCoxCSV_1.0.zip, r-oldrel: ipwCoxCSV_1.0.zip
macOS binaries:	r-release (arm64): ipwCoxCSV_1.0.tgz, r-oldrel (arm64): ipwCoxCSV_1.0.tgz, r-release (x86_64): ipwCoxCSV_1.0.tgz, r-oldrel (x86_64): ipwCoxCSV_1.0.tgz

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