Estimates the log hazard ratio associated with a binary exposure using a Cox PH model weighted by the propensity score. Propensity model is estimated using a simple logistic regression. Variance estimation takes into account the propensity score estimation step with the method proposed by Hajage et al. (2018) <doi:10.1002/bimj.201700330>. Both the average treatment effect on the overall (ATE) or the treated (ATT) population can be estimated. For the ATE estimation, both unstabilized and stabilized weights can be used.
| Version: | 0.1.3 |
| Depends: | R (≥ 3.3) |
| Imports: | survival |
| Suggests: | RISCA, boot |
| Published: | 2020-04-13 |
| Author: | David Hajage [aut, cre] |
| Maintainer: | David Hajage <david.hajage at aphp.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | hrIPW results |
| Reference manual: | hrIPW.pdf |
| Package source: | hrIPW_0.1.3.tar.gz |
| Windows binaries: | r-devel: hrIPW_0.1.3.zip, r-release: hrIPW_0.1.3.zip, r-oldrel: hrIPW_0.1.3.zip |
| macOS binaries: | r-release: hrIPW_0.1.3.tgz, r-oldrel: hrIPW_0.1.3.tgz |
| Old sources: | hrIPW archive |
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