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.
A sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate uncertainty about the sensitivity parameters and sampling variability. Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference. For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data <doi:10.48550/arXiv.2012.06093>.
| Version: | 0.3.0 |
| Imports: | BART |
| Published: | 2021-06-28 |
| DOI: | 10.32614/CRAN.package.SAMTx |
| Author: | Liangyuan Hu [aut], Jungang Zou [aut], Jiayi Ji [aut, cre] |
| Maintainer: | Jiayi Ji <Jiayi.Ji at mountsinai.org> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | SAMTx results |
| Reference manual: | SAMTx.pdf |
| Package source: | SAMTx_0.3.0.tar.gz |
| Windows binaries: | r-devel: SAMTx_0.3.0.zip, r-release: SAMTx_0.3.0.zip, r-oldrel: SAMTx_0.3.0.zip |
| macOS binaries: | r-release (arm64): SAMTx_0.3.0.tgz, r-oldrel (arm64): SAMTx_0.3.0.tgz, r-release (x86_64): SAMTx_0.3.0.tgz, r-oldrel (x86_64): SAMTx_0.3.0.tgz |
| Old sources: | SAMTx archive |
Please use the canonical form https://CRAN.R-project.org/package=SAMTx 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.