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SAMTx
View on CRAN: Click
here
Download and install SAMTx package within the R console
Install from CRAN:
install.packages("SAMTx")
Install from Github:
library("remotes")
install_github("cran/SAMTx")
Install by package version:
library("remotes")
install_version("SAMTx", "0.3.0")
Attach the package and use:
library("SAMTx")
Maintained by
Jiayi Ji
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-01-11
Latest Update: 2021-06-28
Description:
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 .
How to cite:
Jiayi Ji (2021). SAMTx: Sensitivity Assessment to Unmeasured Confounding with Multiple Treatments. R package version 0.3.0, https://cran.r-project.org/web/packages/SAMTx. Accessed 07 Nov. 2024.
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Complete documentation for SAMTx
Functions, R codes and Examples using
the SAMTx R package
Some associated functions: sensitivity_analysis .
Some associated R codes: sensitivity_analysis_algorithm.R . Full SAMTx package functions and examples
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