CRAN/E | SAMTx

SAMTx

Sensitivity Assessment to Unmeasured Confounding with Multiple Treatments

Installation

About

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 .

Key Metrics

Version 0.3.0
Published 2021-06-28 1085 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Jiayi Ji

Authors

Liangyuan Hu

aut

Jungang Zou

aut

Jiayi Ji

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

SAMTx archive

Imports

BART