riAFTBART: A Flexible Approach for Causal Inference with Multiple Treatments and Clustered Survival Outcomes

Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.

Version: 0.3.2
Imports: MASS, MCMCpack, msm, dbarts, magrittr, foreach, doParallel, dplyr, BART, stringr, tidyr, survival, cowplot, ggplot2, twang, nnet, RRF, randomForest
Published: 2022-05-16
Author: Liangyuan Hu [aut], Jiayi Ji [aut, cre]
Maintainer: Jiayi Ji <jj869 at sph.rutgers.edu>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: riAFTBART results


Reference manual: riAFTBART.pdf


Package source: riAFTBART_0.3.2.tar.gz
Windows binaries: r-devel: riAFTBART_0.3.2.zip, r-release: riAFTBART_0.3.2.zip, r-oldrel: riAFTBART_0.3.2.zip
macOS binaries: r-release (arm64): riAFTBART_0.2.0.tgz, r-oldrel (arm64): riAFTBART_0.3.2.tgz, r-release (x86_64): riAFTBART_0.3.2.tgz, r-oldrel (x86_64): riAFTBART_0.3.2.tgz
Old sources: riAFTBART archive


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